pierreqi/r_code_50k · Datasets at Hugging Face

fe2271c0adee5366ffeeb3b53ee4eb8f85512f3b

f9229ecfef3b8ce90dad05cfef395c442cc90e23

/script/question3.r

d672bdaaabf498bad88f1736c7d8ee94396a7f37

[]

no_license

lga37/mc2-trabalhofinal

ee452fa32499b294e0446207e25b5d29c84b4162

fc027d48082bc31bfdf0390d5ee97ddfaff8470e

refs/heads/main

2023-03-09T09:40:19.332620

2021-02-23T22:02:56

342,687,141

0

null

2021-02-26T20:01:15

2021-02-26T20:01:14

null

UTF-8

R

false

896

r

question3.r

library(tidyverse) rm(list = ls()) # SETANDO A HOME COMO WORK DIR setwd("~/Mestrado/TrabalhoMC2"); data <- read.table("data/data_t3-t4.txt", header = TRUE); configs <- unique(as.character(data$config)); instances <- unique(as.character(data$inst)); # qualidade dos dados ic <- matrix(nrow=length(instances), ncol=length(configs), dimnames=list(instances, configs)); hv <- matrix(nrow=length(instances), ncol=length(configs), dimnames=list(instances, configs)); gd <- matrix(nrow=length(instances), ncol=length(configs), dimnames=list(instances, configs)); for (config_ in configs) { for (instance_ in instances) { instance_ <- instances[which(instances==instance_)]; datarow <- subset(data, inst == instance_ & config == config_); ic[instance_, config_] <- mean(datarow$best); hv[instance_, config_] <- mean(datarow$hv); gd[instance_, config_] <- mean(datarow$gd); } }

2ba599784c4338f576966af78ffcd029da039978

c2c35d0c4e9fc33b9efae54ca98f76e9e216bbbd

/project_code/Download MICS from json.R

6213f4b3329dda99772139a8c073e56abdf1b6d8

[]

no_license

danjwalton/MPI

c4bcb83037dd3b176eb0c3a1a30ecf9a80288b61

930d759ad0f12ed5fead7e560b04feccd54733f8

refs/heads/master

2020-07-04T14:50:05.227444

2019-08-19T16:19:55

null

0

null

UTF-8

R

false

6,941

r

Download MICS from json.R

required.packages <- c("reshape2","ggplot2","data.table","jsonlite","RCurl","XML","xml2","RStata","stringr","foreign") lapply(required.packages, require, character.only=T) wd <- "G:/My Drive/Work/GitHub/MPI/" setwd(wd) basename.url=function(path){ path_sep=strsplit(path,split="/")[[1]] path_len=length(path_sep) return(path_sep[path_len]) } mics_dat <- fromJSON("project_data/mics.json",flatten=T) mics_dat <- subset(mics_dat,dataset.url!="") urls <- mics_dat$dataset.url uniquesavs=c() for(url in urls){ if(exists("ch")){rm(ch)} if(exists("hh")){rm(hh)} if(exists("hl")){rm(hl)} if(exists("wm")){rm(wm)} if(exists("mn")){rm(mn)} if(exists("bh")){rm(bh)} if(exists("ph")){rm(bh)} if(exists("who_z")){rm(who_z)} if(exists("fg")){rm(fg)} if(exists("tn")){rm(tn)} if(exists("fs")){rm(fs)} if(exists("uncaptured_list")){rm(uncaptured_list)} filename <- gsub("%20","_",basename.url(url)) uniquename <- substr(filename,1,nchar(filename)-4) message(paste(uniquename)," ... ",match(url,urls),"/",length(urls)) tmp <- tempfile() download.file(url,tmp,quiet=T) zip.contents <- unzip(tmp,exdir="large.data") if(!(exists("zip.contents"))){ next; } file.remove(tmp) if("zip" %in% str_sub(zip.contents,-3)){ message("multiple zips") zip.contents=unzip(zip.contents[which(str_sub(zip.contents,-3)=="zip")],exdir="large.data") }else{ zip.contents <- zip.contents[which(str_sub(zip.contents,-3)=="sav")] } all.sav <- zip.contents[which(grepl("(.*)sav",tolower(basename(zip.contents))))] # uniquesavs=unique(c(uniquesavs,all.sav)) ch.sav <- zip.contents[which(grepl("^ch(.*)sav|(.*)ch.sav",tolower(basename(zip.contents))))] ch.sav2 <- zip.contents[which(grepl("^under5(.*)sav|(.*)under5.sav",tolower(basename(zip.contents))))] ch.sav3 <- zip.contents[which(grepl("^underfive(.*)sav|(.*)underfive.sav",tolower(basename(zip.contents))))] ch.sav=c(ch.sav,ch.sav2,ch.sav3) hh.sav <- zip.contents[which(grepl("^hh(.*)sav|(.*)hh.sav",tolower(basename(zip.contents))))] hl.sav <- zip.contents[which(grepl("^hl(.*)sav|(.*)hl.sav",tolower(basename(zip.contents))))] wm.sav <- zip.contents[which(grepl("^wm(.*)sav|(.*)wm.sav",tolower(basename(zip.contents))))] wm.sav2 <- zip.contents[which(grepl("^woman(.*)sav|(.*)woman.sav",tolower(basename(zip.contents))))] wm.sav <- c(wm.sav,wm.sav2) mn.sav <- zip.contents[which(grepl("^mn(.*)sav|(.*)mn.sav",tolower(basename(zip.contents))))] mn.sav2 <- zip.contents[which(grepl("^man(.*)sav|(.*)man.sav",tolower(basename(zip.contents))))] mn.sav <- c(mn.sav,mn.sav2) bh.sav <- zip.contents[which(grepl("^bh(.*)sav|(.*)bh.sav",tolower(basename(zip.contents))))] ph.sav <- zip.contents[which(grepl("^ph(.*)sav|(.*)ph.sav",tolower(basename(zip.contents))))] who_z.sav <- zip.contents[which(grepl("^who_z(.*)sav|(.*)who_z.sav",tolower(basename(zip.contents))))] fg.sav <- zip.contents[which(grepl("^fg(.*)sav|(.*)fg.sav",tolower(basename(zip.contents))))] tn.sav <- zip.contents[which(grepl("^tn(.*)sav|(.*)tn.sav",tolower(basename(zip.contents))))] fs.sav <- zip.contents[which(grepl("^fs(.*)sav|(.*)fs.sav",tolower(basename(zip.contents))))] if(length(ch.sav)>0){ ch <- read.spss(ch.sav, use.value.labels = F) ch["FILTER_$"] <- NULL }else{ ch <- NULL } if(length(hh.sav)>0){ hh <- read.spss(hh.sav, use.value.labels = T) hh["FILTER_$"] <- NULL }else{ hh <- NULL } if(length(hl.sav)>0){ hl <- read.spss(hl.sav, use.value.labels = T) hl["FILTER_$"] <- NULL }else{ hl <- NULL } if(length(wm.sav)>0){ wm <- read.spss(wm.sav, use.value.labels = T) wm["FILTER_$"] <- NULL }else{ wm <- NULL } if(length(mn.sav)>0){ if(grepl("mnmn",tolower(mn.sav))){ mn.sav <- mn.sav[grepl("mnmn",tolower(mn.sav))] } mn <- read.spss(mn.sav, use.value.labels = T) mn["FILTER_$"] <- NULL }else{ mn <- NULL } if(length(bh.sav)>0){ if(grepl("bhbh",tolower(bh.sav))){ bh.sav <- bh.sav[grepl("bhbh",tolower(bh.sav))] } bh <- read.spss(bh.sav, use.value.labels = T) bh["FILTER_$"] <- NULL }else{ bh <- NULL } if(length(ph.sav)>0){ if(grepl("phph",tolower(ph.sav))){ ph.sav <- ph.sav[grepl("phph",tolower(ph.sav))] } ph <- read.spss(ph.sav, use.value.labels = T) ph["FILTER_$"] <- NULL }else{ ph <- NULL } if(length(who_z.sav)>0){ who_z <- read.spss(who_z.sav, use.value.labels = F) who_z["FILTER_$"] <- NULL }else{ who_z <- NULL } if(length(fg.sav)>0){ fg <- read.spss(fg.sav, use.value.labels = F) fg["FILTER_$"] <- NULL }else{ fg <- NULL } if(length(tn.sav)>0){ tn <- read.spss(tn.sav, use.value.labels = F) tn["FILTER_$"] <- NULL }else{ tn <- NULL } if(length(fs.sav)>0){ fs <- read.spss(fs.sav, use.value.labels = F) fs["FILTER_$"] <- NULL }else{ fs <- NULL } uncaptured=all.sav[which(!all.sav %in% c( ch.sav ,hh.sav ,hl.sav ,wm.sav ,mn.sav ,bh.sav ,ph.sav ,who_z.sav ,fg.sav ,tn.sav ,fs.sav ))] uncaptured_list=list() if(length(uncaptured)>0){ for(uncap in uncaptured){ data.tmp= read.spss(uncap, use.value.labels = T) #uncap.labs <- data.frame(var.name=names(data.tmp),var.lab=attributes(data.tmp)$variable.labels) data.tmp$filename <- uniquename uncap.list=list("data"=data.tmp)#,"labs"=uncap.labs) uncaptured_list[[basename(uncap)]]=uncap.list } } dtapath <- paste0("project_data/DHS MICS data files/",uniquename) dir.create(dtapath) tryCatch({ write.dta(as.data.frame(ch),paste0(dtapath,"/ch.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(hh),paste0(dtapath,"/hh.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(hl),paste0(dtapath,"/hl.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(wm),paste0(dtapath,"/wm.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(mn),paste0(dtapath,"/mn.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(bh),paste0(dtapath,"/bh.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(ph),paste0(dtapath,"/ph.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(who_z),paste0(dtapath,"/who_z.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(fg),paste0(dtapath,"/fg.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(tn),paste0(dtapath,"/tn.dta"),version=12) },error=function(e){return(NULL)}) tryCatch({ write.dta(as.data.frame(fs),paste0(dtapath,"/fs.dta"),version=12) },error=function(e){return(NULL)}) rm(zip.contents) }

1aaf1d6fdd400481319c6356820a9bf1b57ce9ba

ea492f927e78f9eef5e805bb1b884830c1a76f68

/mctd_nc

b5d3bf6afdc44aa709ea628bed527d1f2ffc03ca

[]

no_license

EOGrady21/netCDF

b1f2d7041fb3e556919953b9b54963ac812e00d3

742615c0b8422ca4c260065d7118fc216fe40c46

refs/heads/master

2022-04-27T16:23:19.705034

2019-04-17T16:46:40

null

0

null

UTF-8

R

false

25,253

mctd_nc

####mctd NC template#### # obj <- read.odf('C:/Users/ChisholmE/Documents/sample files/mctd/MCTD_HUD2015006_1897_11688_1800.ODF', header = 'list') # metadata <- ('C:/Users/ChisholmE/Documents/sample files/metadata/MCTD_SAMPLE_METADATA.csv') source('asP01.R') #' Moored CTD netCDF template #' #' @param obj an odf object from oce which contains mctd data #' @param metadata a csv file following the standard template which includes all #' necessary metadata #' @param filename the desired name for the netCDF file produced, if left NULL #' the default will conform to BIO naming conventions #' #' #' @return netCDF file with a maximum of 12 variables #' @export #' #' @examples #' file <- list.files('.', pattern = "MCTD*...*.ODF") #' obj <- read.odf(file) #' metadata <- 'MCTD_SAMPLE_METADATA.csv' #' mctd_nc(obj, metadata) #' mctd_nc <- function(obj, metadata, filename = NULL){ require(oce) require(ncdf4) v <- names(obj@data) var <- obj@metadata$dataNamesOriginal #remove SYTM from var list tr <- grep(v, pattern = 'time') v <- v[-tr] vt <- grep(var, pattern = 'SYTM') var <- var[-vt] #POPULATE VARIABLES WITH APPROPRIATE CODES for ( i in 1:length(var)){ var[[i]] <- as.P01(var[[i]]) } i <- 1 for ( vv in var ){ eval(parse(text = paste0("variable_", i, "<- '" , v[[i]], "'"))) eval(parse(text= paste0("var",i," <-'", vv$gf3,"'"))) eval(parse(text = paste0("units", i, " <-'", vv$units, "'"))) eval(parse(text = paste0('P01_VAR', i," <- paste0('SDN:P01::', vv$P01)" ))) eval(parse(text = paste0('P01_name_var', i," <-'" , vv$P01name , "'"))) eval(parse(text = paste0('P06_var', i, "<-'" , vv$P06 , "'"))) eval(parse(text = paste0('P06_name_var', i, "<- '" , vv$P06name , "'"))) eval(parse(text = paste0('var', i, 'max <-', -10000))) eval(parse(text = paste0('var', i, 'min <-' , 10000))) if(!is.null(vv$std)){ eval(parse(text = paste0("std_variable_", i, " <- '", vv$std, "'"))) }else{ eval(parse(text = paste0("std_variable_", i, " <- NULL"))) } #check if variable also has quality flag if (v[[i]] %in% names(obj[['flags']])) { eval(parse(text = paste0("var", i, "_QC <- '", vv$gf3, "_QC'"))) eval(parse(text = paste0("variable", i , "_QC <- 'quality flag for " , v[[i]], "'"))) } i <- i+1 } #CHECK LENGTH OF VARIABLES numvar <- length(var) #FILENAME if(missing(filename)){ filename <- paste("MCTD", obj[['cruiseNumber']], obj[['eventNumber']], obj[['eventQualifier']], obj[['samplingInterval']], sep = '_') } ncpath <- "./" ncfname <- paste(ncpath, filename, ".nc", sep = "") #DIMENSIONS timedim <- ncdim_def("time", "seconds since 1970-01-01T00:00:00Z", as.double(obj[['time']])) stationdim <- ncdim_def("station", "counts", as.numeric(obj[['station']])) londim <- ncdim_def("lon", "degrees_east" , as.double(obj[['longitude']])) latdim <- ncdim_def("lat", "degrees_north", as.double(obj[['latitude']])) dimnchar <- ncdim_def('nchar', '', 1:23, create_dimvar = FALSE) #FILLVALUE FillValue <- 1e35 #VARIABLES dlname <- 'lon' lon_def <- ncvar_def(longname= "longitude", units = 'degrees_east', dim = stationdim, name = dlname, prec = 'double') dlname <- 'lat' lat_def <- ncvar_def( longname = 'latitude', units = 'degrees_north', dim = stationdim, name = dlname, prec = 'double') dlname <- "time_02" t_def <- ncvar_def("ELTMEP01", "seconds since 1970-01-01T00:00:00Z", list( stationdim, timedim), FillValue, dlname, prec = "double") dlname <- "time_string" ts_def <- ncvar_def("DTUT8601", units = "",dim = list( dimnchar, timedim), missval = NULL, name = dlname, prec = "char") dlname <- variable_1 v1_def <- ncvar_def(var1, units1, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >1){ dlname <- variable_2 v2_def <- ncvar_def(var2, units2, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >2){ dlname <- variable_3 v3_def <- ncvar_def(var3, units3, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >3){ dlname <- variable_4 v4_def <- ncvar_def(var4, units4, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >4){ dlname <- variable_5 v5_def <- ncvar_def(var5, units5, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >5){ dlname <- variable_6 v6_def <- ncvar_def(var6, units6, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >6){ dlname <- variable_7 v7_def <- ncvar_def(var7, units7, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >7){ dlname <- variable_8 v8_def <- ncvar_def(var8, units8, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >8){ dlname <- variable_9 v9_def <- ncvar_def(var9, units9, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >9){ dlname <- variable_10 v10_def <- ncvar_def(var10, units10, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar > 10){ dlname <- variable_11 v11_def <- ncvar_def(var11, units11, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar > 11){ dlname <- variable_12 v12_def <- ncvar_def(var12, units12, list(timedim, stationdim), FillValue, dlname, prec = 'double') if (numvar >12){ warning ("Maximum of 12 variables exceeded, not all data has been exported!") } } } } } } } } } } } } #####write out definitions to new nc file#### defs <- grep(ls(), pattern = '_def', value = TRUE) dd <- NULL for ( i in 1:length(defs)){ eval(parse(text = paste0("dd[[i]] <- ", defs[[i]]))) } ncout <- nc_create( ncfname, dd , force_v4 = TRUE ) ####INSERT DATA#### ncvar_put(ncout, ts_def, obj[['time']]) ncvar_put(ncout, t_def, as.POSIXct(obj[['time']], tz = 'UTC', origin = '1970-01-01 00:00:00')) ncvar_put(ncout, lon_def, obj[['longitude']]) ncvar_put(ncout, lat_def, obj[['latitude']]) ncvar_put(ncout, v1_def, obj[[variable_1]]) if (numvar >1){ ncvar_put(ncout, v2_def, obj[[variable_2]]) if (numvar >2){ ncvar_put(ncout, v3_def, obj[[variable_3]]) if (numvar >3){ ncvar_put(ncout, v4_def, obj[[variable_4]]) if (numvar >4){ ncvar_put(ncout, v5_def, obj[[variable_5]]) if (numvar >5){ ncvar_put(ncout, v6_def, obj[[variable_6]]) if (numvar >6){ ncvar_put(ncout, v7_def, obj[[variable_7]]) if (numvar >7){ ncvar_put(ncout, v8_def, obj[[variable_8]]) if (numvar >8){ ncvar_put(ncout, v9_def, obj[[variable_9]]) if (numvar >9){ ncvar_put(ncout, v10_def, obj[[variable_10]]) if (numvar >10){ ncvar_put(ncout, v11_def, obj[[variable_11]]) if(numvar >11){ ncvar_put(ncout, v12_def, obj[[variable_12]]) } } } } } } } } } } } ####metadata#### ncatt_put(ncout, 'station', 'longitude', obj[['longitude']]) ncatt_put(ncout, 'station', 'latitiude', obj[['latitude']]) ncatt_put(ncout, 'station', 'standard_name', 'platform_name') ncatt_put(ncout, 'station', 'cf_role', 'timeseries_id') ncatt_put(ncout, 'time' , 'calendar', 'gregorian') ncatt_put(ncout, 'time_string', 'note', 'time values as ISO8601 string, YY-MM-DD hh:mm:ss') ncatt_put(ncout, 'time_string', 'time_zone', 'UTC') #FROM ODF ncatt_put(ncout, 0, 'inst_type', obj[['type']]) ncatt_put(ncout, 0, 'model', obj[['model']]) ncatt_put(ncout, 0, 'sampling_interval', obj[['samplingInterval']]) ncatt_put(ncout, 0, 'country_code', obj[['countryInstituteCode']]) ncatt_put(ncout, 0, 'cruise_number', obj[['cruiseNumber']]) ncatt_put(ncout, 0, "mooring_number", obj[['station']]) ncatt_put(ncout, 0, "time_coverage_duration", (tail(obj[['time']], n = 1) - obj[['time']][[1]])) ncatt_put(ncout, 0, "time_coverage_duration_units", "days") ncatt_put(ncout, 0, "cdm_data_type", "station") ncatt_put(ncout, 0, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, 0, "data_type", 'MCTD') ncatt_put(ncout, 0, "longitude", obj[['longitude']]) ncatt_put(ncout, 0, "latitude", obj[['latitude']]) ncatt_put(ncout, 0, "platform", obj[['cruise']]) ncatt_put(ncout, 0, "sounding", obj[['sounding']]) ncatt_put(ncout, 0, "chief_scientist", obj[['scientist']]) ncatt_put(ncout, 0, "water_depth", obj[['waterDepth']]) ncatt_put(ncout, 0, "cruise_name", obj[['cruise']]) ####variable ATTRIBUTES#### ncatt_put(ncout, var1, 'reference_scale', 'IPTS-68') ####variables#### #sensor type, sensor depth and serial number for each variable #generic nameS #STANDARD NAMES #data max and min #VALID MIN AND MAX #p01 and p06 names ncatt_put(ncout, var1, "sensor_type", obj[['model']]) ncatt_put(ncout, var1, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var1, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var1, "generic_name", variable_1) ncatt_put(ncout, var1, "sdn_parameter_urn", P01_VAR1) ncatt_put(ncout, var1, "sdn_parameter_name", P01_name_var1) ncatt_put(ncout, var1, "sdn_uom_urn", P06_var1) ncatt_put(ncout, var1, "sdn_uom_name", P06_name_var1) if (!is.null(std_variable_1)){ ncatt_put(ncout, var1, "standard_name", std_variable_1) } ncatt_put(ncout, var1, "data_max", max(obj[[variable_1]], na.rm = TRUE)) ncatt_put(ncout, var1, "data_min", min(obj[[variable_1]], na.rm = TRUE)) ncatt_put(ncout, var1, "valid_max", var1max) ncatt_put(ncout, var1, "valid_min", var1min) if (numvar > 1){ ncatt_put(ncout, var2, "sensor_type", obj[['model']]) ncatt_put(ncout, var2, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var2, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var2, "generic_name", variable_2) ncatt_put(ncout, var2, "sdn_parameter_urn", P01_VAR2) ncatt_put(ncout, var2, "sdn_parameter_name", P01_name_var2) ncatt_put(ncout, var2, "sdn_uom_urn", P06_var2) ncatt_put(ncout, var2, "sdn_uom_name", P06_name_var2) if (!is.null(std_variable_2)){ ncatt_put(ncout, var2, "standard_name", std_variable_2) } ncatt_put(ncout, var2, "data_max", max(obj[[variable_2]], na.rm = TRUE)) ncatt_put(ncout, var2, "data_min", min(obj[[variable_2]], na.rm = TRUE)) ncatt_put(ncout, var2, "valid_max", var2max) ncatt_put(ncout, var2, "valid_min", var2min) if (numvar >2){ ncatt_put(ncout, var3, "sensor_type", obj[['model']]) ncatt_put(ncout, var3, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var3, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var3, "generic_name", variable_3) ncatt_put(ncout, var3, "sdn_parameter_urn", P01_VAR3) ncatt_put(ncout, var3, "sdn_parameter_name", P01_name_var3) ncatt_put(ncout, var3, "sdn_uom_urn", P06_var3) ncatt_put(ncout, var3, "sdn_uom_name", P06_name_var3) if (!is.null(std_variable_3)){ ncatt_put(ncout, var3, "standard_name", std_variable_3) } ncatt_put(ncout, var3, "data_max", max(obj[[variable_3]], na.rm = TRUE)) ncatt_put(ncout, var3, "data_min", min(obj[[variable_3]], na.rm = TRUE)) ncatt_put(ncout, var3, "valid_max", var3max) ncatt_put(ncout, var3, "valid_min", var3min) if (numvar >3){ ncatt_put(ncout, var4, "sensor_type", obj[['model']]) ncatt_put(ncout, var4, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var4, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var4, "generic_name", variable_4) ncatt_put(ncout, var4, "sdn_parameter_urn", P01_VAR4) ncatt_put(ncout, var4, "sdn_parameter_name", P01_name_var4) ncatt_put(ncout, var4, "sdn_uom_urn", P06_var4) ncatt_put(ncout, var4, "sdn_uom_name", P06_name_var4) if (!is.null(std_variable_4)){ ncatt_put(ncout, var4, "standard_name", std_variable_4) } ncatt_put(ncout, var4, "data_max", max(obj[[variable_4]], na.rm = TRUE)) ncatt_put(ncout, var4, "data_min", min(obj[[variable_4]], na.rm = TRUE)) ncatt_put(ncout, var4, "valid_max", var4max) ncatt_put(ncout, var4, "valid_min", var4min) if (numvar >4){ ncatt_put(ncout, var5, "sensor_type", obj[['model']]) ncatt_put(ncout, var5, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var5, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var5, "generic_name", variable_5) ncatt_put(ncout, var5, "sdn_parameter_urn", P01_VAR5) ncatt_put(ncout, var5, "sdn_parameter_name", P01_name_var5) ncatt_put(ncout, var5, "sdn_uom_urn", P06_var5) ncatt_put(ncout, var5, "sdn_uom_name", P06_name_var5) if (!is.null(std_variable_5)){ ncatt_put(ncout, var5, "standard_name", std_variable_5) } ncatt_put(ncout, var5, "data_max", max(obj[[variable_5]], na.rm = TRUE)) ncatt_put(ncout, var5, "data_min", min(obj[[variable_5]], na.rm = TRUE)) ncatt_put(ncout, var5, "valid_max", var5max) ncatt_put(ncout, var5, "valid_min", var5min) if (numvar >5){ ncatt_put(ncout, var6, "sensor_type", obj[['model']]) ncatt_put(ncout, var6, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var6, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var6, "generic_name", variable_6) ncatt_put(ncout, var6, "sdn_parameter_urn", P01_VAR6) ncatt_put(ncout, var6, "sdn_parameter_name", P01_name_var6) ncatt_put(ncout, var6, "sdn_uom_urn", P06_var6) ncatt_put(ncout, var6, "sdn_uom_name", P06_name_var6) if (!is.null(std_variable_6)){ ncatt_put(ncout, var6, "standard_name", std_variable_6) } ncatt_put(ncout, var6, "data_max", max(obj[[variable_6]], na.rm = TRUE)) ncatt_put(ncout, var6, "data_min", min(obj[[variable_6]], na.rm = TRUE)) ncatt_put(ncout, var6, "valid_max", var6max) ncatt_put(ncout, var6, "valid_min", var6min) if (numvar > 6){ ncatt_put(ncout, var7, "sensor_type", obj[['model']]) ncatt_put(ncout, var7, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var7, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var7, "generic_name", variable_7) ncatt_put(ncout, var7, "sdn_parameter_urn", P01_VAR7) ncatt_put(ncout, var7, "sdn_parameter_name", P01_name_var7) ncatt_put(ncout, var7, "sdn_uom_urn", P06_var7) ncatt_put(ncout, var7, "sdn_uom_name", P06_name_var7) if (!is.null(std_variable_7)){ ncatt_put(ncout, var7, "standard_name", std_variable_7) } ncatt_put(ncout, var7, "data_max", max(obj[[variable_7]], na.rm = TRUE)) ncatt_put(ncout, var7, "data_min", min(obj[[variable_7]], na.rm = TRUE)) ncatt_put(ncout, var7, "valid_max", var7max) ncatt_put(ncout, var7, "valid_min", var7min) if (numvar > 7){ ncatt_put(ncout, var8, "sensor_type", obj[['model']]) ncatt_put(ncout, var8, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var8, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var8, "generic_name", variable_8) ncatt_put(ncout, var8, "sdn_parameter_urn", P01_VAR8) ncatt_put(ncout, var8, "sdn_parameter_name", P01_name_var8) ncatt_put(ncout, var8, "sdn_uom_urn", P06_var8) ncatt_put(ncout, var8, "sdn_uom_name", P06_name_var8) if (!is.null(std_variable_8)){ ncatt_put(ncout, var8, "standard_name", std_variable_8) } ncatt_put(ncout, var8, "data_max", max(obj[[variable_8]], na.rm = TRUE)) ncatt_put(ncout, var8, "data_min", min(obj[[variable_8]], na.rm = TRUE)) ncatt_put(ncout, var8, "valid_max", var8max) ncatt_put(ncout, var8, "valid_min", var8min) if (numvar > 8){ ncatt_put(ncout, var9, "sensor_type", obj[['model']]) ncatt_put(ncout, var9, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var9, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var9, "generic_name", variable_9) ncatt_put(ncout, var9, "sdn_parameter_urn", P01_VAR9) ncatt_put(ncout, var9, "sdn_parameter_name", P01_name_var9) ncatt_put(ncout, var9 , "sdn_uom_urn", P06_var9) ncatt_put(ncout, var9, "sdn_uom_name", P06_name_var9) if (!is.null(std_variable_9)){ ncatt_put(ncout, var9, "standard_name", std_variable_9) } ncatt_put(ncout, var9, "data_max", max(obj[[variable_9]], na.rm = TRUE)) ncatt_put(ncout, var9, "data_min", min(obj[[variable_9]], na.rm = TRUE)) ncatt_put(ncout, var9, "valid_max", var9max) ncatt_put(ncout, var9, "valid_min", var9min) if (numvar >9){ ncatt_put(ncout, var10, "sensor_type", obj[['model']]) ncatt_put(ncout, var10, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var10, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var10, "generic_name", variable_10) ncatt_put(ncout, var10, "sdn_parameter_urn", P01_VAR10) ncatt_put(ncout, var10, "sdn_parameter_name", P01_name_var10) ncatt_put(ncout, var10, "sdn_uom_urn", P06_var10) ncatt_put(ncout, var10, "sdn_uom_name", P06_name_var10) if (!is.null(std_variable_10)){ ncatt_put(ncout, var10, "standard_name", std_variable_10) } ncatt_put(ncout, var10, "data_max", max(obj[[variable_10]], na.rm = TRUE)) ncatt_put(ncout, var10, "data_min", min(obj[[variable_10]], na.rm = TRUE)) ncatt_put(ncout, var10, "valid_max", var10max) ncatt_put(ncout, var10, "valid_min", var10min) if (numvar >10){ ncatt_put(ncout, var11, "sensor_type", obj[['model']]) ncatt_put(ncout, var11, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var11, "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var11, "generic_name", variable_11) ncatt_put(ncout, var11, "sdn_parameter_urn", P01_VAR11) ncatt_put(ncout, var11, "sdn_parameter_name", P01_name_var11) ncatt_put(ncout, var11, "sdn_uom_urn", P06_var11) ncatt_put(ncout, var11, "sdn_uom_name", P06_name_var11) if (!is.null(std_variable_11)){ ncatt_put(ncout, var11, "standard_name", std_variable_11) } ncatt_put(ncout, var11, "data_max", max(obj[[variable_11]], na.rm = TRUE)) ncatt_put(ncout, var11, "data_min", min(obj[[variable_11]], na.rm = TRUE)) ncatt_put(ncout, var11, "valid_max", var11max) ncatt_put(ncout, var11, "valid_min", var11min) if (numvar >11){ ncatt_put(ncout, var12, "sensor_type", obj[['model']]) ncatt_put(ncout, var12, "sensor_depth", obj[['depthMin']]) ncatt_put(ncout, var12 , "serial_number", obj[['serialNumber']]) ncatt_put(ncout, var12, "generic_name", variable_12) ncatt_put(ncout, var12, "sdn_parameter_urn", P01_VAR12) ncatt_put(ncout, var12, "sdn_parameter_name", P01_name_var12) ncatt_put(ncout, var12, "sdn_uom_urn", P06_var12) ncatt_put(ncout, var12, "sdn_uom_name", P06_name_var12) if (!is.null(std_variable_12)){ ncatt_put(ncout, var12, "standard_name", std_variable_12) } ncatt_put(ncout, var12, "data_max", max(obj[[variable_12]], na.rm = TRUE)) ncatt_put(ncout, var12, "data_min", min(obj[[variable_12]], na.rm = TRUE)) ncatt_put(ncout, var12, "valid_max", var12max) ncatt_put(ncout, var12, "valid_min", var12min) } } } } } } } } } } } ####CF conventions & BODC standards#### ncatt_put(ncout, 0, 'Conventions', 'CF-1.7') ncatt_put(ncout, 0, "creator_type", "person") ncatt_put(ncout, 0, "time_coverage_start", as.character(as.POSIXct(obj[['time']][1]))) ncatt_put(ncout, 0, "time_coverage_end", as.character(as.POSIXct(tail(obj[['time']], n= 1)))) ncatt_put(ncout, 0, "geospatial_lat_min", obj[['latitude']]) ncatt_put(ncout, 0, "geospatial_lat_max", obj[['latitude']]) ncatt_put(ncout, 0, "geospatial_lat_units", "degrees_north") ncatt_put(ncout, 0, "geospatial_lon_min", obj[['longitude']]) ncatt_put(ncout, 0, "geospatial_lon_max", obj[['longitude']]) ncatt_put(ncout, 0, "geospatial_lon_units", "degrees_east") ncatt_put(ncout, 0, "geospatial_vertical_max", obj[['depthMax']]) ncatt_put(ncout, 0, "geospatial_vertical_min", obj[['depthMin']]) ncatt_put(ncout, 0, "geospatial_vertical_units", "metres") ncatt_put(ncout, 0, "geospatial_vertical_positive", 'down') ncatt_put(ncout,0, "_FillValue", "1e35") ncatt_put(ncout, 0, "date_modified", date()) ncatt_put(ncout, 0, "institution", obj[['institute']]) ####BODC P01 names#### ncatt_put(ncout, "ELTMEP01", "sdn_parameter_urn", "SDN:P01::ELTMEP01") ncatt_put(ncout, "lon", "sdn_parameter_urn", "SDN:P01::ALONZZ01") ncatt_put(ncout, "lat", "sdn_parameter_urn", "SDN:P01::ALATZZ01") ncatt_put(ncout, "time_string", "sdn_parameter_urn", "SDN:P01::DTUT8601") ncatt_put(ncout, "lon", "sdn_parameter_name", "Longitude east") ncatt_put(ncout, "lat", "sdn_parameter_name", "Latitude north") ncatt_put(ncout, 'ELTMEP01', "sdn_parameter_name", "Elapsed time (since 1970-01-01T00:00:00Z)") ncatt_put(ncout, 'time_string', "sdn_parameter_name", "String corresponding to format 'YYYY-MM-DDThh:mm:ss.sssZ' or other valid ISO8601 string") ncatt_put(ncout, "lon", "sdn_uom_urn", "SDN:P06::DEGE") ncatt_put(ncout, "lat", "sdn_uom_urn", "SDN:P06:DEGN") ncatt_put(ncout, "ELTMEP01", "sdn_uom_urn", "SDN:P06::UTBB") ncatt_put(ncout, "time_string", "sdn_uom_urn", "SDN:P06::TISO") ncatt_put(ncout, "lon", "sdn_uom_name", "Degrees east") ncatt_put(ncout, "lat", "sdn_uom_name", "Degrees north") ncatt_put(ncout, "ELTMEP01", "sdn_uom_name", "Seconds") ncatt_put(ncout, "time_string", "sdn_uom_name", "ISO8601") #####CF standard names#### ncatt_put(ncout, "ELTMEP01", "standard_name", "time") ncatt_put(ncout, "lat", "standard_name", "latitude") ncatt_put(ncout, "lon", "standard_name", "longitude") ####data max and min#### #metadata from spreadsheet if (!missing(metadata)) { metad <- read.csv(metadata, header = TRUE) mn <- as.character(metad[,1]) mv <- as.character(metad[,2]) md <- as.list(mv) names(md) <- mn for (m in seq_along(md)) { ncatt_put(ncout, 0, names(md)[m], md[[m]]) } } ####preserve ODF history header#### if (!is.null(obj@metadata$header)){ if (length(obj@metadata$header) != 0){ head <- obj@metadata$header hi <- list(grep(names(head), pattern = "HISTORY")) hist <- NULL for ( i in 1:length(hi[[1]])){ hist[[i]] <- unlist(head[[hi[[1]][i]]]) } histo <- unlist(hist) histor <- NULL for (i in 1:length(histo)){ histor[[i]] <- paste(names(histo)[[i]],":", histo[[i]]) } history <- unlist(histor) for (i in 1:length(history)){ ncatt_put(ncout, 0, paste0("ODF_HISTORY_", i), history[[i]]) } #PRESERVE EVENT_COMMENTS ec <- list(grep(names(head$EVENT_HEADER), pattern = 'EVENT_COMMENTS')) if (length(ec[[1]] != 0)){ evc <- NULL for( i in 1:length(ec[[1]])){ evc[[i]] <- unlist(head$EVENT_HEADER[[ec[[1]][i]]]) } evec <- unlist(evc) evenc <- NULL for (i in 1:length(evec)){ evenc[[i]] <- paste(names(evec)[[i]], ":", evec[[i]]) } eventc <- unlist(evenc) for( i in 1:length(eventc)){ ncatt_put(ncout, 0, paste0("EVENT_COMMENTS_", i), eventc[[i]]) } } } } ####nc close#### nc_close(ncout) }

7b3d1e00ba465c321a71527ba13e1b7be18760ec

ba14c315f4ed435384c5b48185a5707dcf1ce093

/SidebarUi.R

c9e6ff771681fcdac0d99eb6326499c2c8c2a547

[]

no_license

antgers/Project_AquaMiner_Periodic

0e318e381f1e244ba6858407f22d8900a78d7f6f

7e81781d607e83833e1bd2fd60f93bd5995b8497

refs/heads/master

2021-01-17T19:20:10.620858

2016-10-23T20:57:37

71,663,263

0

null

UTF-8

R

false

3,615

r

SidebarUi.R

sidebarUni <- sidebarPanel( #fixed responsive img #added class img img(src="menfishing21.png", class = "img-responsive", align = 'middle'), hr(), bsCollapse(id = "collapseSidebar" , open = "Upload Data", multiple = FALSE, bsCollapsePanel("Upload Data", style = "primary", radioButtons(inputId = 'ext', label = 'File extention', choices = c('xlsx', 'xls', 'csv'), selected = 'xlsx', inline = TRUE), hr(), checkboxInput(inputId = 'header', label = 'First line is a header', value = TRUE), hr(), radioButtons(inputId = 'th.sep', label = 'Thousand Separator', choices = c(Comma=',', Dot='.'), selected = ',', inline = TRUE), tags$hr(), fileInput('file', 'Choose Excel File...', accept = c('.xls', '.xlsx', '.csv')), tags$hr(), h5("Press to Upload Dataset..."), actionButton("action", label = "Action") ), # end bsCollapsePanel Upload Data bsCollapsePanel("Dimensions", style = "primary", fluidRow(column(6, uiOutput("dimSpecies"), uiOutput("dimUnit"), uiOutput("dimHatchery"), uiOutput("dimOriginMonth"), uiOutput("dimOriginYear"), uiOutput("dimActualFeed"), uiOutput("dimStartAvWtCat"), uiOutput("dimEndAvWtCat") ), column(6, uiOutput("dimRegion"), uiOutput("dimSite"), uiOutput("dimBatch"), uiOutput("dimSamplMonth"), uiOutput("dimSamplYear"), uiOutput("dimSupplier"), uiOutput("dimFeedCategory"), uiOutput("dimFeedingPolicy") ) # end column ), # end fluidRow fluidRow( uiOutput("dateRangeFrom"), uiOutput("dateRangeTo") ) # end fluidRow ), # end bsCollapsePanel Dimensions bsCollapsePanel('Measures', style = "primary" , fluidRow( uiOutput("rangeStAvWeight"), uiOutput("rangeEndAvWeight"), uiOutput("rangeBiolPeriodFCR"), uiOutput("rangeEconPeriodFCR") ), fluidRow(column(6, uiOutput("rangePeriodSGR"), uiOutput("rangePeriodTGC"), uiOutput("rangeAvWtDeviation"), uiOutput("rangeAvgTemp"), uiOutput("rangeFeedDeviation"), uiOutput("rangeLTDEconFCR") ), column(6, uiOutput("rangePeriodSFR"), uiOutput("rangeGrowthPerDay"), uiOutput("rangePeriodMortalityPerc"), uiOutput("rangeDiffDays"), uiOutput("rangePeriodDayDegrees"), uiOutput("rangeLTDMortalityPerc") ) # end column ) # end fluid row ) # end of colapsePanel Measures ), # end bsCollapse hr(), actionButton(inputId = 'Go.Button', label = 'Go...') ) # end sidebarUni function

9fe0d902dff5c71a1edad97eb4f483713b339270

2cb802c7e9bb18670769604cb289b03192661d5a

/COPS code/6b create pregnancy level file.R

1b66a315e5c145b3ecf6dfafb256ea60d15990d9

[]

no_license

Public-Health-Scotland/COPS-public

a18d36d8a69479e34c1ddd31f23a15b5b7a6eba6

b4c4df18020712fbae08a979226d0a382d6aeda9

refs/heads/main

2023-07-29T17:41:26.677028

2023-07-11T12:40:32

362,821,738

0

2

null

2021-12-07T12:55:46

2021-04-29T13:11:02

R

UTF-8

R

false

8,080

r

6b create pregnancy level file.R

fetuslevel <- read_rds(paste0(folder_temp_data, "script6_baby_level_record_infection.rds")) #quick fixes to names to let the cohort run with extra data. #needs changes in 6aa to retain names without "_value_", or a decision to change names below in the long run fetuslevel <-fetuslevel %>% rename(tests_mother_has_had_pcr_test_at_any_point = tests_mother_has_pos_test_at_any_point) %>% rename(tests_mother_positive_test_during_pregnancy_1 =tests_mother_value_positive_test_during_pregnancy_1, tests_mother_positive_test_during_pregnancy_2 = tests_mother_value_positive_test_during_pregnancy_2 ) pregnancies <- fetuslevel %>% rowwise() %>% mutate(x_pregnancy_end_date = replace_na(x_pregnancy_end_date, as.Date("1970-01-01"))) %>% # summarise() won't alter NA date values, so use 1970-01-01 as a stand-in and change it back later ungroup() %>% arrange(pregnancy_id) %>% group_by(pregnancy_id) %>% mutate(overall_outcome = case_when("Live birth" %in% outcome ~ "Live birth", "Termination" %in% outcome ~ "Termination", "Stillbirth" %in% outcome ~ "Stillbirth", "Ectopic pregnancy" %in% outcome ~ "Ectopic pregnancy", "Molar pregnancy" %in% outcome ~ "Molar pregnancy", "Miscarriage" %in% outcome ~ "Miscarriage", "Ongoing" %in% outcome ~ "Ongoing", "Unknown" %in% outcome ~ "Unknown")) %>% summarise(mother_upi = first_(mother_upi), gestation_at_outcome = max_(x_gestation_at_outcome), pregnancy_end_date = as.Date(max_(x_pregnancy_end_date)), est_conception_date = as.Date(min_(x_est_conception_date)), overall_outcome = first_(overall_outcome), first_wave = max_(x_first_wave), full_cohort = max_(x_full_cohort), mother_dob = first_(x_mother_dob), mother_age_at_conception = first_(x_mother_age_at_conception), mother_age_at_outcome = first_(x_mother_age_at_outcome), hbres = first_(x_hbres), postcode = first(x_postcode), simd = first_(x_simd), bmi = first_(x_bmi), booking_smoking_status = first_(x_booking_smoking_status), gp_smoking_status = first_(x_gp_smoking_status), overall_smoking_status = first_(x_overall_smoking_status), ethnicity_code = first_(x_ethnicity_code), ethnicity_description = first_(x_ethnicity_desc), urban_rural_description = first_(x_urban_rural_8_description), births_this_pregnancy = max_(x_births_this_pregnancy), diabetes = max_(x_diabetes), shielding = max_(shielding_shield), shielding_group1 = max_(shielding_group1), shielding_group2 = max_(shielding_group2), shielding_group3 = max_(shielding_group3), shielding_group4 = max_(shielding_group4), shielding_group5 = max_(shielding_group5), shielding_group6 = max_(shielding_group6), shielding_group7 = max_(shielding_group7), shielding_group_any = max_(shielding_group_any), q_covid = max_(q_covid), q_bmi = first_(q_bmi), q_bmi_40_plus = max_(q_bmi_40_plus), q_diabetes_type = max_(q_diabetes_type), q_diag_af = max_(q_diag_af), q_diag_asthma = max_(q_diag_asthma), q_diag_blood_cancer = max_(q_diag_blood_cancer), q_diag_ccf = max_(q_diag_ccf), q_diag_cerebralpalsy = max_(q_diag_cerebralpalsy), q_diag_chd = max_(q_diag_chd), q_diag_cirrhosis = max_(q_diag_cirrhosis), q_diag_ckd3 = max_(q_diag_ckd3), q_diag_ckd4 = max_(q_diag_ckd4), q_diag_ckd5 = max_(q_diag_ckd5), q_diag_congen_hd = max_(q_diag_congen_hd), q_diag_copd = max_(q_diag_copd), q_diag_dementia = max_(q_diag_dementia), q_diag_diabetes_1 = max_(q_diag_diabetes_1), q_diag_diabetes_2 = max_(q_diag_diabetes_2), q_diag_epilepsy = max_(q_diag_epilepsy), q_diag_fracture = max_(q_diag_fracture), q_diag_neuro = max_(q_diag_neuro), q_diag_parkinsons = max_(q_diag_parkinsons), q_diag_pulm_hyper = max(q_diag_pulm_hyper), q_diag_pulm_rare = max_(q_diag_pulm_rare), q_diag_pvd = max_(q_diag_pvd), q_diag_ra_sle = max_(q_diag_ra_sle), q_diag_resp_cancer = max_(q_diag_resp_cancer), q_diag_sev_ment_ill = max_(q_diag_sev_ment_ill), q_diag_sickle_cell = max_(q_diag_sickle_cell), q_diag_stroke = max_(q_diag_stroke), q_diag_vte = max_(q_diag_vte), q_diag_renal_failure = max_(q_diag_renal_failure), q_ethnicity = first_(q_ethnicity), q_ethnicity_mapped9 = first_(q_ethnicity_mapped9), q_home_cat = first_(q_home_cat), # Should we use first_() or max_() here? q_learn_cat = first_(q_learn_cat),# Should we use first_() or max_() here? q_preexisting_diabetes = max_(q_preexisting_diabetes), cv_clinical_vulnerability_category = first_(cv_clinical_vulnerability_category), dose_1_vacc_occurence_date = first_(dose_1_vacc_occurence_date), dose_1_vacc_product_name = first_(dose_1_vacc_product_name), dose_1_vacc_location_health_board_name = first_(dose_1_vacc_location_health_board_name), dose_2_vacc_occurence_date = first_(dose_2_vacc_occurence_date), dose_2_vacc_product_name = first_(dose_2_vacc_product_name), dose_2_vacc_location_health_board_name = first_(dose_2_vacc_location_health_board_name), dose_3_vacc_occurence_date = first_(dose_3_vacc_occurence_date), dose_3_vacc_product_name = first_(dose_3_vacc_product_name), dose_3_vacc_location_health_board_name = first_(dose_3_vacc_location_health_board_name), dose_4_vacc_occurence_date = first_(dose_4_vacc_occurence_date), dose_4_vacc_product_name = first_(dose_4_vacc_product_name), dose_4_vacc_location_health_board_name = first_(dose_4_vacc_location_health_board_name), mother_has_had_pcr_test_at_any_point = max_(tests_mother_has_had_pcr_test_at_any_point), mother_earliest_positive_test = first_(tests_mother_earliest_positive_test), #mother_earliest_negative_test = first_(tests_mother_earliest_negative_test), mother_tested_positive_during_pregnancy = max_(tests_mother_positive_test_during_pregnancy), #mother_tested_negative_during_pregnancy = max_(tests_mother_negative_test_during_pregnancy), mother_earliest_positive_test_during_pregnancy = first_(tests_mother_earliest_positive_test_during_pregnancy), #mother_earliest_negative_test_during_pregnancy = first_(tests_mother_earliest_negative_test_during_pregnancy), mother_positive_test_during_pregnancy_1 = first_(tests_mother_positive_test_during_pregnancy_1), mother_positive_test_during_pregnancy_2 = first_(tests_mother_positive_test_during_pregnancy_2), # Theoretically we could have up to three positive tests during a pregnancy. Revise this code to accept an arbitrary number of positive tests. mother_total_positive_during_pregnancy = first_(tests_mother_total_positives_during_this_pregnancy), mother_eave_linkno = first_(mother_eave_linkno), gp_data_status = "GP data included", chi_validity = first_(chi_validity)) %>% mutate(pregnancy_end_date = if_else(pregnancy_end_date == as.Date("1970-01-01"), NA_Date_ , pregnancy_end_date)) #### Write pregnancy-level file #### pregnancies %>% write_rds(paste0(folder_temp_data, "script6b_pregnancy_level_record.rds"), compress = "gz")

f556a9157829fa6cfda53a6615c5069ad3a1c038

b6abcd32866919c5330394ec171a09d6a9085930

/R/topicModel.terms.R

ba8535e1e6ebeb1f176f77d5ad895683bf352f2d

[]

no_license

aidanoneill/textmining

b1c35e3cff9de1de392b90ba99c99ebde242464b

1699dddb76d305c33799af5bd3f976031db2438a

refs/heads/master

2016-09-10T19:40:03.450673

2015-03-19T19:38:19

32,542,012

0

null

UTF-8

R

false

276

r

topicModel.terms.R

# Models which terms belong to which topic, based on a passed number of topics topicModel.terms <- function(data, k){ require("topicmodels") lda = LDA(x = data, k = k, method = "VEM") return(terms(lda, 10)) # which documents belong to which topic }

eb5f791115caabce15649bc5b3dd49767fd739a7

4fea06a47c87fec7905b553c7f2184664abec48f

/R/create-events.R

dbc4e0ae48bb2bbde429311df368c41a83afee42

[ "CC-BY-4.0", "MIT" ]

permissive

au-cru/site

982792332b309e72cb8417415f2d1b9422fe78ae

c92349af621d62efeb58d4b11e81c85c8328ece8

refs/heads/master

2021-07-24T05:52:37.834147

2021-07-07T15:17:31

207,162,840

0

3

NOASSERTION

2020-02-28T09:31:43

2019-09-08T19:30:07

CSS

UTF-8

R

false

3,632

r

create-events.R

library(tidyverse) # Also use glue and datapasta. # Code-along -------------------------------------------------------------- # Paste from Google Sheets into Excel/Calc using datapaste package. events <- tibble::tribble( ~Date, ~Topic, ~Level, "1 November 2019", "Data visualization with ggplot2, part 1", "Beginner", "15 November 2019", "Data wrangling with dplyr, part 1", "Beginner", "29 November 2019", "Creating functions", "Beginner-Intermediate", "13 December 2019", "Reproducible reports with R Markdown", "Beginner", "17 January 2020", "Reproducibility and project management", "Beginner", "31 January 2020", "Data wrangling with data.table", "Intermediate", "14 February 2020", "Version control with Git", "Beginner", "28 February 2020", "Efficient coding and best practices", "Beginner-Intermediate", "13 March 2020", "Data visualization with ggplot2, part 2", "Beginner-Intermediate", "27 March 2020", "Learning how to learn/finding help", "Beginner", "10 April 2020", "First steps in creating R packages", "Intermediate", "24 April 2020", "Create websites with R (blogdown)", "Intermediate", "8 May 2020", "Creating interactive apps with Shiny", "Intermediate", "22 May 2020", "Data wrangling with dplyr and tidyr, part 2", "Beginner-Intermediate" ) # Do some data wrangling of the pasted in schedule. events_prep <- events %>% rename(name = Topic, level = Level) %>% mutate( date_ymd = lubridate::dmy(Date), start_date = str_c(date_ymd, "T13:00:00+01:00"), end_date = str_c(date_ymd, "T14:15:00+01:00"), type = "code-along", location = "AU Library, Main Floor, Nobelparken, Universitetsparken 461, 8000 Aarhus", software = "R" ) # Template for the events. events_template <- ' type: "{type}" name: "{name}" description: > FILL IN location: "{location}" start_date: {start_date} end_date: {end_date} level: "{level}" software: ["{software}"] ' # All new file names to be created. event_files <- here::here("data", "events", str_c(events_prep$date_ymd, "-code-along.yaml")) # Fill in the template with the contents of the events. event_file_contents <- events_prep %>% glue::glue_data(events_template) # Create the event files. walk2(event_file_contents, event_files, write_lines) # Hacky hours ------------------------------------------------------------- hacky_dates <- events_prep %>% filter(as.numeric(str_extract(Date, "^\\d+")) > 20) hacky_hours_template <- ' type: "coworking" name: "Hacky hour hang out" description: > Come to this informal hangout to ask for feedback on problems you are experiencing, to give advice or help others out with their problems, or just co-work with other likeminded researchers who also use (open) research software for their work. location: "Studiecaféen, Studenterhus, Nordre Ringgade 3, 8000 Aarhus" start_date: {date_ymd}T14:30:00+01:00 end_date: {date_ymd}T15:30:00+01:00 software: [""] level: "everyone" ' # All new file names to be created. hacky_files <- here::here("data", "events", str_c(hacky_dates$date_ymd, "-hacky-hour.yaml")) # Fill in the template with the contents of the events. hacky_file_contents <- hacky_dates %>% glue::glue_data(hacky_hours_template) # Create the event files. walk2(hacky_file_contents, hacky_files, write_lines)

60ade196f0694b1383c5d764590f131f7a051542

247168dd727c19cef2ce885476d3e4102d2ca7de

/man/auth_put.Rd

65f87e05642520320a83038338cfdaacf7616d91

[ "Apache-2.0" ]

permissive

DataONEorg/rdataone

cdb0a3a7b8c3f66ce5b2af41505d89d2201cce90

97ef173bce6e4cb3bf09698324185964299a8df1

refs/heads/main

2022-06-15T08:31:18.102298

2022-06-09T21:07:26

14,430,641

27

19

null

2022-06-01T14:48:02

2013-11-15T17:27:47

R

UTF-8

R

false

true

846

rd

auth_put.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/auth_request.R \name{auth_put} \alias{auth_put} \title{PUT a resource with authenticated credentials.} \usage{ auth_put(url, encode = "multipart", body = NULL, node) } \arguments{ \item{url}{The URL to be accessed via authenticated PUT} \item{encode}{the type of encoding to use for the PUT body, defaults to 'multipart'} \item{body}{a list of data to be included in the body of the PUT request} \item{node}{The D1Node object that the request will be made to.} } \value{ the HTTP response from the request } \description{ PUT data to a URL using an HTTP PUT request using authentication credentials provided in a client certificate. Authenticated access depends on the suggested openssl package. If the openssl package is not installed, then the request fails. }

fb19c08d3c7e0d715f02a346ba9b6a1ea641cd93

4add4f324b954c7dc2e53fc040108dd5d200ce2f

/R/code.R

1721b76db09003b8cf631adc891be1212091c509

[]

no_license

tdhock/requireGitHub

c12b211875be47bb878d2d05f5b9f0211dc592ed

f36a95a1542bbab0614ba20c610bfe4e6f497332

refs/heads/master

2020-12-29T02:19:36.062504

2019-05-17T16:03:10

18,137,680

1

0

null

2017-03-16T20:02:13

2014-03-26T12:46:55

R

UTF-8

R

false

893

r

code.R

##' Print a requireGitHub declaration. ##' @param ... unquoted package names. ##' @return An invisible character vector of repository/package ##' version codes. ##' @author Toby Dylan Hocking ##' @export ##' @examples ##' if(FALSE){ ##' requireGitHub_code(requireGitHub) ##' } requireGitHub_code <- function(...){ pkgs <- match.call()[-1] repo.code <- c() for(pkg.i in seq_along(pkgs)){ pkg.name <- as.character(pkgs[[pkg.i]]) pkg.info <- packageDescription(pkg.name) tryCatch({ repo.code[[pkg.i]] <- with(pkg.info, { sprintf("%s/%s@%s", GithubUsername, GithubRepo, GithubSHA1) }) }, error=function(e){ stop("GitHub meta-data not in ", pkg.name, " DESCRIPTION") }) } txt <- deparse(repo.code) txt.return <- sub("c[(]", "requireGitHub::requireGitHub(\n ", gsub("[ ]+", "\n ", txt)) cat(txt.return, "\n") invisible(repo.code) }

5fbd9b6eecd0577b8e3147592ea6b85e8e422fd5

7bae5569fd5509263b0cdd20fc1c6c14436410f9

/packages/RNASeq/summary/RCODE/readFiles.R

f06d9cb5bf058391f813bf99aa8eb3c090c64ed2

[]

no_license

cfbuenabadn/YosefCode2

fe578ac0e9d0ff5ce724209dde1379acae6ab0ad

35bd4e749301b728ad502d6327b88c01de71cbd3

refs/heads/master

2021-07-05T03:40:51.753643

2017-06-23T10:50:08

105,191,082

3

0

null

UTF-8

R

false

13,768

r

readFiles.R

#for debug rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/archive/users/allonwag/temp/big_pipe_out/collect" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_file.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=F, LOAD_CUFF=T, LOAD_KALLISTO=T) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") #collect_dir="/data/yosef/BRAIN/processed_Sep2015/collect" collect_dir="/data/yosef2/BRAIN/processed_olfactory_Jun2016/collect_20160818/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_olfactory.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="/data/yosef/BRAIN/processed_July2015/collect" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_cortical.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=T, LOAD_CUFF=T) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="/data/yosef2/BRAIN/processed_cortical_Oct2016/collect" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_cortical.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="/data/yosef/BRAIN/processed_Bateup_Aug2015/collect" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_bateup.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=T, LOAD_CUFF=T) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="/data/yosef/BRAIN/processed_Zebrafish_Oct2015/collect" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_samisrael.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=T, LOAD_CUFF=T) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data2/Published_Data/TH17/processed_aw20160712/collect" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_th17.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data2/Published_Data/Shalek_DC/co/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path("~/data2/Published_Data/Shalek_DC/config_shalek2014_aw.txt"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=FALSE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data2/TFH/processed_20160720/collect/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_tfh.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data2/TFH/processed_20160720/collectWithFateMapping/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_tfh.csv"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data/TFH/processed2/collect/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_FC_01930.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=FALSE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data/TFH/processed_20161012/FC_01930/collect/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_FC_01930.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=FALSE, LOAD_CUFF=FALSE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data2/BASF/Nutraceuticals/processed_RNASeq_20160826/collect/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_basf.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="~/data2/BASF/Nutraceuticals/processed_RNASeq_20160826/collect/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_basf.xlsx"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=TRUE, LOAD_KALLISTO=TRUE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="/data/yosef2/th17_RNA_Seq_Out/collect_both_pools" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "config_ChaoWang_Th17Meta_RNAseq_Sep20_2016.csv"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=FALSE, LOAD_KALLISTO=FALSE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData")) rm(list=ls()) setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE") source("loadProcessedRNASeq_NG.R") collect_dir="/data/yosef2/users/allonwag/Th17Metabolic/processed_1/collect/" collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir, config_file=file.path(collect_dir, "../../sources/all_configs.csv"), qc_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/qc_fields.txt", gene_fields_file="/data/yosef/CD8_effector_diff/src/YosefCode/packages/RNASeq/summary/TEXT/gene_fields.txt", LOAD_RSEM=TRUE, LOAD_CUFF=FALSE, LOAD_KALLISTO=FALSE) save(collectedRNASeqStudy, file=file.path(collect_dir, "collectedRNASeqStudy.RData"))

eb283f196984440799e98bbf106e166efff4e19d

2ae1c90fd6beefbf099342fbc48e079ca904b979

/legacy/dhfr/alt_rank_plot.R

fc9b82e30e278a1d3337e0a7192a6f91a78a8041

[]

no_license

SamStudio8/gretel-test

62bab2e1719d5cb20467549fe2ce429cb4b6d55d

9d168f64ab5485416c58f1b7976356ad89eea903

refs/heads/master

2021-10-26T03:26:47.408040

2021-10-18T13:19:46

64,262,553

2

null

2019-06-24T10:54:50

2016-07-26T23:54:56

Python

UTF-8

R

false

2,068

r

alt_rank_plot.R

library("ggplot2"); #d <- read.table(TABLE_P, header=T); #d <- read.table("fbc_hamming_wpd_wmeta.txt", header=T); d <- read.table("fbc_hamming_wpd_wmeta.withbestworstlikl.withabslikl.txt", header=T); #d <- d[d$cov == 10,] #p <- ggplot(d, aes(recovery, rank_w, colour=factor(in_hname))) + facet_grid(readsize~cov) + scale_y_reverse( lim=c(75,0)) + scale_x_reverse( lim=c(100,0)) + theme_grey(base_size=30) + geom_jitter(size=2) d <- d[d$n_outhaps > 0, ] d <- d[d$rank_w > -1, ] d <- d[d$worstlikl < 112.14, ] read_l_labels <- c("50"="50 bp", "100"="100 bp", "125"="125 bp", "150"="150 bp") d$in_hname_o = factor(d$in_hname, levels=c('BC070280', 'XR_634888', 'AK232978', 'M19237', 'XM_014960529')) ho_labels <- c(BC070280 = "BC070280 (99.8%)", XR_634888 = "XR_634888 (97.3%)", AK232978="AK232978 (90.1%)", M19237="M19237 (83.5%)", XM_014960529="XM_014960529 (78.7%)") d$rank_w <- (d$rank_w/(d$n_outhaps-1)) d$liklscale <- (d$abslik - min(d$bestlikll)) / (max(d$worstlikl) - min(d$bestlikll)) #d$liklscale <- (d$abslik - (d$bestlikll)) / ((d$worstlikl) - (d$bestlikll)) #d$liklscale <- (d$abslik - d$bestlikll) / (d$worstlikl - d$bestlikll) #d$liklscale <- (d$abslik - 0) / (d$worstlikl - 0) p <- ggplot(d, aes(recovery, rank_w, colour=factor(cov))) + facet_grid(readsize~in_hname_o, labeller=labeller(in_hname_o = ho_labels, readsize=read_l_labels)) + scale_y_reverse(lim=c(1.01,-0.01)) + scale_x_reverse( lim=c(101,39)) + theme_grey(base_size=30) + geom_point(size=2.5, alpha=0.8) #p <- p + theme(panel.grid.major.x=element_line(color="gray")) #p <- p + theme(panel.grid.major.y=element_line(color="gray")) p <- p + theme(legend.key = element_rect(size = 5), legend.key.size = unit(1.5, 'lines')) p <- p + theme(strip.text=element_text(size=30)) p <- p + theme(legend.position="bottom") p <- p + xlab("Correctly Recovered SNPs (% Proportion) - Facets: Input Haplotype (Reference Identity)") p <- p + ylab("Haplotype Scaled Likelihood - Facets: Synthetic Read Length") p <- p + guides(colour=guide_legend(title="Depth",nrow=1)) ggsave("dhfr-ranks.png")

3cdb84092d446505f3e53e7765cfabc047ce8278

1817920a05d0282936b6bd88fcbc5eeb9cbbcaf0

/run_analysis.R

2acb8eddde9a1be508892fceb3ed3212279c016b

[]

no_license

sallytian/Getting-and-Cleaning-Data-Project

0114324db338871b3e3583c7827852f6d2dcd148

0d805b555fddf086ac9e8cff20800697c95afd81

refs/heads/master

2020-05-19T18:52:56.983970

2015-02-22T22:09:39

31,182,117

0

null

UTF-8

R

false

1,690

r

run_analysis.R

## Get features names feature <- read.table("./UCI HAR Dataset/features.txt") colnames(feature) <- c("No", "Names") ## Extracts mean and std related features index <- grep("mean|std", feature$Names) ## Get test and train data with desired features testAll <- read.table("./UCI HAR Dataset/test/X_test.txt") test <- testAll[ , index] trainAll <- read.table("./UCI HAR Dataset/train/X_train.txt") train <- trainAll[ , index] ## Merge test and train data with desired features into one dataset all <- rbind(test, train) ## Add descriptive column names colnames(all) <- feature[index, 2] ## Read test and train labels&subjects testlabel <- read.table("./UCI HAR Dataset/test/y_test.txt") trainlabel <- read.table("./UCI HAR Dataset/train/y_train.txt") label <- rbind(testlabel, trainlabel) colnames(label) <- "label" testsubject <- read.table("./UCI HAR Dataset/test/subject_test.txt") trainsubject <- read.table("./UCI HAR Dataset/train/subject_train.txt") subject <- rbind(testsubject, trainsubject) colnames(subject) <- "subject" ## Map activities labels activity <- read.table("./UCI HAR Dataset/activity_labels.txt") colnames(activity) <- c("label", "activity") label$activity <- factor(label$label, levels = activity$label, labels = activity$activity) ## Merge label and subject info into dataset all <- cbind(label, subject, all) ## Create a dataset with average of each variable for each activity and each subject col <- as.character(feature[index, 2]) tidy <- aggregate(all[ ,col], by = list(all$subject, all$activity), FUN = mean) colnames(tidy)[1] <- "subject" colnames(tidy)[2] <- "activity" write.table(tidy, file = "tidy.txt", row.names = FALSE)

306170c36139a0e68014b913e4dbdd1f959919c0

2281cdb6065a06304e9ed82649d546e1e64edce3

/man/print_class_id.Rd

f57b099793ba67ddbe378d7be91cb35ab0c1842d

[ "Apache-2.0" ]

permissive

hhoeflin/hdf5r

142d391e60d97b57a4fcba76a407ef9bc0e08984

450d483364e3bf84db19df7eb330633952ec31ae

refs/heads/master

2023-08-17T07:04:15.470093

2023-01-21T15:57:59

70,118,016

77

29

NOASSERTION

2023-06-22T13:49:27

2016-10-06T02:58:42

C

UTF-8

R

false

true

472

rd

print_class_id.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Helper_functions.R \name{print_class_id} \alias{print_class_id} \title{Print the class and ID} \usage{ print_class_id(obj, is_valid) } \arguments{ \item{obj}{The object for which to print the class and id} \item{is_valid}{is the object valid} } \value{ invisible NULL } \description{ Print the class and ID } \details{ Used by the print-methods } \author{ Holger Hoefling } \keyword{internal}

35c04fc06edc51d5e74490d7e36108a5e5c30aa1

b9c2609f7ba23410bb549383a7edc1faefd47c3c

/R_scripts/021_PeriodicProcessTemplate.R

5753c009de080be894cc3ede0e6231bf63955712

[]

no_license

stochastictalk/msc_statistics_thesis

8faa6f4ce3a9384e6f1728e5e431508de89c88d6

fb584d8b34c33e527106b369983ec56d3b10e833

refs/heads/main

2023-01-12T04:23:59.325525

2020-11-20T13:37:38

313,441,237

0

null

UTF-8

R

false

318

r

021_PeriodicProcessTemplate.R

rm(list=ls()) T_ <- 10 j <- seq(1, T_) sigma_P <- 0.1 sigma_P <- sqrt(1/(1 - mean(sin(2*pi*j/T_)^2))) N <- 1000 X_t <- rnorm(N, mean=0, sd=1) X_t <- c(X_t, rnorm(N, mean=sin(2*pi*seq(1, N)/T_), sd=1/sigma_P)) plot(X_t) EX <- c(rep(0, N), sin(2*pi*seq(1, N)/T_)) lines(EX) mean(X_t[(N+1):(2*N)]^2) mean(X_t[1:N]^2)

e23551d3e6f4833e80d4da33ffc78b9e24f40e16

a07c8e474c1f44d69ed862bea9f571e48c61dfc9

/missing-values/missing-values-poisson.R

aea371192b12c22f5eee75c479a18fe1f00dd725

[]

no_license

zlliang/statistical-computing-experiments

11fa050af5ad29895b730fe33847c69547676089

e67a6e1927c372962fcb4f6b28df73db7ecf8487

refs/heads/master

2020-03-17T14:28:03.972110

2018-05-24T10:37:07

133,673,166

0

null

UTF-8

R

false

2,270

r

missing-values-poisson.R

# ----------------------------------------------------------- # Statistical Computing Experiments # ----------------------------------------------------------- # EM Algorithm for Poisson Mixture Distribution # Author: Zilong Liang # Date: 2018-04-04 # ----------------------------------------------------------- # ----------------------------- # Main function of EM Algorithm # ----------------------------- poismm <- function(x, k, tol = 1e-6, iter.max = 200) { # Check the samples n <- nrow(x) # Initialize the parameters tau <- runif(k - 1, 0, 1 / k); tau <- c(tau, 1 - sum(tau)) lambda <- runif(k) # EM iterations iter <- 0 # Iteration index repeat { # E-Step f <- matrix(nrow = n, ncol = k) for (j in 1:k) { # TODO: vectorization? f[, j] <- dpois(x, lambda[j]) } e.weighted <- f %*% tau e <- matrix(0, nrow = n, ncol = k) for (j in 1:k) { # TODO: vectorization? e[, j] <- f[, j] * tau[j] } e <- e / rowSums(e.weighted) # M-Step sum.e <- colSums(e) tau.new <- sum.e / n lambda.new <- c(t(e) %*% x / sum.e) # Judge convergence iter <- iter + 1 if (iter > iter.max) { break } err.tau <- norm(rbind(tau.new - tau), "I") / norm(rbind(tau), "I") err.lambda <- norm(rbind(lambda.new - lambda), "I") / norm(rbind(lambda), "I") err.max <- max(c(err.tau, err.lambda)) if (err.max < tol) { break } # Iterate the parameters tau <- tau.new lambda <- lambda.new } return (list(tau, lambda)) } # ---------- # Experiment # ---------- # Read sample data x <- read.csv("data.csv") x <- as.matrix(x[, 2]) # Estimate parameters estimates <- poismm(x, 2) tau <- estimates[[1]] lambda <- estimates[[2]] # Prepare plotting pfunc <- function(xx, tau, lambda) { # PDF of Gaussian Mixture Distribution k = length(tau) yy <- 0 for (j in 1:k) { yy <- yy + tau[j] * dpois(xx, lambda[j]) } return (yy) } # Plotting xx <- seq(0, 11) yy = pfunc(xx, tau, lambda) hist(x, freq = FALSE, breaks = c(-0.5:11.5), xlab = "k", ylab = "Density or Possibility", main = "EM Algorithm on Poisson Mixture Distribution", family = "serif") points(xx, yy, pch = 18, cex = 1.5, col = "#b28fce")

8b77c0fa374b68afe2988bf2ee9886251dba4d7d

5c2350f172e1a7b7f61e1047d515357735e5895e

/man/christmas_stats_participants.Rd

5ea87ef46ea44ddc4c4d67fe6fbe843569c2603b

[ "CC-BY-4.0", "MIT", "LicenseRef-scancode-public-domain", "LicenseRef-scancode-unknown-license-reference" ]

permissive

richarddmorey/Morey_Hoekstra_StatCognition

4da5b3f205d1038b850fa701354bd59b62a05eed

373b9ac75219d84d7b5a6454296e80aa4f34ea54

refs/heads/master

2022-12-06T14:50:55.198542

2022-11-30T18:23:58

189,821,493

4

1

null

UTF-8

R

false

true

5,889

rd

christmas_stats_participants.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/christmas_stats_public-data.R \docType{data} \name{christmas_stats_participants} \alias{christmas_stats_participants} \title{Cleaned, participant-level data} \description{ Participant-level data for the Christmas statistical cognition experiment } \details{ \itemize{ \item duration: Qualtrics-reported time taken in the experiment (seconds) \item id: participant id \item consent: Response to informed consent \item times: "Have you participated previously"? \item shuffle: "Do you understand use of shuffle reports?" \item shuffle_other" (text) response for "Other" responses to \code{shuffle} \item response: primary response: which team is faster? \item confidence: participant's confidence in their \code{response} \item salient_factors: (text) "What facts or observations were most salient in coming to the conclusion that you did?" \item expt_strategy: (text) "Please describe your experimental strategy, if any, in your own words." \item shuffle_desc: (text) "Did you make use of the "random shuffle reports"? If so, how?" \item is_science: "Is your work in a field that would typically be considered scientific?" \item is_science_other: (text) response for "Other" responses to \code{is_science} \item education: highest level of formal education in scientific field \item formal_training: years of formal statistical training \item how_use: "How do statistics play a role in your work?" Concatenated string of responses \item field: "In what applied field(s) do you use statistics?" Concatenated string of responses \item preferred: "What sort of inferential procedures would you typically prefer?" \item preferred_other: (text) response for "Other" responses to \code{preferred} \item sig_testing: "What is your opinion about statistical significance testing?" Concatenated string of responses \item mobile: Qualtrics flag for mobile browsers. Should be blank for all participants \item evidence_power: Randomly-assigned transformation power. 3 or 7 \item effect_size: "true" effect size in standard deviation units, hidden from the participant \item finished_practice_timestamp: javascript timestamp reported by browser when participant ended the instructions. Indexed to initial_timestamp \item downloaded_expt: Did the participant download their experimental samples? \item downloaded_null: Did the participant download their null samples? \item initial_timestamp: initial timestamp (should be 0, start of experiment) \item true_winner: Which "team" was the true winner? Corresponds to sign of effect_size \item true_null: Was the effect size 0? \item response_null: Was the participant's response "null"? (same or no detection) \item response_alt: Negation of \code{response_null} \item n_expt: Number of experimental samples requested \item n_null: Number of random shuffle reports requested \item how_use.analysis: (logical) Did the participant select the corresponding response to \code{how_use}? \item how_use.develop: (logical) Did the participant select the corresponding response to \code{how_use}? \item how_use.philosophy: (logical) Did the participant select the corresponding response to \code{how_use}? \item how_use.method_comment: (logical) Did the participant select the corresponding response to \code{how_use}? \item how_use.none: (logical) Did the participant select the corresponding response to \code{how_use}? \item how_use.other: (logical) Did the participant select the corresponding response to \code{how_use}? \item field.bio: (logical) Did the participant select the corresponding response to \code{field}? \item field.phys: (logical) Did the participant select the corresponding response to \code{field}? \item field.soc_beh: (logical) Did the participant select the corresponding response to \code{field}? \item field.comp_tech: (logical) Did the participant select the corresponding response to \code{field}? \item field.med: (logical) Did the participant select the corresponding response to \code{field}? \item field.other: (text) If applicable, text response to "Other" responses for \code{field}. \item sig_testing.necessary: (logical) Did the participant select the corresponding response to \code{sig_testing}? \item sig_testing.prefer_other: (logical) Did the participant select the corresponding response to \code{sig_testing}? \item sig_testing.misunderstood: (logical) Did the participant select the corresponding response to \code{sig_testing}? \item sig_testing.no_opinion: (logical) Did the participant select the corresponding response to \code{sig_testing}? \item sig_testing.discontinued: (logical) Did the participant select the corresponding response to \code{sig_testing}? \item sig_testing.fatally_flawed: (logical) Did the participant select the corresponding response to \code{sig_testing}? \item sig_testing.do_not_understand: (logical) Did the participant select the corresponding response to \code{sig_testing}? \item sig_testing.other: (text) If applicable, text response to "Other" responses to \code{sig_testing} \item text_comparison: (Strategy questions) Response mentioned comparison to the random shuffle reports \item text_asymmetry: (Strategy questions) Response mentioned symmetry/asymmetry in the experimental samples \item text_sampling_var: (Strategy questions) Response mentioned random shuffle reports as a means to assess sampling variability, distribution under the null, chance distribution, etc \item text_inc_asymmetry: (Strategy questions) Response mentioned increasing asymmetry (or lack thereof) as sample size increased \item text_no_shuffle: (Strategy questions) Explicitly said they did not use the shuffle reports \item text_irrelevant: (Strategy questions) Text was irrelevant \item text_missing: (Strategy questions) Text was missing } } \author{ Richard D. Morey \email{richarddmorey@gmail.com} } \keyword{data}

cad557fbd9bc866b9876047f72e264235ef4d62e

45fcec2ad46e80b31ab2cd855feb2a0b6a7d7431

/plot3.R

d3e1239518782abcbea41f69efd60dde1ed48370

[]

no_license

lamwai/ExData_Plotting1

371c76090b91c039e8083f3b5d00ba1cdf7dbc6c

289345adeaab504323a055107bb62fddb592460d

refs/heads/master

2021-01-21T17:50:21.513968

2014-09-05T12:15:10

null

0

null

UTF-8

R

false

1,183

r

plot3.R

## read the complete file and read each column as character type fulldata<-read.csv("household_power_consumption.txt", sep=";", colClasses=c(rep("character",9))) ## filter only the first 2 days in Feb 2007, and output the first three columns, ## comprising the date, time and Global Active Power tmpdata <- subset(fulldata, Date=="1/2/2007" | Date=="2/2/2007", select=c(1,2,7,8,9)) ## create a new datetime column based on the first two columns tmpdata <- cbind(tmpdata,strptime(paste(tmpdata[,1],tmpdata[,2]), "%d/%m/%Y %H:%M:%S")) ## Global Active Power, convert to numeric tmpdata[,3]<-as.numeric(tmpdata[,3]) ##sub metering 1 tmpdata[,4]<-as.numeric(tmpdata[,4]) ##sub metering 2 tmpdata[,5]<-as.numeric(tmpdata[,5]) ##sub metering 3 ## setup the png device output png(file="plot3.png", width=480, height=480) ## plot the graph per specification, using line type plot(tmpdata[,6], col="Black",tmpdata[,3], type="l", xlab="", ylab="Energy sub metering") lines(tmpdata[,6], tmpdata[,4], col="Red") lines(tmpdata[,6], tmpdata[,5], col="Blue") legend("topright", lwd=1, col=c("Black","Red","Blue"), legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) dev.off()

2addfa87785fe4ca46e8b77332a5b46d119ef907

ee0689132c92cf0ea3e82c65b20f85a2d6127bb8

/23-functions/49c-replicate.R

236c9742267ddddf47c5d0419d84576b9a2ef343

[]

no_license

DUanalytics/rAnalytics

f98d34d324e1611c8c0924fbd499a5fdac0e0911

07242250a702631c0d6a31d3ad8568daf9256099

refs/heads/master

2023-08-08T14:48:13.210501

2023-07-30T12:27:26

201,704,509

203

29

null

UTF-8

R

false

250

r

49c-replicate.R

#replicate # ?replicate replicate(n, expr, simplify = "array") replicate(4, rnorm(5)) my.fun = function() { for (i in 1:1000) { ... for (j in 1:20) { ... } } return(output) } rep(1:4,len=20) replicate(1:4,len=20)

acbb011f14079e7c8ffafee6d83a86b1a778ed6c

6e9abf08a2d2728495c89611c9e8a1517ff329d8

/man/gwc_parse_args.Rd

4ef5daaae50fac22f038a4ecb3a6969181d64765

[]

no_license

ebi-gene-expression-group/workflowscriptscommon

ef307c615d07599e65dc00bed3334acd291d1956

59d978c4c27df5e6bd1a3fbea443305cff3312b2

refs/heads/develop

2022-03-15T12:34:14.491968

2022-02-13T10:41:00

141,469,535

0

1

null

2022-02-09T22:43:13

2018-07-18T17:43:06

R

UTF-8

R

false

true

542

rd

gwc_parse_args.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/arguments.R \name{gwc_parse_args} \alias{gwc_parse_args} \title{Wrap optparse's parse_args() to add support for mandatory arguments} \usage{ gwc_parse_args(option_list, mandatory = c()) } \arguments{ \item{option_list}{List of OptionParserOption objects} \item{mandatory}{Character vector of mandatory parameters} } \value{ a list containing option values, as per parse_args() } \description{ Wrap optparse's parse_args() to add support for mandatory arguments }

da7f9ad219787598b1a5c7ecfb087913bdf1c00f

f8588995f20739d4ffadd60baa48dafc85a3d1fa

/perceptron_pocket.r

d241912456f81b48d04bae351c5d95c680a7fbcf

[]

no_license

gauravshelangia/ai-lab

90ef8bf6d76d7f2e2373c51ec027ae6da25888b0

3d19c8e5ce849f0a974620e0d1080688b380991c

refs/heads/master

2020-12-01T01:17:52.696436

2016-02-11T09:25:35

51,135,556

0

null

2016-02-05T08:42:03

2016-02-05T08:42:02

null

UTF-8

R

false

1,780

r

perceptron_pocket.r

# By Gaurav Yadav # reading file and storing that in matrix form train = as.matrix(read.table("Iris_data_norm_train.txt",sep=",")) test <- as.matrix(read.table("iris_data_norm_test.txt",sep=",")) H<- function(x){ if(x > 0){ r <- 1 } else{ r <- -1 } return (r) } # initial random weight are runif(len,in,last in_size <- ncol(train)-1 w <- runif(in_size,0,1) #for plotting the error in each learning step make an array to store them errorin <- vector(mode="numeric",length=0) errorout <- vector(mode="numeric",length=0) rows <- nrow(train) test_rows <- nrow(test) # initial large number of error error_temp=10000 w_pre <- vector(mode="numeric") for(f in 1:100){ no_errin=0 no_errout=0 for (i in 1:rows ){ x <- train[i,1:in_size] x <- as.numeric(x) expected <- as.numeric(train[i,in_size+1]) result = w%*%x error = expected - H(result) w <- w + error*x } for(i in 1:rows){ #i <- as.integer(runif(1,1,rows)) x <- train[i,1:in_size] x <- as.numeric(x) expected <- as.numeric(train[i,in_size+1]) result = w%*%x if(result<0 && expected == 1){ no_errin <- no_errin+1 } if(result>0 && expected == -1){ no_errin <- no_errin+1 } } if(error_temp < no_errin){ w <- w_pre } error_temp <- no_errin w_pre <- w for(i in 1:test_rows){ y <- test[i,1:in_size] ex_out <- as.numeric(test[i,in_size+1]) y <- as.numeric(y) result_out = y%*%w if(result_out<0 && ex_out == 1){ no_errout <- no_errout+1 } if(result>0 && ex_out == -1){ no_errout <- no_errout+1 } } errorin <- c(errorin,no_errin) errorout <- c(errorout,no_errout) } print("END") w #points(iris$Petal.Length, iris$Petal.Width,iris$Sepal.Lenght , pch=19, col=iris$Species)

0f61410a08bcf76d911904b428b35ce7f1253507

3727eb350c9f10d1f835314115e401e76ea8e913

/EDA_VisualizationScripts.R

dce4e0e588acc2e03635c8550f4da8aafc2200a2

[]

no_license

TaneishaArora/Pfft

950b6497811fb712ccb6f30f14a618d30b95e81a

0582369efa952be0a4ae7539e01ce84a0a9f5ccd

refs/heads/master

2022-08-19T08:04:24.023120

2020-05-24T12:43:44

265,209,275

0

null

2020-05-24T12:43:45

2020-05-19T09:53:26

R

UTF-8

R

false

7,135

r

EDA_VisualizationScripts.R

library(tidyverse) library(ggplot2) # Dividing the data up by visit month baseline <- amyloid %>% filter(month == 0) # Test score Progression at baseline, by different demographic traits # Sex baseline %>% gather("test_number", "score", c(t1sum, t2sum, t3sum, t4sum, t5sum, t6sum, t7sum)) %>% ggplot(aes(x = test_number, fill = sex)) + scale_x_discrete(labels = c("IR 1", "IR 2", "IR 3", "IR 4", "IR 5", "DR 1", "DR 2")) + geom_boxplot(aes(x= test_number, y = score)) + scale_fill_discrete(name = "Sex", labels = c("Female", "Male")) + labs(x = "AVLT", y = "Score") # Amyloid Positivity baseline %>% gather("test_number", "score", c(t1sum, t2sum, t3sum, t4sum, t5sum, t6sum, t7sum)) %>% ggplot(aes(x = test_number, fill = abeta6mcut)) + scale_x_discrete(labels = c("IR 1", "IR 2", "IR 3", "IR 4", "IR 5", "DR 1", "DR 2")) + geom_boxplot(aes(x= test_number, y = score)) + scale_fill_discrete(name = "Amyloid Positivity", labels = c("Positive", "Negative")) + labs(x = "AVLT", y = "Score") # Genotype baseline %>% gather("test_number", "score", c(t1sum, t2sum, t3sum, t4sum, t5sum, t6sum, t7sum)) %>% ggplot(aes(x = test_number, fill = genotype)) + scale_x_discrete(labels = c("IR 1", "IR 2", "IR 3", "IR 4", "IR 5", "DR 1", "DR 2")) + geom_boxplot(aes(x= test_number, y = score)) + scale_fill_discrete(name = "Genotype", labels = c("e2/e2", "e2/e3", "e3/e3", "e2/e4", "e3/e4", "e4/e4")) + labs(x = "AVLT", y = "Score") # Diagnosis baseline %>% gather("test_number", "score", c(t1sum, t2sum, t3sum, t4sum, t5sum, t6sum, t7sum)) %>% ggplot(aes(x = test_number, fill = dx)) + scale_x_discrete(labels = c("IR 1", "IR 2", "IR 3", "IR 4", "IR 5", "DR 1", "DR 2")) + geom_boxplot(aes(x= test_number, y = score)) + scale_fill_discrete(name = "Diagnosis", labels = c("Cognitively Normal", "Subjectively Cognitively Impaired", "Objective Mild Cognitive Impairment")) + labs(x = "AVLT", y = "Score") # Education ntile(baseline$edu, 3) # To check ranges baseline %>% mutate (edu_cat = ntile(edu, 3)) %>% filter(edu_cat == 1) %>% summarise(min(edu), max(edu)) baseline %>% mutate (edu_cat = ntile(edu, 3)) %>% filter(edu_cat == 2) %>% summarise(min(edu), max(edu)) baseline %>% mutate (edu_cat = ntile(edu, 3)) %>% filter(edu_cat == 3) %>% summarise(min(edu), max(edu)) baseline %>% gather("test_number", "score", c(t1sum, t2sum, t3sum, t4sum, t5sum, t6sum, t7sum)) %>% mutate(edu_factor = as.factor(ntile(edu, 3))) %>% ggplot(aes(x = test_number, fill = edu_factor)) + scale_x_discrete(labels = c("IR 1", "IR 2", "IR 3", "IR 4", "IR 5", "DR 1", "DR 2")) + geom_boxplot(aes(x= test_number, y = score)) + scale_fill_discrete(name = "Education Bracket", labels = c("<= Bachelors", "<= Masters", "<= PhD")) + labs(x = "AVLT", y = "Score") # Age ntile(baseline$age, 3) # To check ranges baseline %>% mutate (age_cat = ntile(age, 3)) %>% filter(age_cat == 1) %>% summarise(min(age), max(age)) baseline %>% mutate (age_cat = ntile(age, 3)) %>% filter(age_cat == 2) %>% summarise(min(age), max(age)) baseline %>% mutate (age_cat = ntile(age, 3)) %>% filter(age_cat == 3) %>% summarise(min(age), max(age)) baseline %>% gather("test_number", "score", c(t1sum, t2sum, t3sum, t4sum, t5sum, t6sum, t7sum)) %>% mutate(age_factor = as.factor(ntile(age, 3))) %>% ggplot(aes(x = test_number, fill = age_factor)) + scale_x_discrete(labels = c("IR 1", "IR 2", "IR 3", "IR 4", "IR 5", "DR 1", "DR 2")) + geom_boxplot(aes(x= test_number, y = score)) + scale_fill_discrete(name = "Age Range", labels = c("[54-72)", "[72-77)", "[77, 89]")) + labs(x = "AVLT", y = "Score") ################################################################################################## # Amyloid Beta Measures at baseline based on various demographic traits # sex amyloid %>% ggplot(aes(x = month, fill = sex)) + geom_boxplot(aes(x= month, y = abeta6m)) + scale_fill_discrete(name = "Sex", labels = c("Female", "Male")) + labs(x = "Month", y = "Amyloid Beta Levels") # Genotype amyloid %>% ggplot(aes(x = month, fill = genotype)) + geom_boxplot(aes(x= month, y = abeta6m)) + scale_fill_discrete(name = "Genotype", labels = c("e2/e2", "e2/e3", "e3/e3", "e2/e4", "e3/e4", "e4/e4")) + labs(x = "Month", y = "Amyloid Beta Levels") # Diagnosis amyloid %>% ggplot(aes(x = month, fill = dx)) + geom_boxplot(aes(x= month, y = abeta6m)) + scale_fill_discrete(name = "Diagnosis", labels = c("Cognitively Normal", "Subjectively Cognitively Impaired", "Objective Mild Cognitive Impairment")) + labs(x = "Month", y = "Amyloid Beta Levels") # Education amyloid %>% mutate(edu_factor = as.factor(ntile(edu, 3))) %>% ggplot(aes(x = month, fill = edu_factor)) + geom_boxplot(aes(x = month, y = abeta6m)) + scale_fill_discrete(name = "Diagnosis", labels = c("<= Bachelors", "<= Masters", "<= PhD")) + labs(x = "Month", y = "Amyloid Beta Levels") # Age amyloid %>% mutate(age_factor = as.factor(ntile(age, 3))) %>% ggplot(aes(x = month, fill = age_factor)) + geom_boxplot(aes(x= month, y = abeta6m)) + scale_fill_discrete(name = "Age", labels = c("[54-72)", "[72-77)", "[77, 89]")) + labs(x = "Month", y = "Amyloid Beta Levels") ################################################################################################## # Proportion Right/Wrong for Recogition Test # get average values of drec hits and fa recognition_prop <- baseline %>% group_by(abeta6mcut) %>% summarise( drec_hits_mean = mean(drec_hits, na.rm = TRUE), drec_fa_mean = mean(drec_fa, na.rm = TRUE) ) # transform data recognition_prop <- as.data.frame(t(recognition_prop)) # remove the abeta6mcut row (first row) recognition_prop = recognition_prop[-1,] # combine both scores columns into one recognition_prop <- data.frame(Score = c(recognition_prop[,"V1"], recognition_prop[,"V2"])) # populate drec and amyloid positivity accordingly recognition_prop <- recognition_prop %>% mutate( drec = if_else(row_number() %% 2 == 0, "False Alarm", "Hit"), amyloid_positivity = if_else(row_number() < 3, "Positive", "Negative"), ) # convert scores from char to numeric recognition_prop$Score <- as.numeric(as.character(recognition_prop$Score)) sapply(recognition_prop, class) # plot ggplot(recognition_prop, aes(fill=drec, y=Score, x=amyloid_positivity)) + geom_bar(position="fill", stat="identity") + ggtitle("Word Recognition Hits/False Alarms") + labs(x="Amyloid Positivity", y="Relative Score", col="Recognized Words") ################################################################################################## # Delta AVLT Score vs. Amyloid Positivity baseline %>% gather("test_number", "score", c(t1t2, t2t3, t3t4, t4t5, t5t6, t6t7)) %>% ggplot(aes(x = test_number, fill = abeta6mcut)) + scale_x_discrete(labels = c("IR1-IR2", "IR2-IR3", "IR3-IR4", "IR4-IR5", "IR5-DR1", "DR1-DR2")) + geom_boxplot(aes(x= test_number, y = score)) + scale_fill_discrete(name = "Amyloid Positivity", labels = c("Positive", "Negative")) + labs(x = "AVLT", y = "Score") + ggtitle("Delta AVLT Scores at Baseline")

9e0c13ae6640826919acd8a9d936a4e4c4d15fde

b47aa2e09add49ab85ec3b04c3e3279f28706c1c

/man/FitGP_MLE.Rd

fb9cffcf02db34a058cf52cd0d0d71f06721434f

[]

no_license

ceesfdevalk/EVTools

db232bc94b0a22b1a0fdfbd8ba5e6e9e94e8ad3c

0e3440f031b6a8abcfd6fc00d981d0656710d93e

refs/heads/master

2022-09-30T06:25:51.784236

2022-08-22T07:48:01

130,972,770

0

null

UTF-8

R

false

true

4,162

rd

FitGP_MLE.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/FitGP_MLE.R \name{FitGP_MLE} \alias{FitGP_MLE} \title{FitGP_MLE} \usage{ Value <- FitGP_MLE(X, p, N= 0, r11= 1, fixedpar= NULL, l0= NULL, metadata= NULL) } \arguments{ \item{X}{data sample (double(n))} \item{p}{probabilities of exceedance of the quantiles to be estimated (double(np))} \item{N}{(optional) (effective) sample size, in case X is not complete but contains only (peak) values above some threshold (integer(1))} \item{r11}{(optional) factor to increase estimator variance by, to account for serial dependence (default: 1) (double(1) or list, see Details)} \item{fixedpar}{(optional): fixed model parameters not to be estimated, and their standard errors (double(1) or list, see Details)} \item{l0}{(optional) value of l (no. of order stats used) in case it is imposed (integer(0))} \item{metadata}{(optional) information about the variable and, if applicable, the time-series (list; see Details)} } \value{ A list, with members: \item{l}{no. of order statistics used for scale and quantile estimation} \item{k}{no. of order statistics used for tail index estimation} \item{tailindex}{estimates or imposed value of GP tail index} \item{tailindexStd}{standard deviations of tail index estimates} \item{logdisp}{estimates or imposed value of log of dispersion coeff.} \item{logdispStd}{standard deviations of log of dispersion coeff. estimates} \item{scale}{estimates of GP scale parameter} \item{locationStd}{standard deviation of order statistic} \item{lambda}{ratio of logarithms of probabilities of exceedance of quantile and threshold} \item{p}{probabilities of exceedance of quantiles to be estimated} \item{quantile}{quantile estimates} \item{quantileStd}{standard deviations of quantile estimates} \item{orderstats}{data X sorted (decreasing)} \item{df}{= "GP": fitted distribution function tail (Generalised Pareto)} \item{estimator}{= "maximum likelihood": see "method" below} } \description{ Fit a Generalised Pareto (GP) upper tail to the sample X and estimate quantiles, using the ML estimator for tail index and scale } \details{ Pre-determined model parameters are to be supplied in the list fixedpar (see above): \itemize{ \item{$gamma0: (optional) value of tailindex in case it is imposed (double(1))} \item{$gamma0Std: (optional) its standard deviation (double(1))} \item{$logdisp0: (optional) value of log of dispersion coeff. in case it is imposed (dispersion coeff. is the raio of scale par. to location par.) (double(1))} \item{$logdisp0Std: (optional) its standard deviation (double(1))} } The serial dependence coefficient r11 can be a positive number, or a list produced by R11.R. In case a quantile is to be estimated for a \emph{frequency}, say f, and \enumerate{ \item{if X contains all values (possibly above some threshold), then with EI an estimate of the Extremal Index from EI.R, set p = f*d/EI and N = T/d, with T the length of the observation period and d the time step. Note that f and d are defined with reference to the same unit of time!! In this case, r11 needs to be estimated. } \item{if X contains only the n (approximately Poisson) peak values above some threshold (in a PoT analysis), it is recommended to set r11= 1 and take p = f*d/EI and N = T/d*EI; in this case (for GP), EI can be any value; e.g. take p= fT/n and N= n. } } metadata may contain the following fields (in addition to your own meta data): \itemize{ \item{$varname: variable name} \item{$varunit: physical unit of variable} \item{$timeunit: time unit (e.g. year)} \item{$timestep: time step in units of timeunit} \item{$timelength: length of time covered by time-series, in units of timeunit} \item{$EI: extremal index (see above)} \item{$nexcess (for PoT only): no. of data values (as opposed to peak values) exceeding the threshold} } } \references{ De Haan, L. and A. Ferreira (2006), Extreme Value Theory - An Introduction. Springer. } \author{ Cees de Valk \email{ceesfdevalk@gmail.com} }

da5b724c0aa24a5b30b6286bb51016c7d3d55402

0fb3d61813752e6134b4dc4d88876d380e31c60b

/code/RunMosaicStates.R

7364d2de40e37a8dfa79d3e53155a1388a068654

[]

no_license

melaniekamm/MergeLANDFIREandCDL

ec96612568a0e090ee4df0b5ec127e3b2cd6cf42

1df8767ef662631d115f3c99e068b6615aa0603a

refs/heads/main

2023-04-08T15:15:13.270691

2023-02-24T19:20:39

351,850,305

0

null

UTF-8

R

false

582

r

RunMosaicStates.R

args <- commandArgs(trailingOnly = T) message(args) # specify input parameters CDLYear <- args[2] # year of NASS Cropland Data Layer tier <- unlist(stringr::str_split(args[3], pattern=":")) # which hierarchy of mosaic states to process message(tier) #outdir <- 'D:/MergeLANDFIRECDL_Rasters/2017MergeCDL_LANDFIRE/' #file path on laptop outdir <- '/90daydata/geoecoservices/MergeLANDFIREandCDL/' statedir <- paste0(outdir,'/StateRasters/', CDLYear) ID <- paste0('CDL', CDLYear,'NVC') beecoSp::mosaic_states(outdir=outdir, statedir=statedir, ID=ID, tier=tier, usepackage='gdal')

7d2ae9f29c19d5a512916fb66d7a458954fbbe0e

f5f6069fc04306383a2b1e015dc9925d57543442

/R_Programming/R_Programming_Coursera/Assignment_Week_4/rankall.R

e6a27cd26adda8b28cfa50f776b5c85325fe37f9

[]

no_license

grizztastic/projects

42b408b2321a27610691629451a014d982533c6c

03019a82b16a23264a8eb1394ea30bc7dc7beb4a

refs/heads/master

2023-01-27T17:33:01.962227

2020-12-02T03:05:32

267,395,406

0

null

UTF-8

R

false

1,592

r

rankall.R

rankall <- function(outcome, num = "best") { ## Read outcome data outcome1 <- read.csv("rprog_data_ProgAssignment3-data/outcome-of-care-measures.csv", colClasses = "character") ## Check that the outcomes are valid unique_outcomes <- c("heart attack", "heart failure", "pneumonia") if(!outcome %in% unique_outcomes){ stop("invalid outcome") } ## Get the desired column to examine based on the outcome condition if (outcome == "heart attack") { col <- outcome1$Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack } else if (outcome == "heart failure") { col <- outcome1$Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure } else { col <- outcome1$Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia } ## Sorting the hospital by state dataframe by the desired column values in ascending order and removing NA vals sorted_df <- outcome1[order(as.numeric(col), outcome1[,2], na.last = NA), ] split_df_by_state <- split(sorted_df, sorted_df$State) ## Split the sorted dataframe by state and loop over the entire list returning the desired hospital name at the num ans = lapply(split_df_by_state, function(x, num) { if(num == "best") { return (x$Hospital.Name[1]) } else if(num == "worst") { return (x$Hospital.Name[nrow(x)]) } else { return (x$Hospital.Name[num]) } }, num) ## Return a data.frame with the proper form return (data.frame(hospital=unlist(ans), state=names(ans)) ) }

6b8278bebce650dd21b1f211009d9042dc3813df

40051d5f9e1fe85adbbb5cebf42bb541012e5237

/R/calculatePosteriors.R

c42edd2b6040872e085b4118b7076ad050e86e47

[]

no_license

ndukler/tkSim

e5f1edce59a746c61a71e7bb22b55a9f097cd9e0

0520ba0467dda9a68f786943237c85ec04760e38

refs/heads/master

2021-05-11T15:35:58.096459

2018-05-01T18:33:40

117,736,436

1

0

null

UTF-8

R

false

4,233

r

calculatePosteriors.R

setGeneric("calculatePosteriors", function(object,...) standardGeneric("calculatePosteriors")) #' Calculate Posterior Probabilities for Infered Parameters #' #' Uses numeric methods to estimate posteriors for the infered parameters \code{alpha} (synthesis rate) and \code{beta} (degredation rate). #' Currently uses a flat prior for both alpha and beta as a default. This function is written such that non-flat priors may be used in the future. #' @param object A \linkS4class{basicKineticModel} object #' @param alphaRange Scale factors used to calculate the upper and lower bounds of the parameter range explored for \code{alpha}. These scale factors will #' be applied to the infered value of \code{alpha}. Must be defined as \code{c(lower,upper)}. #' @param betaRange Scale factors used to calculate the upper and lower bounds of the parameter range explored for \code{beta}. These scale factors will #' be applied to the infered value of \code{beta}. Must be defined as \code{c(lower,upper)}. #' @param paramSpaceSize The total size of parameter space to numerically integrate over. Half of the parameter space will be given to each parameter. #' @param logProbAlpha A function that returns the log probability for a given value of \code{alpha} #' @param lobProbBeta A function that returns the log probability for a given value of \code{beta} #' @param dispByGene Boolean controlling the expected nature of the \code{dispersionModel}. See \code{dispersionModel} description in \code{\link{inferParameters}} #' for more details. #' #' @name calculatePosteriors #' @include class-basicKineticModel.R llFactory.R logSumExp.R #' @examples #' EXAMPLE HERE #' @export setMethod("calculatePosteriors",signature(object="basicKineticModel"), function(object,alphaRange=numeric(2),betaRange=numeric(2),paramSpaceSize=10^4,dispByGene=T,logProbAlpha=NULL,logProbBeta=NULL) { if(alphaRange[1]==0) { warning("No range specified for alpha, using default range of .25x to 2x alpha.") alphaRange=c(.25,2) }else if(alphaRange[1]>alphaRange[2]) stop(paste0("Alpha range specified incorrectly. Lower: ",alphaRange[1]," > Upper: ",alphaRange[2],". Upper must be greater than Lower")) else if(length(alphaRange)>2) stop("Too many arguments supplied for alpha range. Must be a vector of length 2 in the form (lower, upper).") if(betaRange[1]==0) { warning("No range specified for beta, using default range of .25x to 2x beta.") betaRange=c(.25,2) }else if(betaRange[1]>betaRange[2]) stop(paste0("Beta range specified incorrectly. Lower: ",betaRange[1]," > Upper: ",betaRange[2],". Upper must be greater than Lower")) else if(length(betaRange)>2) stop("Too many arguments supplied for beta range. Must be a vector of length 2 in the form (lower, upper).") # | betaRange[1]==0) # { # stop("Must enter ranges for both alpha and beta. They should be in the form c(lower,upper) where lower and upper are multipliers that are applied to the inferred parameter to calculate the bounds") # } if(is.null(logProbAlpha)) { logProbAlpha = function(x){rep(log(1/sqrt(paramSpaceSize)),length(x))} } if(is.null(logProbBeta)) { logProbBeta = function(x){rep(log(1/sqrt(paramSpaceSize)),length(x))} } #generate likelyhood esitmators for each gene logLH = lapply(X=1:nrow(object@inferedParams), FUN=llFactory, object=object,dispByGene=dispByGene) posteriors = lapply(X=1:nrow(object@inferedParams),object=object,logLH=logLH,FUN=function(x,object,logLH) { alpha = object@inferedParams[x,"alpha"] beta = object@inferedParams[x,"beta"] aMax = alphaRange[2]*alpha aMin = alphaRange[1]*alpha bMax = betaRange[2]*beta bMin = betaRange[1]*beta paramRange = expand.grid(seq(aMin,aMax,length.out = sqrt(paramSpaceSize)), seq(bMin,bMax,length.out = sqrt(paramSpaceSize))) numerator = apply(paramRange,1,function(y) logLH[[x]](y)) + logProbAlpha(paramRange[,1]) + logProbBeta(paramRange[,2]) marginal = logSumExp(numerator) posterior = exp(numerator-marginal) res=cbind(paramRange,posterior=posterior) colnames(res) = c("alpha","beta","posterior") return(res) }) object@posteriors = posteriors invisible(object) })

6dea293ef1db0cb3ceabf7d656cf98c732244b67

a2cfda897fad97d76a3b4c4be986eb63ef8046cc

/exercises/c5.R

84e4cd9fc00076a7f5f1d6cd9e988e38f2952fd4

[]

no_license

ssh352/islr_stats

1815ff8731b6d460216898d02bfc88f26ed16cee

9eae37ca99ca6762e67eb9c99e459ef01a9ee9ea

refs/heads/master

2021-09-10T12:26:59.658428

2018-03-26T09:04:30

null

0

null

UTF-8

R

false

4,471

r

c5.R

require(data.table) require(ISLR) ## Question 05 ---- dt <- data.table(Default) glm.fit <- glm(default ~ income+balance, data = dt, family = binomial) # (b) dt <- sample(dt) train <- dt[1:6667] test <- dt[6668:10000] glm.fit <- glm(default ~ income+balance, data = train, family = binomial) glm.prob <- data.table("prob" = predict(glm.fit, test, type = "response")) glm.prob[prob > 0.5, pred := "Yes"] glm.prob[is.na(pred),pred := "No"] table(glm.prob$pred, test$default) # Overall fraction of error in validation dataset: (12+72)/nrow(glm.prob) # 2.52% # c) train <- dt[1:8000] test <- dt[8001:10000] glm.fit <- glm(default ~ income+balance, data = train, family = binomial) glm.prob <- data.table("prob" = predict(glm.fit, test, type = "response")) glm.prob[prob > 0.5, pred := "Yes"] glm.prob[is.na(pred),pred := "No"] table(glm.prob$pred, test$default) # Overall fraction of error in validation dataset: (7+43)/nrow(glm.prob) # 2.5% # The error decreased a bit, but that is explained by having less variables # in the validation set, so that the model already know how to predict too # many values # d) dt[student=="Yes",st:=1] dt[is.na(st),st:=0] train <- dt[1:6667] test <- dt[6668:10000] glm.fit <- glm(default ~ income+balance+st, data = train, family = binomial) glm.prob <- data.table("prob" = predict(glm.fit, test, type = "response")) glm.prob[prob > 0.5, pred := "Yes"] glm.prob[is.na(pred),pred := "No"] table(glm.prob$pred, test$default) # Overall fraction of error in validation dataset: (16+71)/nrow(glm.prob) # 2.61% # The error increased a bit, meaning that student might not be that much # important when feeding our model ## Question 06 ---- dt <- data.table(Default) glm.fit <- glm(default ~ income+balance, data = dt, family = binomial) # a) summary(glm.fit) #std.error: # income 2.965e-6 # balance 2.274e-04 # b) boot.fn <- function(dt, index) return(coef(glm(default ~ income+balance, data = dt[index,], family = binomial))) dt <- data.table(Default) nrow(dt) boot.fn(dt, 1:900) require(boot) boot(data = dt, statistic = boot.fn, R = 1000) # Comparing the two methods, the income had the most different coefficient # for the different approaches ## QUESTION 07 ---- dt <- data.table(Weekly) glm.fit <- glm(Direction ~ Lag1 + Lag2, data = dt, family = binomial) plot(glm.fit) glm.fit2 <- glm(Direction ~ Lag1 + Lag2, data = dt[-1,], family = binomial) glm.prob <- data.table("prob" = predict(glm.fit2, dt[1,], type = "response")) glm.prob[prob > 0.5, pred := "Up"] glm.prob[is.na(pred),pred := "Down"] table(glm.prob$pred, dt[1,Direction]) # The prediction says that it is up, when actually it is down # d) In a loop n <- rep(0, nrow(dt)) for (i in 1:nrow(dt)){ glm.fit <- glm(Direction ~ Lag1 + Lag2, data = dt[-i,], family = binomial) glm.prob <- data.table("prob" = predict(glm.fit, dt[i,], type = "response")) glm.prob[,pred := ifelse(prob > 0.5, "Up", "Down")] if (glm.prob$pred != dt[i,Direction]) n[i] <- 1 } #LOOCV Error cat(paste0((round(sum(n)*100/length(n),3)),"%")) # A relatively high error, but the error of predicting all the # training dataset is already high, about 44.44% ## QUESTION 07 ---- set.seed(1) x = rnorm(100) y = x-2*x^2+rnorm(100) # a) n is 100 points and p is 1, only one predictor, the x # b) The relationship seems to be quadratic, from the scatterplot, # so we expect to have a better fit if we use # X and the square of X plot(x,y) # c) set.seed(1) x = rnorm(100) dt <- data.table(x,y = x-2*x^2+rnorm(100)) plot(dt) # i.) Y = B0+B1X+e cv.glm(dt, glm(y ~ x, data = dt))$delta # i.) Y = B0+B1X+B2X2+e cv.glm(dt, glm(y ~ x+I(x^2), data = dt))$delta # i.) Y = B0+B1X+B2X2+B3X3+e cv.glm(dt, glm(y ~ poly(x,3), data = dt))$delta # i.) Y = B0+B1X+B2X2+B3X3+B4X4+e cv.glm(dt, glm(y ~ poly(x,4), data = dt))$delta # d) set.seed(2) x = rnorm(100) dt <- data.table(x,y = x-2*x^2+rnorm(100)) plot(dt) # i.) Y = B0+B1X+e cv.glm(dt, glm(y ~ x, data = dt))$delta # i.) Y = B0+B1X+B2X2+e cv.glm(dt, glm(y ~ x+I(x^2), data = dt))$delta # i.) Y = B0+B1X+B2X2+B3X3+e cv.glm(dt, glm(y ~ poly(x,3), data = dt))$delta # i.) Y = B0+B1X+B2X2+B3X3+B4X4+e cv.glm(dt, glm(y ~ poly(x,4), data = dt))$delta # The results changed a bit, since the random values are # not coming from the same seed. The scatter plots show the # different patterns

275eab1e6f15eb9e828f0c471fded97ce275ac66

3e9052c3badc3b2363456142b53a552cf3bffdde

/R/create_equal_alignment.R

37a58c3e7c0d40273da0241c2de8e7d9eeb24d8d

[]

no_license

thijsjanzen/nodeSub

ad0a73acfc99241302d2c8307e90dcc4ac302306

a85bb1a6251a1b15cd6add635b721247b508f896

refs/heads/master

2023-05-25T17:28:39.099142

2023-05-15T08:21:56

180,762,207

1

2

null

2020-01-08T15:03:51

2019-04-11T09:45:02

R

UTF-8

R

false

3,324

r

create_equal_alignment.R

#' function create an alignment with identical information content #' @param input_tree phylogeny for which to generate alignment #' @param sub_rate substitution rate used in the original phylogeny #' @param alignment_result result of sim_normal, sim_linked or sim_unlinked #' @param sim_function function that accepts a tree, sequence length, #' rootsequence and substitution rate (in that order). Default is sim_normal #' @param verbose provide intermediate output #' @param node_time node time #' @param input_alignment_type was the input alignment simulated with a node #' substitution model or a normal substitution model? Used to calculate the #' twin mutation rate. Options are "nodesub" and "normal". #' @return list with four properties: 1) alignment: the alignment itself, #' 2) adjusted rate: the substitution rate used to obtain identical information #' content 3) total_accumulated_substitutions: the total number of #' substitutions accumulated. 4) total_node_substitutions: total number of #' substitutions accumulated on the nodes 5) total_branch_substitutions: total #' number of substitutions accumulated on the branches. #' @export create_equal_alignment <- function(input_tree, sub_rate, alignment_result, sim_function = NULL, verbose = FALSE, node_time = NULL, input_alignment_type = "nodesub") { num_emp_subs <- alignment_result$total_accumulated_substitutions adjusted_rate <- sub_rate + sub_rate * alignment_result$total_node_substitutions / alignment_result$total_branch_substitutions if (input_alignment_type == "normal") { if (is.null(node_time)) { stop("Node time needs to be provided") } total_node_sub <- node_time * 2 * input_tree$Nnode total_branch_time <- sum(input_tree$edge.length) frac <- 1 + total_node_sub / total_branch_time adjusted_rate <- sub_rate / frac } if (input_alignment_type == "fix_sub_rate") { adjusted_rate <- sub_rate } seqlen <- length(alignment_result$root_seq) if (is.null(sim_function)) { sim_function <- function(input_tree, seqlen, rootseq, rate) { sim_normal(x = input_tree, l = seqlen, rootseq = rootseq, rate = rate) } } proposed_alignment <- sim_function(input_tree, seqlen, alignment_result$root_seq, adjusted_rate) proposed_subs <- proposed_alignment$total_accumulated_substitutions while (proposed_subs != num_emp_subs) { proposed_alignment <- sim_function(input_tree, seqlen, alignment_result$root_seq, adjusted_rate) proposed_subs <- proposed_alignment$total_accumulated_substitutions if (verbose) cat(proposed_subs, " ", num_emp_subs, " ", sub_rate, " ", adjusted_rate, "\n") } proposed_alignment$adjusted_rate <- adjusted_rate return(proposed_alignment) }

c9bca76cdcbd46799921b9e678750402c51ac1c4

0cc863fed706b96df0c44afe7d466cff23228049

/man/suff_stat.Bernoulli.Rd

38246f9c8b56de0fabf29fbf79f6f7cdffc88198

[ "MIT" ]

permissive

alexpghayes/distributions3

80a96665b4dabe2300908d569cb74de3cc75b151

67d27df128c86d80fe0c903b5b2c8af1fb9b0643

refs/heads/main

2023-01-27T14:49:47.588553

2023-01-18T18:12:22

185,505,802

52

11

NOASSERTION

2023-01-18T18:12:24

2019-05-08T01:38:24

R

UTF-8

R

false

true

723

rd

suff_stat.Bernoulli.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Bernoulli.R \name{suff_stat.Bernoulli} \alias{suff_stat.Bernoulli} \title{Compute the sufficient statistics for a Bernoulli distribution from data} \usage{ \method{suff_stat}{Bernoulli}(d, x, ...) } \arguments{ \item{d}{A \code{Bernoulli} object.} \item{x}{A vector of zeroes and ones.} \item{...}{Unused.} } \value{ A named list of the sufficient statistics of the Bernoulli distribution: \itemize{ \item \code{successes}: The number of successful trials (\code{sum(x == 1)}) \item \code{failures}: The number of failed trials (\code{sum(x == 0)}). } } \description{ Compute the sufficient statistics for a Bernoulli distribution from data }

71621656d85ed2309815089d948c717e5fe7578d

6129a47af94390370862748e1cb00104309766d6

/Human_Cultural_Boundaries/R/Source_Plotting.R

eeca2634cc7d57f9190db141341edbf5304a2682

[]

no_license

NeuroBio/HCB_R_Package

ea06605d1420499044e0478b2719bca2be2e4bb3

a7458d1d6e4ce9a130b741e0b05ce7b17750b3c5

refs/heads/master

2023-08-28T04:35:33.451306

2021-11-08T11:07:07

191,992,585

0

null

UTF-8

R

false

13,633

r

Source_Plotting.R

#Plotting #THIS NEEDS TO BE CLEANED UP #' Get Groups #' #' Returns the territories descended from each seed. Includes detailed ancestory data. Ony works when Uproot and Death are FALSE. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @keywords Plotting #' @export #' GetGroups <- function(P, Data){ Groups <- list() for(i in seq_along(P$PopStart)){ G <- list() start <- P$PopStart[i] repeat{#given a starting place, what populations did it create? start <- which(Data$Populations[,1] %in% Data$Populations[start,2]) if(length(start)==0){ break() } G[[length(G)+1]] <- start } if(length(G)==0){ Terr <- NA Connect <- NA }else{ Terr <- sort(unlist(G)) Connect <- sapply(1:length(Terr), function(x) which(Data$Populations[,2] == Data$Populations[Terr[x],1])) } Groups[[i]] <- cbind(Terr,Connect) } return(Groups) } #' Bering Straight Plot #' #' Creates a plot that shows the Bring strait boundaries. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @param colors A vector of colors of length equal to the number of seed populations. #' @keywords Plotting #' @export # BeringStraitPlot <- function(P, Data, colors=NA){ if(!P$Bering){ stop("This plot assumes that the Berring Barriers were implemented.") } Groups <- GroupBySeed(P, Data) if(is.na(colors)[1]){ colors <- randomColor(length(Groups)) } par(mar=c(3,2.5,1,1), mgp=c(1.5,.5,0), mfrow=c(2,2), bg="grey10", fg="white") Sizes <- Data$Populations$SizeCurrent Sizes <- Sizes[-(which(Sizes==0))] hist(Sizes, xlab="Population Size", ylab="Number of Populations", col.axis="white", col.lab="white", col = "lightblue", border="lightblue4") PopulationPlot(P, Data, Groups, colors) PhonemePopulationFrequencyPlots(P, Data, Groups, colors, sort=TRUE) } #' Group By Seed #' #' Returns the territories descended from each seed. Works for all sims, but lacks ancestory data. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @keywords Plotting #' @export # GroupBySeed <- function(P, Data){ Groups <- vector("list") for(i in seq_along(P$PopStart)){ Groups[[i]] <- which(Data$Populations$SeedID == i) } return(Groups) } #' Snapshot Plot #' #' Shows which territories are populated and from which seed they decended. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @param colors A vector of colors, one for each seed. #' @keywords Plotting #' @export # SnapshotPlot<-function(P, Data, colors){ Groups <- GroupBySeed(P, Data) PopulationPlot(P, Groups, colors) } #' Population Plot #' #' #' @param P A list of parameters. #' @param groups Group structure of which territories were descended from what population seed. #' @param colors A vector of colors of length equal to the number of seed populations. #' @keywords Plotting #' @export # PopulationPlot <- function(P, Data, groups=NA, colors=NA){ if(is.na(groups)[1]){ if(P$Death || P$UpRoot){ groups <- GroupBySeed(P, Data) }else{ groups <- GetGroups(P, Data) for(i in seq_along(groups)){ groups[[i]] <- groups[[i]][,1] } } } if(is.na(colors)[1]){ colors <- randomColor(length(groups)) } plot(0, type="n", xlim=c(1, P$C), ylim=c(P$R,1), col.axis="white", font.axis=2) for(i in seq_along(groups)){ if(length(groups[[i]]) > 0){ Modtest <- groups[[i]]%%P$R points(ceiling(groups[[i]]/P$R), ifelse(Modtest==0,P$R,Modtest), col=colors[i], pch=19) } } if(P$Bering){ Pos <- GetBering(P) AddSegment(P, Pos$AsiaLowerRight, Pos$AsiaUpperRight) AddSegment(P, Pos$AsiaBeringCorner, Pos$NAmericanLowerRight) AddSegment(P, Pos$AsiaBeringCorner, Pos$BeringNAmericaCorner) AddSegment(P, Pos$NAmericanLowerEntry, Pos$NAmericanUpperRight, top=TRUE) } } #' Add Segment #' #' Creates lines to show the Bering Straight borders. #' @param P A list of parameters. #' @param a Start. #' @param b End. #' @param Data The Pre or Post output from an HBC simulation. #' @keywords Plotting #' @export # AddSegment <- function(P, a, b, top=FALSE){ Modtest1 <- a%%P$R Modtest2 <- b%%P$R if(top){ segments(ceiling(a/P$R)+.5, ifelse(Modtest1==0,P$R,Modtest1)+.5, ceiling(b/P$R)+.5, Modtest2-.5, col="white", lwd=2) }else{ segments(ceiling(a/P$R)+.5, ifelse(Modtest1==0,P$R,Modtest1)+.5, ceiling(b/P$R)+.5, ifelse(Modtest2==0,P$R,Modtest2)+.5, col="white", lwd=2) } } #' Migration Plot #' #' Shows the expansion of populations from the seed population. Ony works when Uproot and Death are FALSE. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @param groups Group structure of which territories were descended from what population seed. #' @param colors A vector of colors of length equal to the number of seed populations. #' @keywords Plotting #' @export # MigrationPlot <- function(P, Data, groups=NA, colors=NA){ if(is.na(groups)[1]){ groups <- GetGroups(P, Data) } if(is.na(colors)[1]){ colors <- distinctColorPalette(length(groups)) } par(mar=c(2,2,1,1), mgp=c(1.5,.5,0), bg="grey10", fg="white") plot(0, type="n", xlim=c(1, P$C), ylim=c(P$R,1), col.axis="white", font.axis=2) for(i in seq_along(groups)){ Modtest1 <- groups[[i]][,"Terr"]%%P$R Modtest2 <- groups[[i]][,"Connect"]%%P$R arrows(ceiling(groups[[i]][,"Terr"]/P$R), ifelse(Modtest1==0,P$R,Modtest1), ceiling(groups[[i]][,"Connect"]/P$R), ifelse(Modtest2==0,P$R,Modtest2), col=colors[[i]], angle=15, length=.1, code=1, lwd=2) } } #' Phoneme Frequency Plots #' #' Shows how common each phonemes is in the simulation color coded by population. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @param groups Group structure of which territories were descended from what population seed. #' @param colors A vector of colors of length equal to the number of seed populations. #' @keywords Plotting #' @export # PhonemeFrequencyPlots <- function(P, Data, groups=NA, colors=NA){ if(is.na(groups)[1]){ if(P$Death || P$UpRoot){ groups <- GroupBySeed(P, Data) }else{ groups <- GetGroups(P, Data) for(i in seq_along(groups)){ groups[[i]] <- groups[[i]][,1] } } } if(is.na(colors)[1]){ colors <- randomColor(length(groups)) } par(mar=c(3,3,1,1)) plot(colSums(Data$Languages), type="l", col="White",col.axis="white", col.lab="white", xlab="Phonemes Ordered Most to Least Common", ylab=paste0("Number of Populations (Total=",nrow(Data$Languages),")")) for(i in seq_along(groups)){ Sums <- colSums(Data$Languages[groups[[i]],]) plot(Sums, type="l", col=colors[i],col.axis="white",col.lab="white", xlab="Phonemes Ordered Most to Least Common", ylab=paste0("Number of Populations (Total=",length(groups[[i]]),")")) points(which(Data$Languages[P$PopStart[i],]==1), rep(max(Sums),sum(Data$Languages[P$PopStart[i],])), col="White", cex=.6,pch=19) #PhoPerSeed[i,] <- Sums #print(summary(rowSums(Data$Languages[groups[[i]][,1],]))) #Sums <- colSums(Data$Languages[groups[[i]][,1],]) #segments(1:(728-1), Sums[1:(728-1)], 2:(728), Sums[2:(728)],col=colorSet[i]) } } #' Phoneme Population Frequency Plots #' #' Shows how common each phonemes is in the simulation color coded by population. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @param groups Group structure of which territories were descended from what population seed. #' @param colors A vector of colors of length equal to the number of seed populations.#' @param sort Whether to sort the data from most to least frequent phoneme. #' @keywords Plotting #' @export # PhonemePopulationFrequencyPlots <- function(P, Data, groups=NA, colors=NA, sort=TRUE){ if(is.na(groups)[1]){ if(P$Death || P$UpRoot){ groups <- GroupBySeed(P, Data) }else{ groups <- GetGroups(P, Data) } } if(is.na(colors)[1]){ colors <- randomColor(length(groups)) } #print(colorSet) if(sort){ Data$Languages <- Data$Languages[,order(colSums(Data$Languages), decreasing = TRUE)] } #print(Data$Languages[1:10, 1:10]) PhoPerSeed <- matrix(0,nrow=length(groups),ncol=P$nPhon) for(i in seq_along(groups)){ if(P$Death || P$UpRoot){ Choices <- Data$Languages[groups[[i]],] }else{ Choices <- Data$Languages[groups[[i]][,1],] } if(class(Choices) == "numeric"){ PhoPerSeed[i,] <- Choices }else{ PhoPerSeed[i,] <- colSums(Choices) } } #print(PhoPerSeed) plot(0,type = "n", xlim=c(0,P$nPhon), ylim=c(0,max(colSums(Data$Languages))), col.axis="white",col.lab="white", ylab="Number of populations", xlab="Phonemes, Ordered Common to Rare", font.lab=2, cex.lab=1, font.axis=2) for(i in 1:P$nPhon){ rect(i-1,0,i,rev(cumsum(PhoPerSeed[,i])), col=rev(colors),border = NA) } } #' Get Colors #' #' Creates a color gradient. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @param colors A vector of beginning and ending colors for a gradient. #' @keywords Plotting #' @export # GetColorDistribution <- function(P, Data, i, colors=c('coral1','coral4')){ Order <- list() j <- 1 Order[[1]] <- P$PopStart[i] while(length(Order[[j]])!=0){ Order[[j+1]] <- which(Data$Populations$Founder %in% Data$Populations$ID[Order[[j]]]) j <- j+1 } Order[[j]] <- NULL Order[[1]] <- NULL Pal <- colorRampPalette(colors) names(Order) <- Pal(length(Order)) Order <- setNames(unlist(Order, use.names=F),rep(names(Order), lengths(Order))) return(names(sort(Order))) } #Gets the distance between two points #' Phoneme Mantel #' #' Performs both a Hamming and Jaccard Mantel test. #' @param P A list of parameters. #' @param Data The Pre or Post output from an HBC simulation. #' @param repeats How many times to repeat the analysis. #' @keywords Plotting #' @export # PhonemeMantel <- function(P, Data, repeats=100){ time <- proc.time() #get distances; Geo, Jac, and Ham DistMat <- MakeDistanceMap(P) Jac <- distance(Data$Languages, method="jaccard") Ham <- distance(Data$Languages, method="manhattan") par(mfrow=c(1,2)) image(Jac) image(Ham) #get into right form FinalGeo <- as.dist(DistMat) FinalJac <- as.dist(Jac) FinalHam <- as.dist(Ham) #perform tests print(mantel.rtest(FinalGeo, FinalJac, repeats)) print(mantel.rtest(FinalGeo, FinalHam, repeats)) proc.time()-time } #' Make Distance Map #' #' Creates a distance map based on the euclidian diatcnes between territories. #' @param P A list of parameters. #' @keywords Plotting #' @export # MakeDistanceMap <- function(P){ DistMat <- matrix(0, nrow=2000,ncol=2000) for(i in 1:2000){ for(j in 1:2000){ DistMat[i,j]<-GetDist(P,i,j) } } return(DistMat) } #' Get Distance #' #' Get the euclidian distance between two territories. #' @param P A list of parameters. #' @param point1 One territory location. #' @param point2 Another territory location. #' @keywords Plotting #' @export # GetDist <- function(P, point1, point2){ XY <- GetXYCoords(P, rbind(point1, point2)) A <- XY[1,1]-XY[2,1] B <- XY[1,2]-XY[2,2] return(sqrt(A^2+B^2)) } #' Get XY Coordinates #' #' COnverts territory numbers into X,Y corrdinates. #' @param P A list of parameters. #' @param territories A vector of territory indicies to convert into X,Y coordinates. #' @keywords Plotting #' @export # GetXYCoords <- function(P, territories){ Xs <- integer(length(territories)) Ys <- integer(length(territories)) for(i in 1:length(territories)){ Ys[i] <- territories[i]%%P$R if(Ys[i] == 0){ Xs[i] <- territories[i]%/%P$R }else{ Xs[i] <- territories[i]%/%P$R+1 } if(Ys[i]==0){ Ys[i] <- P$R } } return(cbind(Xs,Ys)) } #' Get Bering Strait Coordinates #' #' Returns the hardcoded locations of the Bering Strait boundaries. #' @keywords Plotting #' @export # GetBeringCoords <- function(P){ Pos <- GetBering(P) #vertical boundaries are imposed rightward/increasing X index Vert<-GetXYCoords(P, c(Pos$AsiaLowerRight:Pos$AsiaUpperRight, Pos$AsiaBeringCorner:Pos$BeringNAmericaCorner, Pos$NAmericanLowerEntry:Pos$NAmericanUpperRight)) Vert[,1] <- Vert[,1]+.5 #horizontal boundaries are imposed downward/increasing Y index Horz <- GetXYCoords(P, c(seq(Pos$AsiaUpperRight,Pos$AsiaBeringCorner, by=-P$R)-1, seq(Pos$AsiaUpperRight, Pos$NAmericanLowerRight, by=P$R)-1)) Horz[,2] <- Horz[,2]+.5 return(rbind(Vert,Horz)) } #' Save Data #' #' Saves just the language data from the simulation to .csv files. #' @keywords Plotting #' @export # SaveData <- function(Data, filename){ if("NoHorizontal" %in% names(Data)){ write.csv(Data$NoHorizontal$Languages, paste0(filename, "-pre.csv")) write.csv(Data$Horizontal$Languages, paste0(filename, "-post.csv")) write.csv(Data$NoHorizontal$Populations$SeedID, paste0(filename, "-seeds.csv")) } if("Alternated" %in% names(Data)){ write.csv(Data$Alternated$Languages, paste0(filename, "-alt.csv")) write.csv(Data$Alternated$Populations$SeedID, paste0(filename, "-seeds.csv")) } }

1b5b25a413380dd6d4c536c6fc4edb414704b564

3f36e3afc25870cf6e9429de4a5b0604d52dc03a

/inst/shiny/VisualisingTrajectories/app.R

442568430b2e2dc7173b8a07ff434821469bfd62

[]

no_license

Patricklomp/VisualisingHealthTrajectories

4077a62b7da7b92ad2c7aa99a918aaf15585788e

98e69c50d354a693f0e9e8a3d76c81e3e5088a7a

refs/heads/master

2023-05-31T16:21:50.435675

2021-06-04T07:43:42

317,466,951

0

null

UTF-8

R

false

61

r

app.R

#Starts shiny application shinyApp(ui = ui, server = server)

a0fac1bf948521d541cfb800c6b277116db27c10

1b2646afcc7c602243e1025c6653d91a8aa313e9

/R/resample.CoxBoost.R

596103ebeccd8b5179debe6e63698e884beea5a2

[]

no_license

kaixinhuaihuai/CoxBoost

b171c8d28b0ff7f302acf3310845f53ae40b04e4

e7fe9d6a30a8e77d9516539f2c21d377c83ea8f2

refs/heads/master

2023-01-15T12:08:06.490468

2020-11-18T22:06:48

null

0

null

UTF-8

R

false

4,491

r

resample.CoxBoost.R

resample.CoxBoost<- function(time,status,x,rep=100,maxstepno=200,multicore=TRUE, mix.list=c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99), stratum,stratnotinfocus=0, penalty=sum(status)*(1/0.02-1),criterion="hscore",unpen.index=NULL) { rep <- rep trainind <- list() for (i in 1:rep){ trainind[[length(trainind)+1]] <- sample(1:nrow(x),round(nrow(x)*0.632),replace = F) } out <- list() for (iter in 1:rep) { message('iter=', iter) outbeta<-c() outCV.opt<-c() for (mix.prop in mix.list) { print(mix.prop) obs.weights <- rep(1,length(status)) case.weights <- ifelse(stratum == stratnotinfocus,mix.prop,1) obs.weights <- case.weights/sum(case.weights)*length(case.weights) set.seed(x[1,5]*100+time[19]*10) CV <- cv.CoxBoost(time=time[trainind[[iter]]],status=status[trainind[[iter]]],x=x[trainind[[iter]],], stratum=stratum[trainind[[iter]]],unpen.index=unpen.index, coupled.strata = FALSE,weights=obs.weights[trainind[[iter]]], maxstepno=maxstepno,K=10,penalty=penalty, standardize=TRUE,trace=TRUE, multicore=multicore,criterion=criterion) set.seed(x[1,5]*100+time[19]*10) CB <- CoxBoost(time=time[trainind[[iter]]],status=status[trainind[[iter]]],x=x[trainind[[iter]],], stratum=stratum[trainind[[iter]]],unpen.index=unpen.index, coupled.strata = FALSE,weights=obs.weights[trainind[[iter]]], stepsize.factor=1,stepno=CV$optimal.step,penalty=penalty, standardize=TRUE,trace=TRUE,criterion=criterion) outbeta<-c(outbeta,CB$model[[1]][[5]][nrow(CB$model[[1]][[5]]),] ) outCV.opt <- c(outCV.opt,CV$optimal.step) } out[[iter]] <- list(beta=outbeta,CV.opt=outCV.opt) } out } stabtrajec<-function(RIF,mix.list=c(0.001,0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99) ,plotmix=c(0.001,0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99) ,my.colors=grDevices::gray(seq(.99,0,len=10)), yupperlim=1,huge=0.6,lowerRIFlimit=0.6,legendval=4.5) { RIF1<-c() for (i in 1: length(RIF)){RIF1<-c(RIF1,RIF[[i]][[1]])} freqmat <-matrix(apply(matrix(unlist(RIF1), ncol=length(RIF))!=0,1,mean), ncol=length(mix.list)) sel.mask <- apply(freqmat,1,function(arg) any(arg >= lowerRIFlimit & arg < 1.1)) w5<-c(1:length(which(sel.mask==T))) jitmat<-cbind(w5-0.28,w5-0.21,w5-0.14,w5-0.07,w5,w5+0.07,w5+0.14,w5+0.21,w5+0.28,w5+0.35) colnames(jitmat)<-mix.list colnames(freqmat)<-mix.list freqmat<-freqmat[,which(colnames(freqmat)%in%paste("",plotmix,sep=""))] jitmat<-jitmat[,which(colnames(jitmat)%in%paste("",plotmix,sep=""))] plot(0,xlim=c(0.5,length(which(sel.mask==T))+legendval),ylim=c(0,yupperlim),type="n",main=" ", xlab=" ",ylab="resampling inclusion frequency", las=T,xaxt = "n") axis(1, at = c(1:length(which(sel.mask==T))),labels =rownames(freqmat[sel.mask,]),cex.axis=huge) for (i in 1:length(plotmix)){ points(jitmat[,i],freqmat[,i][sel.mask],col=my.colors[i],type = 'p',pch=16)} for (i in 1:length(plotmix)){ points(jitmat[,i],freqmat[,i][sel.mask],col=1,type = 'p')} for (i in c(1:length(which(sel.mask==T)))){lines(jitmat[i,],freqmat[sel.mask,][i,],col=1)} for (i in 1:length(plotmix)) {legend("topright",paste("w=",plotmix, sep=""),pch=16,col=my.colors,bty="n")} for (i in 1:length(plotmix)) {legend("topright",paste("w=",plotmix, sep=""),pch=1,col=1,bty="n")} abline(v=c(1:length(which(sel.mask==T))), col=grDevices::gray(0.7)) } weightfreqmap<-function(RIF,mix.list=c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99) ,plotmix=c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99) ,lowerRIFlimit=0.5,method="complete") { RIF1<-c() for (i in 1: length(RIF)){RIF1<-c(RIF1,RIF[[i]][[1]])} freqmat <-matrix(apply(matrix(unlist(RIF1), ncol=length(RIF))!=0,1,mean), ncol=length(mix.list)) colnames(freqmat)<-mix.list sel.indz<-apply(freqmat,1,function(arg) any(arg >= lowerRIFlimit & arg < 1.1)) heatcol<-grDevices::gray(seq(0,.9,len=100)) heatmap(freqmat[sel.indz,which(colnames(freqmat)%in%paste("",plotmix,sep=""))], col=heatcol,hclustfun = function(x) hclust(x,method=method), distfun=function(x) as.dist((1-cor(t(x)))/2), xlab ="relative weights", scale="row",Colv=NA) }

c24cd8d4b8af37410765953d0822d8b811ad8366

f2643256c6611d7de0db96d162f594388c2c2c50

/analyses/Trial 2/satstudy_recruitment.R

35f703f9a1c90ddcee69aade70c5e66b3e285789

[]

no_license

raubreywhite/trial_dofiles

e06a5b3b39e9195eda79dd33856d67c918ec4053

eface3b83b107cf7e621b3c654e65b5cbd45b711

refs/heads/master

2022-06-14T03:26:17.492945

2022-06-02T07:27:04

114,857,557

1

0

null

UTF-8

R

false

18,408

r

satstudy_recruitment.R

### not to be run on server ### # set working directory setwd("C:/Users/Mervett_Isbeih/sat_study") getwd() #setting up folders FOLDER_SAT_RESULTS <<-file.path("C:/Users/Mervett_Isbeih/sat_study/sat_results") FOLDER_SAT_DATA_CLEAN <<-file.path("C:/Users/Mervett_Isbeih/sat_study/sat_data_clean") #idenfifying packages we want desiredPackages <- c("stringr", "lubridate", "data.table", "bit64", "readxl", "openxlsx", "bit64", "haven", "lubridate", "ggplot2", "irr", "rel", "gridExtra", "openssl", "fmsb", "ICC", "arabicStemR", "lme4", "fs", "fancycut" ) for(i in desiredPackages) if(!i %in% rownames(installed.packages())) install.packages(i) library(data.table) library(readxl) #load in key data set skey<-fread("C:/Users/Mervett_Isbeih/sat_study/sat_data/T2_key.csv", encoding = "UTF-8") # Load in data for this week satresults<-fread("C:/Users/Mervett_Isbeih/sat_study/sat_data/raw.csv", encoding="UTF-8") nrow(satresults) sat <- merge(skey,satresults, by="cliniccode", all.y = TRUE) nrow(sat) sat <- setDT(sat) nrow(sat) ########### cleaning variales ########### ## adjusting variable structures and some basic cleaning setnames(sat,"cliniccode", "clustercode") # yes or no questions sat[, q9a:=as.logical(NA)] sat[q9=="no", q9a:=FALSE] sat[q9=="yes", q9a:=TRUE] setnames(sat,"q9a","leavehome") #sat[,leavehome:=q9a] sat[,q10a:=as.logical(NA)] sat[q10=="no", q10a:=FALSE] sat[q10=="yes", q10a:=TRUE] setnames(sat,"q10a","primipreg") #sat[,q10a:=primipreg] setnames(sat,"q11","bookgAmonth") #sat[bookgAmonth:=q11] sat[, q12a:=as.logical(NA)] sat[q12=="no", q12a:=FALSE] sat[q12=="yes", q12a:=TRUE] setnames(sat,"q12a","usother") #sat[,usother:=q12a] sat[, q13a:=as.logical(NA)] sat[q13=="no", q13a:=FALSE] sat[q13=="yes", q13a:=TRUE] setnames(sat,"q13a","ancother") #sat[,ancother:=q13a] sat[, q14a:=as.logical(NA)] sat[q14=="no", q14a:=FALSE] sat[q14=="yes", q14a:=TRUE] setnames(sat,"q14a","refHR") #sat[,refHR:=q14a] vars <- c("q15", "q16", "q17", "q18", "q19", "q20", "q21", "q22", "q23", "q24", "q25", "q26", "q27", "q28", "q29", "q30", "q31", "q32", "q33", "q34", "q35", "q36", "q37", "q38", "q39", "q40") # ident variables sat[,T2:=as.logical(NA)] sat[ident_TRIAL_2=="Y", T2:=TRUE] sat[,T3:=as.logical(NA)] sat[ident_TRIAL_3=="Y", T3:=TRUE] sat[,T2T3:=as.logical(NA)] sat[ident_TRIAL_2_and_3=="Y", T2T3:=TRUE] sat[,T2T3control:=as.logical(NA)] sat[ident_TRIAL_2_3_Control=="Y", T2T3control:=TRUE] ### outliers ### xtabs(~sat$educyears, addNA=TRUE) xtabs(~sat$district, addNA=T) ### cleaning day end via time end ### sat[,dayend:=timeend] sat[,dayend:=stringr::str_remove_all(as.character(dayend)," [0-9][0-9]:[0-9][0-9]:[0-9][0-9]$")] sat[,dayend:=stringr::str_remove_all(as.character(dayend)," [0-9][0-9]:[0-9][0-9]$")] sat[,dayend:=stringr::str_remove_all(as.character(dayend)," [0-9][0-9]$")] sat[,dayend:=stringr::str_remove_all(as.character(dayend)," [0-9]:[0-9][0-9]$")] library(lubridate) sat[,dayend:=mdy(dayend)] ### cleaning end time ### sat[,endtime:=timeend] unique(sat$endtime) sat[,endtime:=stringr::str_remove_all(endtime,"^[0-9]/[0-9][0-9]/[0-9][0-9][0-9][0-9] ")] sat[,endtime:=stringr::str_remove_all(endtime,"^[0-9]/[0-9]/[0-9][0-9][0-9][0-9] ")] unique(sat$endtime) ### age categories ### # need to calculate age first # change birthyear to birthdate and subtract from todays date to get years unique(sat$birthyear) sat[,birthyearDate:=lubridate::ymd(birthyear, truncated = 2L)] unique(sat$birthyearDate) sat[,age:=floor(as.numeric(difftime(lubridate::today(),birthyearDate, units="days")/365.25))] unique(sat$age) sat[,agecat:=cut(age, breaks=c(0,20,24,29,34,39,100), include.lowest=T)] xtabs(~sat$agecat, addNA = T) ### educ categories ### sat[,educat:=cut(educyears, breaks=c(-1,0,6,12,16,25), include.lowest=T)] xtabs(~sat$educat) ### educ level ### sat[,edulevel:=as.character(NA)] sat[educat=="[-1,0]", edulevel:="None"] sat[educat=="(0,6]", edulevel:="Primary"] sat[educat=="(6,12]", edulevel:="Secondary"] sat[educat=="(12,16]", edulevel:="College or University"] sat[educat=="(16,25]", edulevel:="After college or university"] ### gestational age at booking ### (q11) # these are months in pregnancy, change to weeks sat[,bookgestage:=as.numeric(NA)] sat[!is.na(bookgAmonth),bookgestage:=4*bookgAmonth] # bookgAmonthcat sat[,bookgAmonthcat:=cut(bookgAmonth, breaks=c(0,3,6,10), include.lowest=T)] xtabs(~sat$bookgAmonthcat,addNA=T) # attendance setnames(sat,"q15","attend_allanc") setnames(sat,"q16","attend_testdiab") setnames(sat,"q17","attend_testanemia") setnames(sat,"q18","attend_testhtn") setnames(sat,"q19","attend_fg") # visit setnames(sat,"q20","visit_schedvisitconfid") setnames(sat,"q21","visit_waittime") setnames(sat,"q22","visit_healthstaff") setnames(sat,"q23","visit_testpurpose") setnames(sat,"q24","visit_testgA") setnames(sat,"q25","visit_recommend") setnames(sat,"q26","visit_returnnextpreg") setnames(sat,"q27","visit_satisfaction") # worry setnames(sat,"q28","worry_housing") setnames(sat,"q29","worry_money") setnames(sat,"q30","worry_partner") setnames(sat,"q31","worry_family") setnames(sat,"q32","worry_ownhealth") setnames(sat,"q33","worry_otherhealth") setnames(sat,"q34","worry_employment") setnames(sat,"q35","worry_baby") setnames(sat,"q36","worry_stillbirth") setnames(sat,"q37","worry_hospital") setnames(sat,"q38","worry_internalexam") setnames(sat,"q39","worry_givingbirth") setnames(sat,"q40","worry_coping") ################ Anonymized Data Set ################ # Choose only first 4 if more than four sat <- sat[order(clustercode,dayend)] sat[eligibility=="agree" & withdraw!="yes",SampNum:=1:.N, by=.(clustercode)] # Make a second variable to include only first four women sat[,instudy:=as.logical(NA)] sat[eligibility=="agree" & withdraw!="yes" & SampNum<=4,instudy:=TRUE] sat[eligibility=="agree" & withdraw!="yes" & SampNum>4, instudy:= FALSE] ## need to anonymize, code different arms (control and not control), and rename variables sat[,exposure:=as.character(NA)] sat[T2T3control==T, exposure:="A"] sat[T2==T|T3==T, exposure:="B"] xtabs(~sat[eligibility=="agree" & withdraw!="yes"]$exposure=="B", addNA=T) nocalls<-sat[withdraw!="yes",.(N=.N, Control=sum(exposure=="A"), Intervention=sum(exposure=="B")), keyby=.(eligibility)] # collector code sat[,collectorcode:=as.character(NA)] sat[collector=="naila", collectorcode:="A"] sat[collector=="entisar", collectorcode:="B"] sat[collector=="khadija", collectorcode:="C"] sat[collector=="najah", collectorcode:="D"] ### vars for anonymization data set varskeep <- c("SampNum", "instudy", "clustercode", "exposure", "collectorcode", "clinicsize", "timestarted", "timeend", "dayend", "endtime", "samplnum", "eligibility", "herphone", "district", "agecat", "educat", "edulevel", "leavehome", "primipreg", "bookgAmonth", "bookgAmonthcat", "usother", "ancother", "refHR", "attend_allanc", "attend_testdiab", "attend_testanemia", "attend_testhtn", "attend_fg", "visit_schedvisitconfid", "visit_waittime", "visit_healthstaff", "visit_testpurpose", "visit_testgA", "visit_recommend", "visit_returnnextpreg", "visit_satisfaction", "worry_housing", "worry_money", "worry_partner", "worry_family", "worry_ownhealth", "worry_otherhealth", "worry_employment", "worry_baby", "worry_stillbirth", "worry_hospital", "worry_internalexam", "worry_givingbirth", "worry_coping", "callbackanothertime", "callended_1", "callended_2", "callended_3", "withdraw") satKeep <- sat[,varskeep, with=F] satKeep <- setDT(satKeep) nrow(satKeep) openxlsx::write.xlsx(satKeep, file.path(FOLDER_SAT_DATA_CLEAN, sprintf("Sat_data_clean_%s.xlsx",lubridate::today()))) background <- c("agecat", "edulevel", "bookgAmonthcat", "leavehome", "primipreg", "refHR", "usother", "ancother") smallD<-satKeep[instudy==T,c("exposure",background), with=F] long <- melt.data.table(smallD, id.vars=c("exposure"),variable.factor = F) uglytable <- long[, .( N=.N, control=sum(exposure=="A"), intervention=sum(exposure=="B")), keyby=.( variable,value)] openxlsx::write.xlsx(uglytable, file.path( FOLDER_SAT_RESULTS, "freqtabs", sprintf("Background_%s.xlsx", lubridate::today()))) ################ Primary outcome ################ primary <- names(satKeep)[stringr::str_detect(names(satKeep),"^worry_")] smallD<-satKeep[instudy==T,c("exposure",primary), with=F] long <- melt.data.table(smallD, id.vars=c("exposure"),variable.factor = F) uglytable <- long[, .( N=.N, mean=round(mean(value, na.rm=T),digits=2), sd=round(sd(value, na.rm=T),digits=2)), keyby=.(exposure,variable)] openxlsx::write.xlsx(uglytable, file.path( FOLDER_SAT_RESULTS, "freqtabs", sprintf("%s_Primary_outcomes(rounded).xlsx", lubridate::today()))) # confidence intervals #t.test(var1~exposure, data=long, conf.level=0.95) #t.test(var2~exposure, data=long, conf.level=0.95) #t.test(var3~exposure, data=long, conf.level=0.95) #variables_to_test <- c("worry_baby", "worry_coping", "worry_mondy") retval <- vector("list", length=length(primary)) for(i in seq_along(retval)){ var_of_interest <- primary[i] formula <- glue::glue("{var_of_interest} ~ exposure") fit <- t.test(as.formula(formula), data = satKeep, conf.level=0.95) ### extract results here #temp <- data.frame(conf_level=fit$conf.int, var = var_of_interest) temp <- data.frame(conf_level_l95=fit$conf.int[1], conf_level_u95=fit$conf.int[2], statistic=fit$statistic,var = var_of_interest) retval[[i]] <- temp } retval <- rbindlist(retval) openxlsx::write.xlsx(retval, file.path( FOLDER_SAT_RESULTS, "freqtabs", sprintf("%s_Primary_outcomes_Confidence_Intervals.xlsx", lubridate::today()))) ################ Frequency Tables ################ #### use satkeep for analysis # vars for frequency tables freqvars <- c("attend_allanc", "attend_testdiab", "attend_testanemia", "attend_testhtn", "attend_fg", "visit_schedvisitconfid", "visit_waittime", "visit_healthstaff", "visit_testpurpose", "visit_testgA", "visit_recommend", "visit_returnnextpreg", "visit_satisfaction", "worry_housing", "worry_money", "worry_partner", "worry_family", "worry_ownhealth", "worry_otherhealth", "worry_employment", "worry_baby", "worry_stillbirth", "worry_hospital", "worry_internalexam", "worry_givingbirth", "worry_coping") smallD<-sat[instudy==T,c("exposure", freqvars), with=F ] long <- melt.data.table(smallD, id.vars=c( "exposure" ),variable.factor = F) uglytable <- long[, .( not_NA=sum(!is.na(value)), value0=sum(value==0,na.rm=T), value1=sum(value==1, na.rm=TRUE), value2=sum(value==2, na.rm=T), value3=sum(value==3, na.rm=T), value4=sum(value==4, na.rm=T), value5=sum(value==5, na.rm=T), Missing=sum(is.na(value)) ), keyby=.( variable, exposure) ] openxlsx::write.xlsx(uglytable, file.path( FOLDER_SAT_RESULTS, "freqtabs", sprintf("frequencies_%s.xlsx", lubridate::today()))) ############ Completeness Report and Numbers For data extraction ############ #completeness report DQ <- sat[eligibility=="agree" & withdraw!="yes",.( N=.N, herphone=sum(herphone=="yes"|herphone=="no", na.rm=T), district=sum(!is.na(district)), birthyear=sum(!is.na(birthyear)), educyears=sum(!is.na(educyears)), #meanEdu=mean(q11, na.rm = T), notmissing_q9=sum(!is.na(q9a)), q9T=sum(q9a==T, na.rm=T), q9F=sum(q9a==F, na.rm=T), notmissing_q10=sum(!is.na(q10a)), q10T=sum(q10a==T, na.rm=T), q10F=sum(q10a==F, na.rm=T), notmissing_q11=sum(!is.na(q11)), notmissing_q12=sum(!is.na(q12a)), q12T=sum(q12a==T, na.rm=T), q13T=sum(q13a==T, na.rm=T), q13F=sum(q13a==F, na.rm=T), notmissing_q14=sum(!is.na(q14a)), q14T=sum(q14a==T, na.rm=T), notmissing_q15=sum(!is.na(q15)), notmissing_q16=sum(!is.na(q16)), notmissing_q17=sum(!is.na(q17)), notmissing_q18=sum(!is.na(q18)), notmissing_q19=sum(!is.na(q19)), notmissing_q20=sum(!is.na(q20)), notmissing_q21=sum(!is.na(q21)), notmissing_q22=sum(!is.na(q22)), notmissing_q23=sum(!is.na(q23)), notmissing_q24=sum(!is.na(q24)), notmissing_q25=sum(!is.na(q25)), notmissing_q26=sum(!is.na(q26)), notmissing_q27=sum(!is.na(q27)), notmissing_q28=sum(!is.na(q28)), notmissing_q29=sum(!is.na(q29)), notmissing_q30=sum(!is.na(q30)), notmissing_q31=sum(!is.na(q31)), notmissing_q32=sum(!is.na(q32)), notmissing_q33=sum(!is.na(q33)), notmissing_q34=sum(!is.na(q34)), notmissing_q35=sum(!is.na(q35)), notmissing_q36=sum(!is.na(q36)), notmissing_q37=sum(!is.na(q37)), notmissing_q38=sum(!is.na(q38)), notmissing_q39=sum(!is.na(q39)), notmissing_q40=sum(!is.na(q40)))] openxlsx::write.xlsx(DQ,file.path(FOLDER_SAT_RESULTS, sprintf("%s_Completeness_Report.xlsx", lubridate::today()))) ############ Data Extraction Reports ############ # creating weekly report satcounts <- sat[,.(N=.N, agree=sum(eligibility=="agree" & withdraw!="yes", na.rm=T), disagree=sum(eligibility=="disagree", na.rm=T), cantcontact=sum(eligibility=="cantcontact", na.rm=T), ineligible=sum(eligibility=="ineligiblegA", na.rm=T), agreebutwithdraw=sum(eligibility=="agree" & withdraw=="yes")), keyby=.(weeknum,clustercode)] openxlsx::write.xlsx(satcounts,file.path(FOLDER_SAT_RESULTS, sprintf("%s_satcounts_clinic.xlsx", lubridate::today()))) satcountsTotal <- sat[,.(N=.N, agree=sum(eligibility=="agree" & withdraw!="yes", na.rm=T), disagree=sum(eligibility=="disagree", na.rm=T), cantcontact=sum(eligibility=="cantcontact", na.rm=T), ineligible=sum(eligibility=="ineligiblegA", na.rm=T), agreebutwithdraw=sum(eligibility=="agree" & withdraw=="yes")), keyby=.(clustercode)] openxlsx::write.xlsx(satcountsTotal,file.path(FOLDER_SAT_RESULTS, sprintf("%s_satcounts_clinicTotals.xlsx", lubridate::today()))) removed <- sat[withdraw=="yes" & eligibility=="agree",.(N=.N), keyby=.(weeknum,clustercode)] # ID clinics with less than 4 to send out sendout <- sat[withdraw!="yes" & eligibility=="agree",] sendout <-sendout[,.(N=.N), keyby=.(clustercode)] sendout <- sendout[is.na(N) | N<4,] openxlsx::write.xlsx(sendout,file.path(FOLDER_SAT_RESULTS, sprintf("%s_send out list.xlsx", lubridate::today()))) # results by data extractor collectornums <- sat[,.(N=.N, agree=sum(eligibility=="agree" & withdraw!="yes", na.rm=T), disagree=sum(eligibility=="disagree", na.rm=T), cantcontact=sum(eligibility=="cantcontact", na.rm=T), ineligible=sum(eligibility=="ineligiblegA", na.rm=T), withdraw=sum(withdraw=="yes", na.rm=T)), keyby=.(collector)]

3ea3b28206deb089a1cb8b58c02fa3ecf5e135c5

9e77527c480d453d64b317f1261f842f260efa6c

/code/06_variables.R

a68552a89c1d6ee3bf7b890752abc198e2dc92b3

[]

no_license

AnnikaErtel/CropDiversity_NutritionalSupply

e636663df614002d2c25c643317b8787388b9d81

34bdcd6582eca1984f031ecd84480d1d5b7e7aba

refs/heads/main

2023-06-27T03:45:18.198520

2021-07-28T09:41:15

360,645,197

1

0

null

UTF-8

R

false

20,243

r

06_variables.R

#####Affiliance #Annika Ertel #Universität Leipzig/ Institut für Geographie #Matrikelnummer: 3710313 #SKRIPT 6: Preparation of other variables ####Setting up#### setwd("~/data/MAS-group-share/04_personal/Annika/CropDiversity_NutritionalStability_new") rm(list=ls()) library(tidyverse) library(readxl) library(countrycode) ####Load data target_country<-read.csv("data/target_country.csv") fertilizer<-read.csv("data/variables/FAOSTAT_fertilizer.csv") irrigation<-read_csv("data/variables/Area_equ_Irrigation.csv") warfare<-read_xls("data/variables/warefare.xls") gdp<-read_csv("data/pop_data/worldbank/GDP_per_capita.csv") #final_ISO<-read_csv("data/final_ISO.csv") #final Country selection (done at the end of this skript) agriculture<-read.csv("data/FAOstat/FAO_Agriculture.csv") livestock<-read.csv("data/FAOstat/FAO_Livestock.csv") ####GDP PPP per capita#### # only take target country gdp<-gdp[gdp$`Country Code` %in% target_country$ISO,] #rename col colnames(gdp)[5:65]<-colnames(gdp)[5:65]%>% str_sub(start = 1L, end = 4L) # only 1961-2010 and and country code gdp<-gdp[,c(2,6:55)] #from wide to long gdp<-pivot_longer(gdp, cols = "1961":"2010", names_to = "Year", values_to = "gdp_per_capita_USD") #Assign time Periods in 10 year intervals gdp$timePeriod=0 gdp[gdp$Year%in%c(1961:1970),"timePeriod"] = 1961 gdp[gdp$Year%in%c(1971:1980),"timePeriod"] = 1971 gdp[gdp$Year%in%c(1981:1990),"timePeriod"] = 1981 gdp[gdp$Year%in%c(1991:2000),"timePeriod"] = 1991 gdp[gdp$Year%in%c(2001:2010),"timePeriod"] = 2001 # ".." to NA gdp[gdp==".."]<-NA sum(is.na(gdp)) #104 #count per country: which timePeriods are how insecure? gdp_count<-na.omit(gdp) gdp_count<-gdp_count%>% group_by(`Country Code`)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise(rowCount= n()) #calculate mean per decade gdp$gdp_per_capita_USD<-as.numeric(gdp$gdp_per_capita_USD) gdp<-gdp%>% group_by(`Country Code`)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("gdp_per_capita_USD"= mean(`gdp_per_capita_USD`, na.rm= T)) #rename countrycode for later join colnames(gdp)[1]<-"ISO" # I will add all data to final data even if there are some NA`s values # -> have to be adressed before analysis! ####Agriculture area#### # only take target country agriculture<-agriculture[agriculture$Area.Code %in% target_country$Area.Code,] # #### adapt region names (fao.code-> iso) agriculture$ISO <- countrycode(agriculture$`Area.Code`, 'fao', 'iso3c') # no important regions missing # only keep target year agriculture <- agriculture[which(agriculture$Year%in%1961:2010),] #only important info agriculture$Value<-names(agriculture)[names(agriculture)=="Value"]<-"agriculture_area_ha" #rename column agriculture<-agriculture[c("ISO", "Year", "agriculture_area_ha")] #Assign time Periods in 10 year intervals agriculture$timePeriod=0 agriculture[agriculture$Year%in%c(1961:1970),"timePeriod"] = 1961 agriculture[agriculture$Year%in%c(1971:1980),"timePeriod"] = 1971 agriculture[agriculture$Year%in%c(1981:1990),"timePeriod"] = 1981 agriculture[agriculture$Year%in%c(1991:2000),"timePeriod"] = 1991 agriculture[agriculture$Year%in%c(2001:2010),"timePeriod"] = 2001 #count per country agriculture_count<-agriculture%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise(rowCount= n()) #only those timePeriods were time series is complete agriculture_country<-agriculture_count%>%filter(rowCount==10) #find out which is complete agriculture_country<-agriculture_country[,c("ISO", "timePeriod")] #selects info for filter agriculture_merge<-merge(agriculture_country,agriculture) #takes only those timePeriods were series is complete agriculture_merge<-agriculture_merge[,c("ISO", "timePeriod","agriculture_area_ha")] #calculate mean per decade agriculture_mean<-agriculture_merge%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("agriculture_area_ha"= mean(`agriculture_area_ha`)) #### Livestock #### livestock<-read.csv("data/FAOstat/FAO_Livestock.csv") # only take target country livestock<-livestock[livestock$Area %in% target_country$Area,] # #### adapt region names (fao.code-> iso) livestock$ISO <- countrycode(livestock$`Area.Code`, 'fao', 'iso3c') # no important regions missing # only keep target year livestock <- livestock[which(livestock$Year%in%1961:2010),] #only important info livestock$Value<-names(livestock)[names(livestock)=="Value"]<-"Livestock_LSU" livestock<-livestock[c("ISO", "Year", "Livestock_LSU")] #summarize all livestocks per year and country (doesn't mind, different kinds of animals LSU references bodyweight) livestock<-livestock%>% group_by(ISO)%>% group_by(Year, .add = T)%>% dplyr::summarise(Livestock_LSU =sum(Livestock_LSU)) #Assign time Periods in 10 year intervals livestock$timePeriod=0 livestock[livestock$Year%in%c(1961:1970),"timePeriod"] = 1961 livestock[livestock$Year%in%c(1971:1980),"timePeriod"] = 1971 livestock[livestock$Year%in%c(1981:1990),"timePeriod"] = 1981 livestock[livestock$Year%in%c(1991:2000),"timePeriod"] = 1991 livestock[livestock$Year%in%c(2001:2010),"timePeriod"] = 2001 #count per country livestock_count<-livestock%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise(rowCount= n()) #only those timePeriods were time series is complete livestock_country<-livestock_count%>%filter(rowCount==10) #find out which is complete livestock_country<-livestock_country[,c("ISO", "timePeriod")] #selects info for filter livestock_merge<-merge(livestock_country,livestock) #takes only those timePeriods were series is complete livestock_merge<-livestock_merge[,c("ISO", "timePeriod","Livestock_LSU")] #calculate mean per decade livestock_mean<-livestock_merge%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("Livestock_LSU"= mean(`Livestock_LSU`)) #### Fertilizer data ##### # only take target country fertilizer<-fertilizer[fertilizer$Area %in% target_country$Area,] # #### adapt region names (fao.code-> iso) fertilizer$ISO <- countrycode(fertilizer$`Area.Code`, 'fao', 'iso3c') # no important regions missing # only keep target year fertilizer <- fertilizer[which(fertilizer$Year%in%1961:2010),] #only important info fertilizer$Value<-names(fertilizer)[names(fertilizer)=="Value"]<-"N_use/croparea_in_kg/ha" fertilizer<-fertilizer[c("ISO", "Year", "N_use/croparea_in_kg/ha")] #Assign time Periods in 10 year intervals fertilizer$timePeriod=0 fertilizer[fertilizer$Year%in%c(1961:1970),"timePeriod"] = 1961 fertilizer[fertilizer$Year%in%c(1971:1980),"timePeriod"] = 1971 fertilizer[fertilizer$Year%in%c(1981:1990),"timePeriod"] = 1981 fertilizer[fertilizer$Year%in%c(1991:2000),"timePeriod"] = 1991 fertilizer[fertilizer$Year%in%c(2001:2010),"timePeriod"] = 2001 #count per country fertilizer_count<-fertilizer%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise(rowCount= n()) #only those timePeriods were time series is complete fertilizer_country<-fertilizer_count%>%filter(rowCount==10) #find out which is complete fertilizer_country<-fertilizer_country[,c("ISO", "timePeriod")] #selects info for filter fertilizer_merge<-merge(fertilizer_country,fertilizer) #takes only those timePeriods were series is complete fertilizer_merge<-fertilizer_merge[,c("ISO", "timePeriod","N_use/croparea_in_kg/ha")] #calculate mean per decade fertilizer_mean<-fertilizer_merge%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("N_use/croparea_in_kg/ha"= mean(`N_use/croparea_in_kg/ha`)) ####Irrigation data##### #-> Land Area equipped for Irrigation # only take target country irrigation<-irrigation[irrigation$`Area Code` %in% target_country$Area.Code,] # adapt region names (fao.code-> iso) irrigation$ISO <- countrycode(irrigation$`Area Code`, 'fao', 'iso3c') # no important regions missing # only keep target year irrigation <- irrigation[which(irrigation$Year%in%1961:2010),] #only important info names(irrigation)[names(irrigation)=="Value"]<-"Land_area_equ._for-Irrigation_%" irrigation<-irrigation[c("ISO", "Year", "Land_area_equ._for-Irrigation_%")] #Assign time Periods in 10 year intervals irrigation$timePeriod=0 irrigation[irrigation$Year%in%c(1961:1970),"timePeriod"] = 1961 irrigation[irrigation$Year%in%c(1971:1980),"timePeriod"] = 1971 irrigation[irrigation$Year%in%c(1981:1990),"timePeriod"] = 1981 irrigation[irrigation$Year%in%c(1991:2000),"timePeriod"] = 1991 irrigation[irrigation$Year%in%c(2001:2010),"timePeriod"] = 2001 #count per country irrigation_count<-irrigation%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise(rowCount= n()) #only those timePeriods were time series is complete irrigation_country<-irrigation_count%>%filter(rowCount==10) #find out which is complete irrigation_country<-irrigation_country[,c("ISO", "timePeriod")] #selects info for filter irrigation_merge<-merge(irrigation_country,irrigation) #takes only those timePeriods were series is complete irrigation_merge<-irrigation_merge[,c("ISO", "timePeriod","Land_area_equ._for-Irrigation_%")] #calculate mean per decade irrigation_mean<-irrigation_merge%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("Land_area_equ._for-Irrigation_%"= mean(`Land_area_equ._for-Irrigation_%`)) ####Warefare#### #->number of armed conflicts! # adapt region names (country.name-> iso) warfare$ISO<- countrycode(warfare$country, 'country.name', 'iso3c') #von country zu iso #Some values were not matched unambiguously: Czechoslovakia, Germany East, Kosovo, Serbia and Montenegro, Vietnam South, Yemen North, Yemen South, Yugoslavia # only keep target year warfare <- warfare[which(warfare$year%in%1961:2010),] #target column (number of armed conflicts) warfare <- warfare[,c("ISO","country","year","actotal")] #remove na's and countries with no armed conflicts warfare <- warfare[!warfare$actotal==0,] # Check wich countries need to be prepared/translated warfare$ISO<- countrycode(warfare$country, 'country.name', 'iso3c') #Some values were not matched unambiguously: Czechoslovakia, Vietnam South, Yemen North, Yemen South, Yugoslavia # combine north and south yemen Yemen_N <- warfare[which(warfare$country=="Yemen North" & warfare$year%in%1961:1990),] Yemen_N$ISO <- "YEM" Yemen_S <- warfare[which(warfare$country=="Yemen South" & warfare$year%in%1961:1990),] Yemen_S$ISO <- "YEM" Yemen_comb <-merge(Yemen_N, Yemen_S, all= T) Yemen_comb <-Yemen_comb %>% group_by(year) %>% dplyr::summarise(actotal=sum(actotal)) Yemen_comb$ISO<-"YEM" #combine North and South Vietnam Viet_N <- warfare[which(warfare$country=="Vietnam North" & warfare$year%in%1961:1975),] Viet_N$ISO <- "VNM" Viet_S <- warfare[which(warfare$country=="Vietnam South" & warfare$year%in%1961:1975),] Viet_S$ISO <- "VNM" Viet_comb <-merge(Viet_N, Viet_S, all= T) Viet_comb <-Viet_comb %>% group_by(year) %>% dplyr::summarise(actotal=sum(actotal)) Viet_comb$ISO<-"VNM" #Czechoslovakia #prager frühling 1968 in territory of Czechoslovakia, which does not exist anymore. #-> I remove the conflict as this period is not assessed for that region #remove formaly devided countries and Czechoslovakia warfare<-rbind(warfare[-which(warfare$country%in% c("Yemen North", "Yemen South", "Vietnam North","Vietnam South","Czechoslovakia")),]) #remove country col (so that dfs merge) warfare$country<-NULL #add aggregate countries instead warfare<-rbind(warfare,Viet_comb,Yemen_comb) # only take target country warfare<-warfare[warfare$ISO %in% target_country$ISO,] #Assign time Periods in 10 year intervals warfare$timePeriod=0 warfare[warfare$year%in%c(1961:1970),"timePeriod"] = 1961 warfare[warfare$year%in%c(1971:1980),"timePeriod"] = 1971 warfare[warfare$year%in%c(1981:1990),"timePeriod"] = 1981 warfare[warfare$year%in%c(1991:2000),"timePeriod"] = 1991 warfare[warfare$year%in%c(2001:2010),"timePeriod"] = 2001 #calculate mean per decade warfare<-warfare%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("actotal"= mean(`actotal`)) names(warfare)[names(warfare) == 'year'] <- 'Year' #####final variable data together##### final_data<-fertilizer_mean%>% full_join(irrigation_mean)%>% full_join(warfare)%>% full_join(gdp)%>% full_join(agriculture_mean)%>% full_join(livestock_mean) #no conflicts whenever no reported final_data$actotal[is.na(final_data$actotal)]<-0 #remove where not all information is available n_distinct(final_data$ISO) #94 final_data<-na.omit(final_data) n_distinct(final_data$ISO) write.csv(final_data,"data/data_for_analysis/variables.csv") ####ALL DATA TOGETHER#### #### LOAD DATA #### diversity <- read_csv("data/data_for_analysis/diversity.csv") diversity$X1<-NULL fulfilled_nutr <- read_csv("data/data_for_analysis/fulfilled_nutr.csv") fulfilled_nutr$X1<-NULL variables <- read_csv("data/data_for_analysis/variables.csv") variables$X1<-NULL climate<- read_csv("data/data_for_analysis/egli_climate_national.csv") target_country <- read_csv("data/target_country.csv") country_info<- read_csv("data/spatial/UNSD - Methodology.csv") selfsuf_food_basket<-read_csv("data/data_for_analysis/selfsuf_food_basket.csv") selfsuf_food_basket$X1<-NULL # sd_fulfilled_nutr<-read_csv("data/data_for_analysis/fulfilled_nutr_sd.csv") # sd_fulfilled_nutr$X1<-NULL # #Not really used in analysis # self_suffiency<-read_csv("data/data_for_analysis/fulfilled_selfsuffiency.csv") # self_suffiency$X1<-NULL # self_suf_2<-read_csv("data/data_for_analysis/share_prod_in_fulfilled_sup_ISO.csv") # self_suf_2$X1<-NULL #### country information #### #####country grouping#### #grouping with Groupings the UN uses for SDG's final_ISO<-read_csv("data/final_ISO.csv") final_ISO$X1<-NULL country_info<- read_csv("data/spatial/UNSD - Methodology.csv") #only those countries which will be assessed country_info<-country_info%>% filter(`ISO-alpha3 Code`%in% target_country$ISO)%>% dplyr::select(c(`ISO-alpha3 Code`, `Developed / Developing Countries`, `Sub-region Name`)) #select cols #country grouping; regional groups: https://unstats.un.org/sdgs/indicators/regional-groups #assigning regions in extra col country_info$Region1<-ifelse(country_info$`Sub-region Name`=='Sub-Saharan Africa', "Sub_Saharan_Africa", "") country_info$Region2<-ifelse(country_info$`Sub-region Name`%in% c("Northern Africa", "Western Asia"), "Northern_Africa_and_Western_Asia", "") country_info$Region3<-ifelse(country_info$`Sub-region Name`%in% c("Central Asia", "Southern Asia"), "Central_and_Southern_Asia", "") country_info$Region4<-ifelse(country_info$`Sub-region Name`%in% c("Eastern Asia", "South-eastern Asia"), "Eastern_and_South_Eastern_Asia", "") country_info$Region5<-ifelse(country_info$`Sub-region Name` == "Latin America and the Caribbean", "Latin_America_and_the_Caribean", "") country_info$Region6<-ifelse(country_info$`Sub-region Name` == "Oceania", "Oceania", "") country_info$Region7<-ifelse(country_info$`Sub-region Name` == "Australia and New Zealand", "Australia_and_New_Zealand", "") country_info$Region8<-ifelse(country_info$`Sub-region Name` %in% c("Eastern Europe", "Northern Europe", "Southern Europe", "Western Europe", "Northern America"), "Europe_and_Northern_America", "") #to one col country_info$Region<-with(country_info, paste0(Region1, Region2, Region3 , Region4, Region5, Region6, Region7, Region8)) country_info<-country_info%>%dplyr::select(!4:11) #rename for later join names(country_info)[names(country_info) == 'ISO-alpha3 Code'] <- 'ISO' #### Preparation for Analysis#### #climate: decided for sd, to measure climatic stability # only take target country names(climate)[names(climate) == 'Level'] <- 'ISO' climate<-climate[climate$ISO %in% target_country$ISO,] # only keep target year climate <- climate[which(climate$Year%in%1961:2010),] #Assign time Periods in 10 year intervals climate$timePeriod=0 climate[climate$Year%in%c(1961:1970),"timePeriod"] = 1961 climate[climate$Year%in%c(1971:1980),"timePeriod"] = 1971 climate[climate$Year%in%c(1981:1990),"timePeriod"] = 1981 climate[climate$Year%in%c(1991:2000),"timePeriod"] = 1991 climate[climate$Year%in%c(2001:2010),"timePeriod"] = 2001 #calculate mean per decade sd_Temp<-climate%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("sd_Temp"= mean(`sdTemp`)) sd_Prec<-climate%>% group_by(ISO)%>% group_by(timePeriod, .add=T)%>% dplyr::summarise("sd_Prec"= mean(`sdPrec`)) #adjust naming selfsuf_food_basket colnames(selfsuf_food_basket) <- paste0( "self_suf_food_basket_", colnames(selfsuf_food_basket)) names(selfsuf_food_basket)[names(selfsuf_food_basket) == 'self_suf_food_basket_timePeriod'] <- 'timePeriod' names(selfsuf_food_basket)[names(selfsuf_food_basket) == 'self_suf_food_basket_ISO'] <- 'ISO' ##### one final timePeriod/country selection#### dat<-selfsuf_food_basket%>% full_join(fulfilled_nutr)%>% full_join(variables)%>% full_join(sd_Prec)%>% full_join(sd_Temp)%>% full_join(diversity)%>% #full_join(self_suffiency)%>% #full_join(self_suf_2)%>% full_join(country_info) #full_join(sd_fulfilled_nutr) n_distinct(dat$ISO) #94 dat$actotal[is.na(dat$actotal)] <- 0 dat<-na.omit(dat) n_distinct(dat$ISO) #65 #just those countrys where time series is complete! distinct_countries<-dat%>% group_by(ISO, .add= T)%>% dplyr::summarise(rowCount= n()) #count entries per country distinct_countries<-filter(distinct_countries,distinct_countries$rowCount==5) #only take, when time series complete countrycode(distinct_countries$ISO, "iso3c", "country.name") #shows which countries n_distinct(dat$ISO) #65 dat<-dat[dat$ISO %in% distinct_countries$ISO,] n_distinct(dat$ISO) #57 #write.csv(distinct_countries$ISO, "data/final_ISO.csv") # save final data selection! names(dat)[names(dat) == 'mean_invSimp_D_prod'] <- 'Simp_Div' #### FULL BASKET / NUTRITIOUS ADEQUACY #### # Criteria: demand of the lowest fulfilliation counts- as it shows which demographic percentage is fully nourished by supply # Accounts for: High supply for one nutrient does not substitute other requirements col_ful_nutr<-colnames(fulfilled_nutr)[-c(1:2)] # extract colnames all but timePeriod and ISO dat$full_basket<-apply(dat[,names(dat)[names(dat) %in% col_ful_nutr]], 1, FUN= min) # col_ful_nutr_sd<-colnames(sd_fulfilled_nutr)[-c(1:2)] # extract colnames # dat$sd_full_basket<-apply(dat[,names(dat)[names(dat) %in% col_ful_nutr_sd]], 1, FUN= "mean" ) #### SELF SUFFICIENT FOOD BASKET #### # Criteria: demand of the lowest fulfilliation (of nutrients) counts- as it shows which demographic percentage is depependent on trade over all nutrients # Accounts for: High supply for one nutrient does not substitute other requirements col_selfsuf_bas<-colnames(selfsuf_food_basket)[-c(1:2)] # extract colnames dat$selfsuf_food_basket<-apply(dat[,names(dat)[names(dat) %in% col_selfsuf_bas]], 1, FUN= min) # #### SELF SUFFIENCY ##### # dat$full_sufficiency<-apply(dat[,19:26], 1, FUN= min) # # pdf("Plots/boxplot_SelfSuffiency_with_time_Region.pdf") # ggplot(data = dat, aes(y = full_sufficiency, x = timePeriod, group= timePeriod, colour = Region))+ # structure of the graph # geom_boxplot() + # add the boxplot # geom_jitter() + # show the points distribution # geom_hline(yintercept=100, col= "red")+ # labs(x = '', y = "full_sufficiency [%]") + # add labels to the axis # theme_classic() # make it pretty # dev.off() # # #### SELF SUFFIENCY 2 #### # # dat$self_suf_2<-apply(dat[,27:34], 1, FUN= min) # pdf("Plots/boxplot_SelfSuffiency_2_with_time_Region.pdf") # ggplot(data = dat, aes(y = self_suf_2, x = timePeriod, group= timePeriod, colour = Region))+ # structure of the graph # geom_boxplot() + # add the boxplot # geom_jitter() + # show the points distribution # geom_hline(yintercept=100, col= "red")+ # labs(x = '', y = "share of national production in nutritional supply [%]") + # add labels to the axis # theme_classic() # make it pretty # dev.off() #### save csv#### write.csv(dat,"data/final_dataset.csv") #write.csv(dat,"data/final_dataset_inkl_sd.csv")

09f451959e2e9b2ed0115d4bb10150651f8d98e9

fbc244647eaf602abb4637c43641d3cdb1d178a8

/xts.processing.R

e4921a2a99bd443201441131990a3e97231bc551

[]

no_license

patchdynamics/ct-river-R

9111dcebe232db3f6aa8e9c4edbcbe60a468cecc

bf3d4384a225aa7db705d483cb61d16ce3bbcbe2

refs/heads/master

2021-05-04T11:33:15.144478

2016-09-24T01:43:15

50,894,715

0

null

UTF-8

R

false

773

r

xts.processing.R

# calculate warming series from xts yearly.hval = function(ts,col) { processed = ts[,col] minimum = min(ts[,col]) highest = minimum for(i in 1:nrow(ts)){ if(.indexyday(ts[i,col]) == 0) { highest = minimum } if(as.numeric(ts[i,col]) > as.numeric(highest)){ highest = ts[i,col] } processed[i] = highest } tshval = processed return(tshval) } # calculate cooling series from xts yearly.lval = function(ts,col) { processed = ts[,col] minimum = min(ts[,col]) highest = minimum for(i in nrow(ts):1){ if(.indexyday(ts[i, col]) == 0) { highest = minimum } if(as.numeric(ts[i,col]) > as.numeric(highest)){ highest = ts[i,col] } processed[i] = highest } tslval = processed return(tslval) }

1850b4ff27b028cd7da3bbabcd70c77554f11818

98614a140562bebd7a6dde6df7d3fec149159e0b

/R/HKCSS - service utilization 191207.r

0e71299651357cd1a58b1419398d9705cd74c6bd

[]

no_license

chenshuangzhou/programming101

07628a4fc797eb564531aa405b6e896c3c349da7

c09dbdb4777afa237a989fa67d9b7766079c821c

refs/heads/master

2020-03-29T16:30:36.184733

2020-03-08T10:53:57

150,116,457

2

0

null

UTF-8

R

false

20,977

r

HKCSS - service utilization 191207.r

### Notes # dementia, non-dementia # male, female # met/unmet needs # generations ### HKCSS data on Unmet Need of Caregivers ### library(xlsx); library(outreg); library(plyr); library(psych); library(stargazer); library(interplot); library(Hmisc); # library(VIM) # visualization of missing data # aggr(d,prop=F,numbers=T) # matrixplot(d) ############### add number of caregivers data = work = read.csv("C:/Users/chens/OneDrive/research/Projects/4 HKCSS/191216 HKCSS.csv",header=T,na.strings = "NA") # Office - Dell Inspiron 16 # data = read.csv("D:/OneDrive/research/Projects/4 HKCSS/190908 HKCSS (no missing).csv",header=T,na.strings = "NA") # Office - Dell Inspiron 16 ## Var names data$CMon=data$CGMon data$CF=data$B13 # CG frequency data$comb=data$B16f data$dr=data$C12 # dyadic relation data$fr=data$C10T # family relation # with adult children caregivers ac = data[data$CRtype2=="2" | data$CRtype2=="3",] # 742 | data$CRtype2=="4" w = ac[ac$workCG=="1",] # 494 nw = ac[ac$workCG=="0",] # 252 # dementia population among working caregivers d = w[w$B16b=="1",] # 181 m = d[d$genderCG=="1",] # 37 f = d[d$genderCG=="0",] # 144 nd = w[w$B16b=="0",] # 309 ### unmet need of CR # D201A-D210A: use - 0 no, 1 yes # D201B-D210B: reasons not using - 1 dont know, 2 cannt use, 3 not appropriate # D201C-D210C: need - 1-5 very unneed to very need D2A - services utilized by CR # D2C - service needed by caregivers # D2_UN - unmet need of CR ### Correlation # ggpairs(data = d, columns = 2:10, title = "bivariates") # data1 <- d[, c("ageCR","genderCR","ageCG")] corr <- round(cor(data1), 2) # Visualize - library("ggcorrplot") ggcorrplot(corr, p.mat = cor_pmat(data1), hc.order = TRUE, type = "lower", color = c("#FC4E07", "white", "#00AFBB"), outline.col = "white", lab = TRUE,na.rm=T) ### Anderson Model on Service Utialization #### # predisposing (age[ageCR], gender[genderCR], marital status[null], ethnicity[null] and family size[null]; +[resid]) # enabling (education level[eduCG], family support[C10T(relationship)], access to services[ADL_UN?], travel time to the nearest health facility[?], medical expense per capita[?], and health insurance coverage[?]), # need factors (chronic disease)[phyFra], actual needs[ADL_UN], with the utilization of health services (i.e. physician visit and hospitalization). # description table # head(describe(un),10) # show basic description of first 10 variables attach(d);table1.1 = rbind.fill(describe(ageCG),describe(ageCR),describe(phyFra),describe(ADL_UN));detach() attach(nd);table1.2 = rbind.fill(describe(ageCG),describe(ageCR),describe(phyFra),describe(ADL_UN));detach() table1 = cbind(table1.1,table1.2) reg1 = (glm(US ~ ageCR+genderCR+ageCG+genderCG+phyFra+ADL_UN+eduCG+economicCG+fr+resid+CF,data=d, family=poisson)) reg2 = (glm(US ~ ageCR+genderCR+ageCG+genderCG+phyFra+ADL_UN+eduCG+economicCG+fr+resid+CF,data=nd, family=poisson)) table2 = outreg(list(reg1,reg2)) write.csv(table1,file="C:/Users/chens/Desktop/table1.csv") write.csv(table2,file="C:/Users/chens/Desktop/table2.csv") ### Pearlin's SPM Model ### reg3 = (glm(US ~ ageCG+genderCG+eduCG+economicCG+C12+LvHm+phyFra+GF12Positive+burden+resid+CRtype3+fr+depressive+C6T,data=d, family=poisson)) reg4 = (glm(US ~ ageCG+genderCG+eduCG+economicCG+C12+LvHm+phyFra+GF12Positive+burden+resid+CRtype3+fr+depressive+C6T,data=nd, family=poisson)) table3 = outreg(list(reg3,reg4)) write.csv(table3,file="C:/Users/chens/Desktop/table3.csv") ### Andersen's model on unmet needs ### CR's unmet need: ADL_UN, IADL_UN, ADL_UNP (unmet need percentage), IADL_UNP reg5 = (glm(ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+US,data=d, family=poisson)) reg6 = (glm(ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+US,data=nd, family=poisson)) ADL1 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US+C5T,data=d, family=poisson,na.action='na.omit')) # heart issues # ADL1.1 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US+C5T,data=m, family=poisson,na.action='na.omit')) # heart issues # ADL1.2 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US+C5T,data=f, family=poisson,na.action='na.omit')) # heart issues ADL2 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US+C5T,data=nd, family=poisson,na.action='na.omit')) # heart issues ADL3 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US*C5T,data=d, family=poisson,na.action='na.omit')) # heart issues # ADL3.1 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US*C5T,data=m, family=poisson,na.action='na.omit')) # heart issues # ADL3.2 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US*C5T,data=f, family=poisson,na.action='na.omit')) # heart issues ADL4 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US*C5T,data=nd, family=poisson,na.action='na.omit')) # heart issues IADL1 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US+C5T,data=d, family=poisson,na.action='na.omit')) # heart issues IADL2 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US+C5T,data=nd, family=poisson,na.action='na.omit')) # heart issues IADL3 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US*C5T,data=d, family=poisson,na.action='na.omit')) # heart issues IADL4 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+US*C5T,data=nd, family=poisson,na.action='na.omit')) # heart issues table4 = outreg(list(ADL1,ADL2,ADL3,ADL4,IADL1,IADL2,IADL3,IADL4)) write.csv(table4,file="C:/Users/chens/Desktop/table.csv") # felt need: real need # expressed need: real need to be expressed ADL1 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS+PAC1,data=d, family = gaussian(),na.action='na.omit')) # heart issues ADL2 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS+PAC1,data=nd, family = gaussian(),na.action='na.omit')) # heart issues ADL3 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS*PAC1,data=d, family = gaussian(),na.action='na.omit')) # heart issues ADL4 = (glm( ADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS*PAC1,data=nd, family = gaussian(),na.action='na.omit')) # heart issues IADL1 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS+PAC1,data=d, family = gaussian(),na.action='na.omit')) # heart issues IADL2 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS+PAC1,data=nd, family = gaussian(),na.action='na.omit')) # heart issues IADL3 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS*PAC1,data=d, family = gaussian(),na.action='na.omit')) # heart issues IADL4 = (glm(IADL_UN ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+UNS*PAC1,data=nd, family = gaussian(),na.action='na.omit')) # heart issues table4 = outreg(list(ADL1,ADL2,ADL3,ADL4,IADL1,IADL2,IADL3,IADL4)) write.csv(table4,file="C:/Users/chens/Desktop/table1.csv") ############ m1 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+ ADL_UN+C6T+PAC,data=d, family=poisson,na.action='na.omit')) m2 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+ ADL_UN+C6T+PAC,data=nd, family=poisson,na.action='na.omit')) m3 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+ ADL_UN+C6T*PAC,data=d, family=poisson,na.action='na.omit')) m4 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+ ADL_UN+C6T*PAC,data=nd, family=poisson,na.action='na.omit')) m5 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+IADL_UN+C6T+PAC,data=d, family=poisson,na.action='na.omit')) m6 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+IADL_UN+C6T+PAC,data=nd, family=poisson,na.action='na.omit')) m7 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+IADL_UN+C6T*PAC,data=d, family=poisson,na.action='na.omit')) m8 = (glm(UNS ~ ageCR+genderCR+ageCG+genderCG+CGMarry+eduCG+resid+dr+CF+economicCG+phyFra+depressive+B16c+IADL_UN+C6T*PAC,data=nd, family=poisson,na.action='na.omit')) UNS_ADL_d = interplot(m3, var1 = "PAC",var2 = "C6T", predPro = TRUE, var2_vals = c(min( d$C6T,na.rm=T), max( d$C6T,na.rm=T))) + ggtitle("Unmet Need on PAC by ZBI among CG of Dementia Caregivers") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("LowestZBI", "Highest ZBI")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("LowestZBI", "Highest ZBI")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) + ylab("Estimated Coefficient for Unmet Need of Services") UNS_ADL_nd = interplot(m4, var1 = "PAC",var2 = "C6T", predPro = TRUE, var2_vals = c(min( nd$C6T,na.rm=T), max(nd$C6T,na.rm=T))) + ggtitle("Unmet Need on PAC by ZBI among CG of non-Dementia Caregiver") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("LowestZBI", "Highest ZBI")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("LowestZBI", "Highest ZBI")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) UNS_IADL_d = interplot(m7, var1 = "PAC",var2 = "C6T", predPro = TRUE, var2_vals = c(min( d$C6T,na.rm=T),max( d$C6T,na.rm=T))) + ggtitle("Unmet Need on PAC by ZBI among CG of Dementia Caregiver") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("LowestZBI", "Highest ZBI")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("LowestZBI", "Highest ZBI")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) + ylab("Estimated Coefficient for Unmet Need of Services") UNS_IADL_nd = interplot(m8, var1 = "PAC",var2 = "C6T", predPro = TRUE, var2_vals = c(min( nd$C6T,na.rm=T),max( nd$C6T,na.rm=T))) + ggtitle("Unmet Need on PAC by ZBI among CG of non-Dementia Caregiver") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("LowestZBI", "Highest ZBI")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("LowestZBI", "Highest ZBI")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) grid.arrange(UNS_ADL_d,UNS_ADL_nd,UNS_IADL_d,UNS_IADL_nd, ncol=2, nrow=2) # library(gridExtra) table4 = outreg(list(m1,m2,m3,m4,m5,m6,m7,m8)) write.csv(table4,file="C:/Users/chens/Desktop/table1.csv") ############### # interplot(ADL3, var1 = 'US',var2 = 'C5T', predPro = FALSE) + ggtitle("Average Conditional Effects") # V1 on x-axis; prediction of V2 on DV on y-axis # impute(d$C5T,median) # impute(d$US,median) library(gridExtra) ADLd = interplot(ADL3, var1 = "PAC1",var2 = "UNS", predPro = TRUE, var2_vals = c(min( d$UNS,na.rm=T), max( d$UNS,na.rm=T))) + ggtitle("Unmet Need of ADL on` PAC by SU among CG of Dementia Population") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) + ylab("Estimated Coefficient for Service Utilization") ADLnd = interplot(ADL4, var1 = "PAC1",var2 = "UNS", predPro = TRUE, var2_vals = c(min(nd$UNS,na.rm=T), max(nd$UNS,na.rm=T))) + ggtitle("Unmet Need of ADL on` PAC by SU among CG of Other Population") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) IADLd = interplot(IADL3, var1 = "PAC1",var2 = "UNS", predPro = TRUE, var2_vals = c(min( d$UNS,na.rm=T), max( d$UNS,na.rm=T))) + ggtitle("Unmet Need of IADL on` PAC by SU among CG of Dementia Population") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) + xlab("PAC") + ylab("Estimated Coefficient for Service Utilization") IADLnd = interplot(IADL4,var1 = "PAC1",var2 = "UNS", predPro = TRUE, var2_vals = c(min(nd$UNS,na.rm=T), max(nd$UNS,na.rm=T))) + ggtitle("Unmet Need of IADL on` PAC by SU among CG of Other Population") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("Least Service Unmet Need", "Most Service Unmet Need")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) + xlab("PAC") grid.arrange(ADLd,ADLnd,IADLd,IADLnd, ncol=2, nrow=2) # library(gridExtra) # plot_3val <- interplot(ADL3, var1 = "US",var2 = "C5T", predPro = TRUE, var2_vals = c(min(d$C5T), max(d$C5T))) + ggtitle("Conditional Predicted Probabilities for \nCitizens with Low and High Incomes") + scale_colour_discrete(guide = guide_legend(title = "Income"), labels = c("Low", "High")) + scale_fill_discrete(guide = guide_legend(title = "Income"), labels = c("Low", "High")) + theme(legend.position = c(0, .8), legend.justification = c(0, .5)) interplot(ADL3, var1 = "US",var2 = "C5T", predPro = TRUE, var2_vals = c(min(d$US,na.rm=T), max(d$US,na.rm=T)), point=T) + ggtitle("Unmet Need of ADL on PAC by SU among CG of Dementia Population") + scale_colour_discrete(guide = guide_legend(title = "Mean"), labels = c("Least Service", "Most Service")) + scale_fill_discrete(guide = guide_legend(title = "Intervals"), labels = c("Least Service", "Most Service")) + theme(legend.position = c(.1, .8), legend.justification = c(0, .5)) ## Framework # DV: utilization of service in need [US] # IV: unmet need of CR in services[D2_UN], in ADL[ADL_UN], in IADL[IADL_UN], health status [FrailtyT], CR support [CRCsp,CREsp,CRFsp,CRdm], CG support [CGCsp,CGEsp,CGFsp,CGdm] # CV: demographics of CR[genderCR, ageCR, resCR, B15p/cohabit(living alone)], of CG[genderCG, ageCG, workCG, eduCG, maritalCG, economicCG, incomeCG], caregiving[CMon], PAC [C5T], ZBI [C6T], caregiving hours weekly [B13], relationship[C12, C10T] # MV: meaning [PAC, PACas,PACel] m1 = glm(US ~ ageCG+genderCG+eduCG+CGMarry+B13+economicCG+DemCom+ADL_UN+C6T+C10T,data=w, family=poisson) m2 = glm(US ~ ageCG+genderCG+eduCG+CGMarry+B13+economicCG+DemCom+ADL_UN+C6T*C10T,data=w, family=poisson) m3 = glm(US ~ ageCG+genderCG+eduCG+CGMarry+B13+economicCG+DemCom+ADL_UN+C6T+C10T,data=nw, family=poisson) m4 = glm(US ~ ageCG+genderCG+eduCG+CGMarry+B13+economicCG+DemCom+ADL_UN+C6T*C10T,data=nw, family=poisson) summary(glm(US ~ ageCG+genderCG+eduCG+CGMarry+B13+economicCG+DemCom+ADL_UN+C6T+C10T,data=w, family=poisson)) summary(glm(US ~ ageCG+genderCG+eduCG+CGMarry+B13+economicCG+DemCom+ADL_UN+C6T+C10T,data=nw, family=poisson)) ## Variables - basic check (finished) # ad1=un[,c("D1_UN", "D2_UN", "ADL_UN", "IADL_UN", "phyFra", "psyFra", "socFra")] # pairs(ad1) # between DV and IVs # cor(ad1,method = c("pearson", "kendall", "spearman")) # cor(ad1,na.rm=T) # describe(ad1) # table(c(D1_UN, D2_UN, ADL_UN, IADL_UN, FrailtyT)) # hist(D1_UN, breaks=0:6) ## Models # # Model 1: CR demographics -> CR UN *** # m1.1 = glm(ADL_UN ~ genderCR+ageCR+CMon+CF+comb+CRCsp+CREsp+CRdm, data=data, family=poisson) # m1.2 = glm(ADL_UN ~ genderCR+ageCR+CMon+CF+CRCsp*comb+CREsp*comb+CRdm*comb, data=data, family=poisson) # m1.3 = glm(IADL_UN ~ genderCR+ageCR+CMon+CF+comb+CRCsp+CREsp+CRdm, data=data, family=poisson) # m1.4 = glm(IADL_UN ~ genderCR+ageCR+CMon+CF+CRCsp*comb+CREsp*comb+CRdm*comb, data=data, family=poisson) # # stargazer(m1.1,m1.2,m1.3,m1.4,title="ModelResult",column.labels=c('ADL','Interaction','IADL','Interaction'),align=T,type="text",out="table.htm") # # # Model 2: CR UN -> CR Health * # m2.1 = glm(FrailtyT ~ ADL_UN+IADL_UN+genderCR+ageCR, data=data) # m2.2 = glm(phyFra ~ ADL_UN+IADL_UN+genderCR+ageCR, data=data) # m2.3 = glm(psyFra ~ ADL_UN+IADL_UN+genderCR+ageCR, data=data) # m2.4 = glm(socFra ~ ADL_UN+IADL_UN+genderCR+ageCR, data=data) # # stargazer('m2.1',m2.2,m2.3,'m2.4',title="ModelResult",column.labels=c('Frailty','Phy Frail','Psy Frail','Soc Frail'),align=T,type="text",out="table.htm") # # m2.2, m2.3, m2.3 # # # Model 3: CR UN -> CG ZBI | PAC # m3.1 = glm(ZBI4Score ~ ageCG+genderCG+CF+economicCG+ADL_UN+IADL_UN+phyFra+psyFra+PAC+C12+C10T, data=w) # m3.2 = glm(ZBI4Score ~ ageCG+genderCG+CF+economicCG+ADL_UN+IADL_UN+phyFra+psyFra+PAC+C12+C10T, data=nw) # # stargazer(m3.1,m3.2,title="ModelResult",column.labels=c('Working','Non-working'),align=T,type="text",out="table.htm") # # # Model 4: CR Health -> CG utilization of services in need | PAC # m4.1 = glm(US ~ ZBI4Score+workCG, data=data, family=poisson) # m4.2 = glm(US ~ ZBI4Score*workCG, data=data, family=poisson) # # stargazer(m4.1,m4.2,title="ModelResult",column.labels=c('Main','Interaction'),align=T,type="text",out="table.htm") # # # check actual coefficients if model is desirable # # t1 = cbind(exp(coef(m1)),exp(confint(m1))) # # t1.1 = cbind(exp(coef(m1.1)),exp(confint(m1.1))) # # t1.2 = cbind(exp(coef(m1.2)),exp(confint(m1.2))) # # stepAIC(m1,direction="both") # library(MASS) # Model 1: CR demographics + PAC -> frailty # summary(glm(US ~ genderCG+ageCG+workCG+eduCG+economicCG+incomeCG+ADL_UN+IADL_UN+C6T+C7,data=data, family=poisson)) # summary(glm(US ~ genderCG+ageCG+workCG+eduCG+economicCG+ADL_UN+IADL_UN+C6T+C7,data=data, family=poisson)) ## Model Tables # Regression Models - export to Excel # Model Table Set 1 # table1 = outreg(list(m1.1,m1.2,m1.3,m1.4)) # table2 = outreg(list(m2.1,m2.2,m2.3,m2.4)) # table3 = outreg(list(m3.1,m3.2)) # table4 = outreg(list(m4.1,m4.2)) # reg1 = (glm(US ~ ageCR+genderCR+ageCG+genderCG+phyFra+ADL_UN+eduCG+economicCG+fr+resid+CF,data=d, family=poisson)) # reg2 = (glm(US ~ ageCR+genderCR+ageCG+genderCG+phyFra+ADL_UN+eduCG+economicCG+fr+resid+CF,data=nd, family=poisson)) # table2 = outreg(list(reg1,reg2)) # Combine all tables # table = rbind.fill(table1,table2,table3,table4) # require library(plyr) # output to excel # write.csv(table1,file="C:/Users/chens/Desktop/test1.csv") # write.csv(table2,file="C:/Users/chens/Desktop/test2.csv") # write.csv(table3,file="C:/Users/chens/Desktop/test3.csv") # write.csv(table4,file="C:/Users/chens/Desktop/test4.csv") #anova12=anova(linear1,linear2) #anova23=anova(linear2,linear3) #anova34=anova(linear3,linear4) #anova14=anova(linear1,linear4) #stargazer(anova12,anova23,anova34,anova14,title="Model Comparison",align=T,type="text",out="table.htm") library(jiebaR) library(rJava) library(Rwordseg) # install.packages("Rwordseg", repos = "http://R-Forge.R-project.org") ### Word Cloud data = read.table("C:/Users/chens/Desktop/wordcloud.text") segmentCN("C:/Users/chens/Desktop/wordcloud.text",returnType="tm") wk = worker() segment("C:/Users/chens/Desktop/wordcloud.text", wk)

f3d3bc997679bf29a8e37e64139bb441daedafc5

2d1a8db7061ceda55e5f37990f764317d4c193d8

/LOLA Enrichments/LOLA_mmarge_4_30_19.R

4bcb600080c809cdd2856795f9c32acd11c6aff7

[]

no_license

aciernia/BTBR-BMDM-Endotoxin-Tolerance

643a007011b7a273eed111480a57c7b957579f9c

be119492b3d1208dc59f578ca2e1f53bbef30afa

refs/heads/master

2022-12-27T07:25:31.580457

2020-10-09T14:42:38

258,902,236

0

null

UTF-8

R

false

9,206

r

LOLA_mmarge_4_30_19.R

#author: Annie Vogel Ciernia #a.ciernia@gmail.com #10/9/2018 ############################################################################################################## library(dplyr) library(tidyr) library(cowplot) library(gplots) #if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") #BiocManager::install("LOLA") library(LOLA) #source("https://bioconductor.org/biocLite.R") #biocLite("GenomicRanges") library(GenomicRanges) #source("https://bioconductor.org/biocLite.R") #biocLite("qvalue") library(qvalue) options("scipen"=100, "digits"=4) #prevent exponents ############################################################################################################## #read in peak bed files to GRanges List library(ChIPseeker) library("zonator") setwd("/Users/annieciernia/Sync/collaborations/Ashwood/BTBR_BMDM/atac_2019/consensus\ peaks") path <- getwd() #get lists files <- list.files(path=".", pattern="*.mm10.bed", all.files=T, full.names=T) filelist <- lapply(files, readPeakFile) #get and fix names of files names_list <- paste0(basename(file_path_sans_ext(files))) names_list <- gsub("_DEpeaks.mm10", "", names_list) names_list <- gsub("_L_", "<", names_list) names_list <- gsub("_G_", ">", names_list) names(filelist) <- names_list names(filelist) #load background regions: all possible peaks called in all samples Background <- readBed(file = "/Users/annieciernia/Sync/collaborations/Ashwood/BTBR_BMDM/atac_2019/DiffBind/Allconsensuspeaks.bed") #load DB: #regionDB <- loadRegionDB(dbLocation = "/Users/annieciernia/Desktop/regionDB/mm10/",limit = NULL, collections = c("collection1","collection3","collection4","collection5","collection6","collection7","collection8")) #save(regionDB,file="/Users/annieciernia/Desktop/regionDB/RegionDBmm10_9_3_18.Rdata") #support is the overlap, and b, c, and d complete the 2x2 table regionDB_TF <- loadRegionDB(dbLocation = "/Users/annieciernia/Desktop/mm10_LOLA_DB/mm10/",limit = NULL, collections = c("collection5","collection6","collection7","collection8")) setwd("/Users/annieciernia/Sync/collaborations/Ashwood/BTBR_BMDM/atac_2019/peak_overlaps1_5_2020") #two tailed fisher exact test: Results <- runLOLA(userSets = filelist, userUniverse = Background, regionDB = regionDB_TF, minOverlap = 1, cores=2, redefineUserSets = FALSE,direction = "enrichment") #locResult = Results[2,] #extractEnrichmentOverlaps(locResult, filelist, regionDB_TF) writeCombinedEnrichment(combinedResults = Results, outFolder = "DEpeak_RegionOverlaps", includeSplits=F) ######################################################################################################################## merge <- Results #pValueLog:=-log10(pValueLog + 10^-322) #make pvalue #merge$pvalue <- 10^-(merge$pValueLog) #undo pseudo count: # merge$pvalue <- merge$pvalue - 10^-322 # merge$pvalue <- abs(merge$pvalue) # merge$FDR <- p.adjust(merge$pvalue,method = "fdr") #merge <- enrichments names(merge)[names(merge) == 'support'] <- 'userSet.in.target.list' names(merge)[names(merge) == 'b'] <- 'NonuserSet.in.target.list' names(merge)[names(merge) == 'c'] <- 'userSet.not.in.target.list' names(merge)[names(merge) == 'd'] <- 'NonuserSet.not.in.target.list' #% enrichment merge$percent_userSet_in_Target <- (merge$userSet.in.target.list/(merge$userSet.in.target.list + merge$userSet.not.in.target.list)*100) merge$percent_BG_in_Target <- (merge$NonuserSet.in.target.list/(merge$NonuserSet.in.target.list + merge$NonuserSet.not.in.target.list)*100) #fold enrichment relative to background #merge$FC <- (merge$percent_userSet_in_Target - merge$percent_BG_in_Target)/merge$percent_BG_in_Target #read in list descriptions listdescript <- read.csv("descriptionorder_fixed.csv") merge2 <- merge(merge,listdescript,by.x="description",by.y="old") write.csv(merge2,file="FisherExact_TF_Enrichements_1_5_20.csv") ######################################################################################################################## #clean up names enrichments <- merge2 unique(enrichments$userSet) neworder <- c("BTBRmedia<C57media" ,"BTBRmedia>C57media", "C57LPS1>C57media","BTBRLPS1>BTBRmedia", "C57LPS2>C57media","BTBRLPS2>BTBRmedia", "C57LPS1<C57media","BTBRLPS1<BTBRmedia", "C57LPS2<C57media","BTBRLPS2<BTBRmedia", "BTBRLPS1<C57LPS1","BTBRLPS2<C57LPS2", "BTBRLPS1>C57LPS1","BTBRLPS2>C57LPS2") enrichments$userSet <- factor(enrichments$userSet, levels = neworder) #enrichments_sig <- filter(enrichments,enrichments$qValue<0.05) ######################################################################################################################## #plots ######################################################################################################################## #significant enrichments only: Collection5678 <- enrichments %>% filter(collection == "collection5"| collection == "collection6"| collection == "collection7"| collection == "collection8") %>% filter(qValue < 0.05) #plot of odds ratios as dot size and pvalues as heatmap color for all lists sig across samples ggplot(Collection5678, aes(y = newnames, x = oddsRatio)) + facet_grid(~userSet)+ geom_point( alpha=0.75, aes(size = userSet.in.target.list,color=qValue)) + scale_size(name = "Number of \n Overlapping Regions", breaks = signif(fivenum(Collection5678$userSet.in.target.list),2), #returns rounded values for 5 sets labels = signif(fivenum(Collection5678$userSet.in.target.list),2))+ theme_bw() + xlab("Odds Ratio") + ylab("Comparison List") + scale_color_gradient(low="blue",high="red")+ theme(strip.text.x = element_text(size = 14)) + #theme(axis.text.x=element_text(angle=65,hjust=1)) + theme(legend.key = element_blank(), strip.background = element_blank(),panel.border = element_rect(colour = "black"))+ theme(axis.text=element_text(size=14),axis.title=element_text(size=14)) ggsave(filename="Collection5678_BMDMmarks_1_5_20.pdf",width = 16, height = 6, dpi = 300, useDingbats = FALSE) ######################################################################################################################## #significant enrichments only: media<LPS Collection5678 <- enrichments %>% filter(collection == "collection5"| collection == "collection6"| collection == "collection7"| collection == "collection8") %>% filter(qValue < 0.05) %>% filter(grepl("<", userSet)) #plot of odds ratios as dot size and pvalues as heatmap color for all lists sig across samples ggplot(Collection5678, aes(y = newnames, x = oddsRatio)) + facet_grid(~userSet)+ geom_point( alpha=0.75, aes(size = userSet.in.target.list,color=qValue)) + scale_size(name = "Number of \n Overlapping Regions", breaks = signif(fivenum(Collection5678$userSet.in.target.list),2), #returns rounded values for 5 sets labels = signif(fivenum(Collection5678$userSet.in.target.list),2))+ theme_bw() + xlab("Odds Ratio") + ylab("Comparison List") + scale_color_gradient(low="red",high="blue")+ theme(legend.key = element_blank(), strip.background = element_blank(),panel.border = element_rect(colour = "black"))+ theme(axis.text=element_text(size=14),axis.title=element_text(size=14)) ggsave(filename="LessThanPeaks_BMDMmarks_5_2_19.pdf",width = 12, height = 5, dpi = 300, useDingbats = FALSE) ######################################################################################################################## #significant enrichments only: media>LPS Collection5678 <- enrichments %>% filter(collection == "collection5"| collection == "collection6"| collection == "collection7"| collection == "collection8") %>% filter(qValue < 0.05) %>% filter(grepl(">", userSet)) #plot of odds ratios as dot size and pvalues as heatmap color for all lists sig across samples ggplot(Collection5678, aes(y = newnames, x = oddsRatio)) + facet_grid(~userSet)+ geom_point( alpha=0.75, aes(size = userSet.in.target.list,color=qValue)) + scale_size(name = "Number of \n Overlapping Regions", breaks = signif(fivenum(Collection5678$userSet.in.target.list),2), #returns rounded values for 5 sets labels = signif(fivenum(Collection5678$userSet.in.target.list),2))+ theme_bw() + xlab("Odds Ratio") + ylab("Comparison List") + scale_color_gradient(low="red",high="blue")+ theme(strip.text.x = element_text(size = 14)) + #theme(axis.text.x=element_text(angle=65,hjust=1)) + theme(legend.key = element_blank(), strip.background = element_blank(),panel.border = element_rect(colour = "black"))+ theme(axis.text=element_text(size=14),axis.title=element_text(size=14)) ggsave(filename="GreaterThanPeaks_BMDMmarks_5_2_19.pdf",width = 12, height = 6, dpi = 300, useDingbats = FALSE)

3ea323ca0b8191427c28a9204923ed0194bd30d7

c12d52663ecd6f7088337fe371e77e2f82398758

/man/colCumprods.Rd

0657457c085919eedfb57f3e899e0d3bb0f740a2

[]

no_license

federicomarini/sparseMatrixStats

8d581ad4db29583c4f9d56f18fbdfdcebc3cf1d0

b5c036095d3aac4be00096f793b199aebf4d1fcd

refs/heads/master

2020-08-05T03:24:15.701873

2019-10-02T14:37:51

212,375,029

0

null

2019-10-02T15:22:15

2019-10-02T15:22:14

null

UTF-8

R

false

true

1,683

rd

colCumprods.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R, R/methods_row.R \docType{methods} \name{colCumprods} \alias{colCumprods} \alias{colCumprods,dgCMatrix-method} \alias{rowCumprods} \alias{rowCumprods,dgCMatrix-method} \title{Cumulative sums, products, minima and maxima for each row (column) in a matrix} \usage{ colCumprods(x, rows = NULL, cols = NULL, ...) \S4method{colCumprods}{dgCMatrix}(x, rows = NULL, cols = NULL, ...) rowCumprods(x, rows = NULL, cols = NULL, ...) \S4method{rowCumprods}{dgCMatrix}(x, rows = NULL, cols = NULL, ...) } \arguments{ \item{x}{A \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}}.} \item{rows}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{cols}{A \code{\link[base]{vector}} indicating subset of elements (or rows and/or columns) to operate over. If \code{\link[base]{NULL}}, no subsetting is done.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{numeric}} NxK \code{\link[base]{matrix}} of the same mode as \code{x}. } \description{ Cumulative sums, products, minima and maxima for each row (column) in a matrix. } \examples{ x <- matrix(1:12, nrow = 4, ncol = 3) print(x) yr <- rowCumsums(x) print(yr) yc <- colCumsums(x) print(yc) yr <- rowCumprods(x) print(yr) yc <- colCumprods(x) print(yc) yr <- rowCummaxs(x) print(yr) yc <- colCummaxs(x) print(yc) yr <- rowCummins(x) print(yr) yc <- colCummins(x) print(yc) } \seealso{ See \code{\link[base]{cumsum}}(), \code{\link[base]{cumprod}}(), \code{\link[base]{cummin}}(), and \code{\link[base]{cummax}}(). }

acc1ebcb4ba5200928f3921f1032624565f69f6e

9cfdec25ad3ec65679a4cca555422bfef54e73ab

/hw/hw1/test.r

ae5bcb829cbfd48e1f4165baae5c362083895282

[]

no_license

huberf/matlab-class

a5e186f558cf3bbdd42340ee4fb134a723cf26a0

202e30ec217bf6e50ff48757e4a4fedc9598987b

refs/heads/master

2021-01-17T16:46:47.765418

2016-07-01T05:35:27

62,062,404

0

null

UTF-8

R

false

248

r

test.r

total <- ((pi * 0.04^2 * 0.07) + (0.25 * 0.08 * 0.07) - 3 * (pi * 0.015^2 * 0.07)) * 8050 total <- ((pi * 0.04^2 * 0.07) + (0.25 * 0.08 * 0.07)) * 8050 total <- ((pi * 0.04^2 * 0.07) + (0.25 * 0.08 * 0.07) - 3 * (pi * 0.02779327^2 * 0.07)) * 8050

acd97d4dca644eea97cbcd14b375cb8b59c47633

538b909ebc208800939ee38d479e19f34e033123

/cachematrix.R

704ee2eba5b1a8b0e6453c9c49cecbc29e76c056

[]

no_license

mwirth7070/ProgrammingAssignment2

0e4b61610eba71462c92a8ac40cb3d36fed02a78

d7d3adb67c6c03963bd5b88f3db4c8c2a2af9e6c

refs/heads/master

2021-01-18T04:35:00.814275

2014-07-25T01:48:10

null

0

null

UTF-8

R

false

993

r

cachematrix.R

#makeCacheMatrix: This function creates a special "matrix" object that caches its inverse. #cacheSolve: This function computes the inverse of the special "matrix" returned by makeCacheMatrix. makeCacheMatrix <- function(x = matrix()) { m<-NULL # Set the value of the vector set<-function(y){ x<<-y m<<-NULL } get<-function() x #get the value of the vector setmatrix<-function(solve) m<<- solve #set the value of the matrix getmatrix<-function() m #get the value of the matrix list(set=set, get=get,setmatrix=setmatrix,getmatrix=getmatrix) } cacheSolve <- function(x=matrix(), ...) { m<-x$getmatrix() if(!is.null(m)){ #if the matrix has not already been calculated, notify and then calculate, otherwise skip message("getting cached data") return(m) } matrix<-x$get() m<-solve(matrix, ...) #calculates the matrix of the special "vector" created with makeCacheMatrix function x$setmatrix(m) #sets the value of the matrix m }

ac7d6e0230b835e2f9823101f9deb82360236543

fadd25738df09516aedb88a53579e7e121ad51f4

/R/signalInfo.R

38ce552e112d47f10fbe8ed9d65e3819f4b83c1d

[]

no_license

JangSeonghoon/maintcivil

2630dee5df3512c5f9ea39b71169b590138e4ddc

7a5c61eedfdd4bb3b10f506b8e11aac11b475f30

refs/heads/master

2021-09-06T21:48:32.855105

2018-02-12T05:23:12

103,596,243

0

null

UTF-8

R

false

1,203

r

signalInfo.R

#' #' signal information #' #' @param workspace_no, startT,lastT,direction, order, kind #' @return km of the signal devtools::use_package("stringr") #' @importFrom stringr str_c #' @importFrom stringr str_detect #' @importFrom compiler cmpfun #' @export signal=function(workspace_no,startT,lastT,direction,order,kind){ A=cmpfun( function(){ if(Sys.info()['sysname']=="Windows"){ path= paste0( Sys.getenv("CATALINA_HOME"),"/webapps/bigTeam/" ) }else if(Sys.info()['sysname']=="Linux"){ load("/home/jsh/eclipse-workspace/bigTeam/src/main/webapp/") } startT=as.character(startT) lastT=as.character(lastT) direction=as.character(direction) kind=as.character(kind) workspace_no=floor(workspace_no/100)*100 load(path,"RData/DB(utf8).RData") compare=eval(parse(text=paste0("signal_",workspace_no))) compareSet=str_c(compare[,1],collape=",",compare[,2],collape=",",compare[,3],collape=",",compare[,4],collape="번,",compare[,6],collape="") no=which( str_detect(compareSet,startT)* str_detect(compareSet,lastT)* str_detect(compareSet,direction)* str_detect(compareSet,paste0(order,"번"))* str_detect(compareSet,kind)==1 )[1] return(compare[no,5]) } ) A() }

bcbb2fe83d7872ebab19e0a4f150a3047b37b399

2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0

/fuzzedpackages/gaston/R/bm_vcf.r

58f833053d8accfd2207f9559e2c14b3022b4aa1

[]

no_license

akhikolla/testpackages

62ccaeed866e2194652b65e7360987b3b20df7e7

01259c3543febc89955ea5b79f3a08d3afe57e95

refs/heads/master

2023-02-18T03:50:28.288006

2021-01-18T13:23:32

329,981,898

7

1

null

UTF-8

R

false

2,622

r

bm_vcf.r

read.vcf <- function(file, max.snps, get.info = FALSE, convert.chr = TRUE, verbose = getOption("gaston.verbose",TRUE)) { xx <- NULL; filename <- path.expand(file) if(missing(max.snps)) max.snps = -1L; L <- .Call("gg_read_vcf2", PACKAGE = "gaston", filename, max.snps, get.info) snp <- data.frame(chr = L$chr, id = L$id, dist = 0, pos = L$pos , A1 = L$A1, A2 = L$A2, quality = L$quality, filter = factor(L$filter), stringsAsFactors = FALSE) if(get.info) snp$info <- L$info if(convert.chr) { chr <- as.integer(L$chr) chr[L$chr == "X" | L$chr == "x"] <- getOption("gaston.chr.x")[1] chr[L$chr == "Y" | L$chr == "y"] <- getOption("gaston.chr.y")[1] chr[L$chr == "MT" | L$chr == "mt"] <- getOption("gaston.chr.mt")[1] if(any(is.na(chr))) warning("Some unknown chromosomes id's (try to set convert.chr = FALSE)") snp$chr <- chr } ped <- data.frame(famid = L$samples, id = L$samples, father = 0, mother = 0, sex = 0, pheno = NA, stringsAsFactors = FALSE) x <- new("bed.matrix", bed = L$bed, snps = snp, ped = ped, p = NULL, mu = NULL, sigma = NULL, standardize_p = FALSE, standardize_mu_sigma = FALSE ) if(getOption("gaston.auto.set.stats", TRUE)) x <- set.stats(x, verbose = verbose) x } read.vcf.filtered <- function(file, positions, max.snps, get.info = FALSE, convert.chr = TRUE, verbose = getOption("gaston.verbose",TRUE)) { xx <- NULL; filename <- path.expand(file) if(missing(max.snps)) max.snps = -1L; L <- .Call("gg_read_vcf_filtered", PACKAGE = "gaston", filename, positions, max.snps, get.info) snp <- data.frame(chr = L$chr, id = L$id, dist = 0, pos = L$pos , A1 = L$A1, A2 = L$A2, quality = L$quality, filter = factor(L$filter), stringsAsFactors = FALSE) if(get.info) snp$info <- L$info if(convert.chr) { chr <- as.integer(L$chr) chr[L$chr == "X" | L$chr == "x"] <- getOption("gaston.chr.x")[1] chr[L$chr == "Y" | L$chr == "y"] <- getOption("gaston.chr.y")[1] chr[L$chr == "MT" | L$chr == "mt"] <- getOption("gaston.chr.mt")[1] if(any(is.na(chr))) warning("Some unknown chromosomes id's (try to set convert.chr = FALSE)") snp$chr <- chr } ped <- data.frame(famid = L$samples, id = L$samples, father = 0, mother = 0, sex = 0, pheno = NA, stringsAsFactors = FALSE) x <- new("bed.matrix", bed = L$bed, snps = snp, ped = ped, p = NULL, mu = NULL, sigma = NULL, standardize_p = FALSE, standardize_mu_sigma = FALSE ) if(getOption("gaston.auto.set.stats", TRUE)) x <- set.stats(x, verbose = verbose) x }

9c51a3575f9d3bd376f51a56927ca818bf8e2c80

4a6b5be2d735c8d6c3caa4ba2c47803dd386d546

/R/centrality.R

25e798696ef5541ceab33fd2f9afefd3e4184de8

[]

no_license

jonmcalder/tidygraph

8e19df9c90c696a24878d8bcdd4a1d3762b7faa4

fba663d33b1ac4dfc18b30488b4f5ea24a0d079a

refs/heads/master

2020-12-02T22:17:46.282865

2017-07-03T12:08:28

96,108,967

0

null

2017-07-03T12:35:01

null

UTF-8

R

false

4,411

r

centrality.R

#' Calculate node and edge centrality #' #' The centrality of a node measures the importance of node in the network. As #' the concept of importance is ill-defined and dependent on the network and #' the questions under consideration, many centrality measures exist. #' `tidygraph` provides a consistent set of wrappers for all the centrality #' measures implemented in `igraph` for use inside [dplyr::mutate()] and other #' relevant verbs. All functions provided by `tidygraph` have a consistent #' naming scheme and automatically calls the function on the graph, returning a #' vector with measures ready to be added to the node data. #' #' @param ... Parameters passed on to the `igraph` implementation. #' #' @return A numeric vector giving the centrality measure of each node. #' #' @name centrality #' @rdname centrality #' #' @examples #' create_notable('bull') %>% #' activate(nodes) %>% #' mutate(importance = centrality_alpha()) #' #' # Most centrality measures are for nodes but not all #' create_notable('bull') %>% #' activate(edges) %>% #' mutate(importance = centrality_edge_betweenness()) NULL #' @describeIn centrality Wrapper for [igraph::alpha_centrality()] #' @importFrom igraph V alpha_centrality #' @export centrality_alpha <- function(...) { expect_nodes() graph <- .G() alpha_centrality(graph = graph, nodes = V(graph), ...) } #' @describeIn centrality Wrapper for [igraph::authority_score()] #' @importFrom igraph authority_score #' @export centrality_authority <- function(...) { expect_nodes() authority_score(graph = .G(), ...)$vector } #' @describeIn centrality Wrapper for [igraph::betweenness()] and [igraph::estimate_betweenness()] #' @importFrom igraph V betweenness estimate_betweenness #' @importFrom rlang quos #' @export centrality_betweenness <- function(...) { expect_nodes() graph <- .G() dots <- quos(...) if (is.null(dots$cutoff)) { betweenness(graph = graph, v = V(graph), ...) } else { estimate_betweenness(graph = graph, vids = V(graph), ...) } } #' @describeIn centrality Wrapper for [igraph::power_centrality()] #' @importFrom igraph V power_centrality #' @export centrality_power <- function(...) { expect_nodes() graph <- .G() power_centrality(graph = graph, nodes = V(graph), ...) } #' @describeIn centrality Wrapper for [igraph::closeness()] and [igraph::estimate_closeness()] #' @importFrom igraph V closeness estimate_closeness #' @importFrom rlang quos #' @export centrality_closeness <- function(...) { expect_nodes() graph <- .G() dots <- quos(...) if (is.null(dots$cutoff)) { closeness(graph = graph, vids = V(graph), ...) } else { estimate_closeness(graph = graph, vids = V(graph), ...) } } #' @describeIn centrality Wrapper for [igraph::eigen_centrality()] #' @importFrom igraph eigen_centrality #' @export centrality_eigen <- function(...) { expect_nodes() eigen_centrality(graph = .G(), ...)$vector } #' @describeIn centrality Wrapper for [igraph::hub_score()] #' @importFrom igraph hub_score #' @export centrality_hub <- function(...) { expect_nodes() hub_score(graph = .G(), ...)$vector } #' @describeIn centrality Wrapper for [igraph::page_rank()] #' @importFrom igraph V page_rank #' @export centrality_pagerank <- function(...) { expect_nodes() graph <- .G() page_rank(graph = graph, vids = V(graph), ...)$vector } #' @describeIn centrality Wrapper for [igraph::subgraph_centrality()] #' @importFrom igraph subgraph_centrality #' @export centrality_subgraph <- function(...) { expect_nodes() subgraph_centrality(graph = .G(), ...) } #' @describeIn centrality Wrapper for [igraph::degree()] and [igraph::strength()] #' @importFrom igraph V degree strength #' @importFrom rlang quos #' @export centrality_degree <- function(...) { expect_nodes() graph <- .G() dots <- quos(...) if (is.null(dots$weights)) { degree(graph = graph, v = V(graph), ...) } else { strength(graph = graph, vids = V(graph), ...) } } #' @describeIn centrality Wrapper for [igraph::edge_betweenness()] #' @importFrom igraph edge_betweenness estimate_edge_betweenness E #' @importFrom rlang quos #' @export centrality_edge_betweenness <- function(...) { expect_edges() graph <- .G() dots <- quos(...) if (is.null(dots$cutoff)) { edge_betweenness(graph = graph, e = E(graph), ...) } else { estimate_edge_betweenness(graph = graph, e = E(graph), ...) } }

9d9ea2a547a338dac268ba05d2e348d4788e0ca8

d33e98129206021371f50e4d74c44486a5a0a5a1

/install_load.R

3a8e4f8b38a423feddecf3bcda3a1524e4f5fa15

[]

no_license

therealcrowder/Case_Study_2

312367728bdee372feedf29a6365caca4b58447d

1746696b4d0f39da3c3277edb7b04096dd0c9e03

refs/heads/master

2021-01-20T00:46:03.247362

2017-04-24T22:43:29

89,184,241

0

null

UTF-8

R

false

596

r

install_load.R

install.packages("weathermetrics", repos = 'http://cran.us.r-project.org') install.packages("knitr", repos='http://cran.us.r-project.org') install.packages("markdown", repos='http://cran.us.r-project.org') install.packages("ggplot2", repos='http://cran.us.r-project.org') install.packages("plyr", repos='http://cran.us.r-project.org') install.packages("lubridate", repos='http://cran.us.r-project.org') install.packages("formatR", repos='http://cran.us.r-project.org') library(weathermetrics) library(knitr) library(markdown) library(ggplot2) library(plyr) library(lubridate) library(formatR)

4a643bcf4bbf6140e42def32c75c5f0931150198

91f977492d1e2757c0fabc52e3ade6680c5dec30

/tests/testthat/test_helsinki.R

87c8cfc3e83b0716c433b7fd6ea4ba5628a5959e

[]

no_license

cran/helsinki

1fa8b241c639f87446ebced598ab59ec0e9a754b

13d68daba1321e156f77ae47d2c5e89a235d1669

refs/heads/master

2022-12-15T08:13:36.309621

2022-12-02T08:30:05

18,805,161

0

null

UTF-8

R

false

892

r

test_helsinki.R

test_that("wfs_api() works correctly", { expect_error(wfs_api(base.url = NULL)) expect_error(wfs_api(base.url = "gopher://gopher.quux.org")) suppressMessages(expect_message(wfs_api(base.url = "https://httpstat.us/404", queries = "search"))) suppressMessages(expect_message(wfs_api(base.url = "https://httpstat.us/200", queries = c("sleep" = 11000)))) }) test_that("get_city_map() works correctly", { # Non-supported city expect_error(get_city_map(city = "porvoo")) # Non-supported level expect_error(get_city_map(city = "helsinki", level = "keskialue")) # Extremely short timeout parameter (1 ms) to ensure connection timeout suppressMessages(expect_message(get_city_map(city = "helsinki", level = "suuralue", timeout.s = 0.001))) })

7bedde392ef9b7c4a9c32c44c63c45ebb9e98738

7917fc0a7108a994bf39359385fb5728d189c182

/cran/paws.analytics/man/mturk_list_workers_with_qualification_type.Rd

59a2f19022f6f534e48750a6b38ddbe7a308b618

[ "Apache-2.0" ]

permissive

TWarczak/paws

b59300a5c41e374542a80aba223f84e1e2538bec

e70532e3e245286452e97e3286b5decce5c4eb90

refs/heads/main

2023-07-06T21:51:31.572720

2021-08-06T02:08:53

396,131,582

1

0

NOASSERTION

2021-08-14T21:11:04

null

UTF-8

R

false

true

1,616

rd

mturk_list_workers_with_qualification_type.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mturk_operations.R \name{mturk_list_workers_with_qualification_type} \alias{mturk_list_workers_with_qualification_type} \title{The ListWorkersWithQualificationType operation returns all of the Workers that have been associated with a given Qualification type} \usage{ mturk_list_workers_with_qualification_type(QualificationTypeId, Status, NextToken, MaxResults) } \arguments{ \item{QualificationTypeId}{[required] The ID of the Qualification type of the Qualifications to return.} \item{Status}{The status of the Qualifications to return. Can be \code{Granted | Revoked}.} \item{NextToken}{Pagination Token} \item{MaxResults}{Limit the number of results returned.} } \value{ A list with the following syntax:\preformatted{list( NextToken = "string", NumResults = 123, Qualifications = list( list( QualificationTypeId = "string", WorkerId = "string", GrantTime = as.POSIXct( "2015-01-01" ), IntegerValue = 123, LocaleValue = list( Country = "string", Subdivision = "string" ), Status = "Granted"|"Revoked" ) ) ) } } \description{ The \code{\link[=mturk_list_workers_with_qualification_type]{list_workers_with_qualification_type}} operation returns all of the Workers that have been associated with a given Qualification type. } \section{Request syntax}{ \preformatted{svc$list_workers_with_qualification_type( QualificationTypeId = "string", Status = "Granted"|"Revoked", NextToken = "string", MaxResults = 123 ) } } \keyword{internal}

3c08c46dee2fa0ee110936e2e4736754cf46c388

f7018991debe81fc53a55e9bf125e6514932379d

/NegBinModel.R

b0aa3e447aba8657e93d2d915600968945ccfb8d

[]

no_license

nguyenty/stat544

66b583bbab1ea3a7ce858cacfff5be4ae22a7634

01c82e02c4ac72f77719a02a8c50f989d9a0028b

refs/heads/master

2016-08-04T17:46:33.683260

2014-06-02T17:32:25

null

0

null

UTF-8

R

false

5,798

r

NegBinModel.R

library(rjags) library(xtable) #############modelm - using point mass mixture prior for signals############### modelm <- " model{ # likelihood for (i in 1:length(y)){ y[i] ~ dnegbin((1/omega[gene[i]])/(lambda[i] + 1/omega[gene[i]]), 1/omega[gene[i]]) log(lambda[i]) <- alpha[gene[i]] + (-1)^line[i]*tau[gene[i]] + beta[gene[i]]*cov[i] } # prior level 1 for (i in 1:ngene){ alpha[i] ~ dnorm(0,1/10000) omega[i] ~ dlnorm(0, 1/10000) tau[i] <- (1-bintau[i])*normtau[i] bintau[i] ~ dbern(pitau) normtau[i] ~ dnorm(0,1/sigmatau^2) beta[i] <- (1-binbeta[i])*normbeta[i] binbeta[i] ~ dbern(pibeta) normbeta[i] ~ dnorm(0,1/sigmabeta^2) } #prior level 2 pitau ~ dbeta(8,1) pibeta ~ dbeta(8,1) sigmatau ~ dunif(0,100) sigmabeta ~ dunif(0,100) } " #########modelh - using horseshoe prior for the signals############## modelh <- " model{ # likelihood for (i in 1:length(y)){ y[i] ~ dpois(lambda[i]) log(lambda[i]) <- alpha[gene[i]] + (-1)^line[i]*tau[gene[i]] + beta[gene[i]]*cov[i] } # prior level 1 for (j in 1:ngene){ alpha[j] ~ dnorm(0,1/10000) tau[j] ~ dnorm(0, 1/sigmatauj[j]^2) sigmatauj[j] ~ dt(0, 1/sigmatau^2, 1) T(0,) beta[j] ~ dnorm(0, 1/sigmabetaj[j]^2) sigmabetaj[j] ~ dt(0, 1/sigmabeta^2, 1) T(0,) } # prior level 2 sigmatau ~ dt(0, 1, 1) T(0,) sigmabeta ~ dt(0, 1, 1) T(0,) } " #############Sim_data_function ################### library(reshape) sim_data <- function(K, ngene, mualpha, sigmaalpha, pitau,mutau, sigmatau, pibeta, mubeta, sigmabeta){ # prior level 1 x <- rnorm(2*K, 0,1) bintau <- rbinom(ngene,1,pitau) tau <-(1-bintau)*rnorm(ngene, mutau, sigmatau) binbeta <- rbinom(ngene,1,pibeta) beta <- (1-binbeta)*rnorm(ngene, mubeta, sigmabeta) alpha <- rnorm(ngene, mualpha,sigmaalpha ) lambda <- matrix(0, ncol = 2*K, nrow = ngene) omega <- exp(rnorm(ngene, 0, 2)) count <- matrix(0, ncol = 2*K, nrow = ngene) for (j in 1:ngene){ for (k in 1:K){ lambda[j,k] <- exp(alpha[j] - tau[j] + beta[j]*x[k]) lambda[j, k+K] <- exp(alpha[j] + tau[j] + beta[j]*x[k+K]) count[j,k] <- rnbinom(1, size = 1/omega[j], mu = lambda[j,k]) count[j,k+K] <- rnbinom(1, size = 1/omega[j], mu = lambda[j,k+K]) } } melt_count <- melt(count) melt_count$line <- NULL melt_count$line[melt_count$X2%in%c(1:K)] <- 1 melt_count$line[melt_count$X2%in%c((K+1):(2*K))] <- 2 melt_count$cov <- NULL for(i in 1:(2*K)) melt_count$cov[melt_count$X2==i] <- x[i] dat <- list(y = melt_count$value, gene = melt_count$X1, line = melt_count$line, cov = melt_count$cov, ngene = ngene, bintau=bintau, tau = tau, binbeta = binbeta, beta = beta, alpha = alpha, omega = omega) return(dat) } ###############run_mm_simulationdata####################### out <- function(K, ngene, mualpha, sigmaalpha, pitau,mutau, sigmatau, pibeta, mubeta, sigmabeta, epstau, epsbeta){ data <- sim_data(K, ngene, mualpha, sigmaalpha, pitau,mutau, sigmatau, pibeta, mubeta, sigmabeta) mm <- jags.model(textConnection(modelm), data[1:5],n.chains = 1) resm <- coda.samples(mm, c("tau","alpha","beta", "pitau","pibeta", "binbeta","bintau", "sigmatau","sigmabeta"), 2000) mm_tau_est <- which(apply(resm[[1]][,paste("bintau[", 1:ngene,"]",sep ="")], 2, function(x) mean(abs(1-x))) > 0.5) mm_tau_est_eps <- which(apply(resm[[1]][,paste("tau[", 1:ngene,"]",sep ="")], 2, function(x) mean(abs(x)>epstau)) > 0.5) mm_tau_true <- which(data$tau!=0) mm_tau_correct <- sum(mm_tau_est%in%mm_tau_true) mm_tau_correct_eps <- sum(mm_tau_est_eps%in%mm_tau_true) # mh <- jags.model(textConnection(modelh), data[1:5] ,n.chains = 1) # resh <- coda.samples(mh, c("tau","alpha","beta", # "sigmatauj", "sigmabetaj", # "sigmatau","sigmabeta"), 2000) # mh_tau_est <- which(apply(resh[[1]][,paste("sigmatauj[", 1:ngene,"]",sep ="")], 2, # function(x) mean(1-1/(x^2+1))) > 0.5) # mh_tau_est_eps <- which(apply(resh[[1]][,paste("tau[", 1:ngene,"]",sep ="")], 2, # function(x) mean(abs(x)>epstau)) > 0.5) # mh_tau_true <- which(data$tau!=0) # mh_tau_correct <- sum(mh_tau_est%in%mh_tau_true) # mh_tau_correct_eps <- sum(mh_tau_est_eps%in%mh_tau_true) return(c( mm_tau_est = length(mm_tau_est), mm_tau_correct_est = mm_tau_correct, # mh_tau_est = length(mh_tau_est), # mh_tau_correct_est = mh_tau_correct, mm_tau_est_eps = length(mm_tau_est_eps), mm_tau_correct_est_eps = mm_tau_correct_eps, # mh_tau_est_eps = length(mh_tau_est_eps), # mh_tau_correct_est_eps = mh_tau_correct_eps, tau_true = length(mm_tau_true) )) } @ ######run_sim########## K <- 12 ngene <- 100 # prior level 2 mualpha <- 3 sigmaalpha <- 2 pitau <- 0.8 #mutau <- c(0.5,1,2) mutau <- 1 sigmatau <- 0.25 pibeta <- 0.8 #mubeta <- c(0.5,1,2) mubeta <- 1 sigmabeta <- 0.25 epstau <- epsbeta <- 2*mutau/3 post_out <- array(0, dim = c(3,3,9)) for(i in 1:3){ for (j in 1:3){ post_out[i,j,] <- out(K, ngene, mualpha, sigmaalpha, pitau,mutau[i], sigmatau, pibeta, mubeta[j] , sigmabeta, epstau[i], epsbeta[j]) } }

51620b1f440a0cf57ae651a1722362b0ac40e9c0

599e6d59345ba36cbfb297de29a61243cc728e4d

/learn lattice.R

0b94a960cabb4dc2f4f2eef97f4ae75d0a902d70

[]

no_license

abhatia2014/practice-R-Models

a0433e11ea49dd0598cac4648b8bb9779b71f130

c2e6c639e5565c2ccb22116bde562d60ed4c52f8

refs/heads/master

2021-01-11T00:06:47.639003

2016-10-13T01:21:29

69,142,991

0

null

UTF-8

R

false

1,441

r

learn lattice.R

getwd() #y~x|A*B means display relationship between numeric variables x&y separately for every combination of factors A,B library(lattice) attach(mtcars) # create factors with value labels str(mtcars) head(mtcars,3) gearf=factor(gear,levels=c(3,4,5),labels=c("3gears","4gears","5gears")) table(gearf) summary(cyl) table(cyl) cylf=factor(cyl,levels=c(4,6,8),labels=c("4cyl","6cyl","8cyl")) table(cylf) #kernel density plot densityplot(~mpg,main="Density Plot",xlab="Miles per Gallon") #Kernel density plot by factor levels densityplot(~mpg|cylf,main="density plot by number of cyls",xlab="miles per gallon") #kernel density plot by factor level(alternate method densityplot(~mpg|cylf,layout=c(1,3)) #boxplot for each combination of two factors bwplot(cylf~mpg|gearf,ylab="cylinders",xlab="miles per gallon",main="mileage by cylinders and gears",layout=c(1,3)) #scatterplots for each combination of two factors xyplot(mpg~wt|cylf,layout=c(1,3),ylab="miles per gallon", xlab="Car Weight") #3D scatter plot by factor level cloud(mpg~wt*qsec|cylf,main="3D scatter plot by cylinders") #dotplot forcombination of two factors dotplot(cylf~mpg|gearf) #scatterplot matrix names(mtcars) splom(mtcars[c(1,3,4,5,6)],) #smooothen the graph smooth=function(x,y){ panel.xyplot(x,y) panel.loess(x,y) } hpc=cut(hp,3) xyplot(mpg~wt|hpc,scales=list(cex=0.8,col="red"),panel=smooth,xlab="car wt",ylab="miles per gallon")

fe620ec168b86a5e56342d4d0db7983505bb4074

b2f61fde194bfcb362b2266da124138efd27d867

/code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Database/Miller-Marin/trafficlight-controller/tlc02-nonuniform-depth-48/tlc02-nonuniform-depth-48.R

2fb97624991ddbf68d1007c88a1a69b6b1938944

[]

no_license

arey0pushpa/dcnf-autarky

e95fddba85c035e8b229f5fe9ac540b692a4d5c0

a6c9a52236af11d7f7e165a4b25b32c538da1c98

refs/heads/master

2021-06-09T00:56:32.937250

2021-02-19T15:15:23

136,440,042

0

null

UTF-8

R

false

78

r

tlc02-nonuniform-depth-48.R

1e0c557ad954c2663f2c790d1f284f4b tlc02-nonuniform-depth-48.qdimacs 11222 29588

b742a166ca5b12280121cd112e1de34e05811854

b2d074c532e4077987d1452d79622eeda753d158

/kNNImputationNonRandom.R

d1000a5be0df6443de61f6850bc6fbf77f17d8c6

[]

no_license

Alex-Nguyen/CS5331R

f7c477b68acc96f7ab0fbc6d3ef5a256d1627c3f

c2fb18ac0520e2e2a5d595df41338a49535e1d9b

refs/heads/master

2021-09-06T17:13:14.148598

2018-02-08T20:45:51

104,676,043

0

null

UTF-8

R

false

1,915

r

kNNImputationNonRandom.R

original_data <-iris set.seed(104) ####### Set initial parameters portion <-0.2 # percent of missing values to occupy the data. 0.02 = 2 % training_size <-0.7 # percent of data for training data_length <-nrow(original_data) missing_data <-original_data id <-portion*data_length missing_data[1:id,'Petal.Length'] <-NA missing_data # missing_data <-knnImputation(missing_data) #impute missing data with mean # missing_data$Petal.Length[is.na(missing_data$Petal.Length)] <-mean(missing_data$Petal.Length, na.rm = TRUE) missing_data <-knnImputation(missing_data) # #root mean square between imputed and true values rmse = sqrt(mean( (original_data$Petal.Length - missing_data$Petal.Length)^2, na.rm = TRUE) ) print("RMSE") rmse #Random splitting of iris data as 70% train and 30%test datasets #first we normalize whole dataset indexes <- sample(1:nrow(iris), floor(training_size*nrow(iris))) iris.train <- iris[indexes,-5] iris.train.target <- iris[indexes,5] iris.test <- iris[-indexes,-5] iris.test.target <- iris[-indexes,5] original_prediction <- knn(train=iris.train, test=iris.test, cl=iris.train.target, k=3) confusion_matrix <- table(iris.test.target, original_prediction) accuracy <- (sum(diag(confusion_matrix)))/sum(confusion_matrix) accuracy set.seed(103) indexes_imputed <- sample(1:nrow(missing_data), floor(training_size*nrow(missing_data))) iris.imputed.train <- missing_data[indexes_imputed,-5] iris.imputed.train.target <- missing_data[indexes_imputed,5] iris.imputed.test <- missing_data[-indexes_imputed,-5] iris.imputed.test.target <- missing_data[-indexes_imputed,5] imputed_prediction <- knn(train=iris.imputed.train, test=iris.imputed.test, cl=iris.imputed.train.target, k=3) imputed_confusion_matrix <- table(iris.imputed.test.target, imputed_prediction) imputed_confusion_matrix imputed.accuracy <- (sum(diag(imputed_confusion_matrix)))/sum(imputed_confusion_matrix) imputed.accuracy

a6678e50d2bb8ae9c1b37dacdd4dd70bf08ed6bb

4af4d40aaf9ce8311c75774d41be1256bb5730c7

/R/data.R

4d1fb663ea911f82c08af9f8b4cdcbbdff9af3fb

[]

no_license

zhgarfield/violationsandpunishmentsdata

69376879d3dd0932ff40eedfabc14963e4436014

5bb456f8bd52298fe1772760498473470f8c7a4b

refs/heads/master

2023-04-12T06:27:17.070753

2023-03-15T09:38:12

552,007,083

0

null

UTF-8

R

false

5,863

r

data.R

#' @title violations and punishments data #' @description Primary data set of researcher-coded punishment types, SCCS socioecological predictor variables, and phylogenetic tree. #' @format A data frame with 131 rows and 14 variables: #' \describe{ #' \item{\code{SCCS_NAME}}{character SCCS culture name associated with ethnographic document.} #' \item{\code{EHRAF_NAME}}{character HRAF culture name associated with ethnographic document.} #' \item{\code{SCCS_ID}}{double SCCS identificaiton number of society associated with ethnographic document.} #' \item{\code{Rape_viol}}{double Evidence for a violation of norms against rape.} #' \item{\code{Rape_SN_present}}{double Evidence for punishment of a violation of norms against rape.} #' \item{\code{War_viol}}{double Evidence for violation of norms against war cowardice.} #' \item{\code{War_SN_present}}{double Evidence for punishment of a violation of norms against war cowardice.} #' \item{\code{Religion_viol}}{double Evidence for violation of religous norms.} #' \item{\code{Religion_SN_present}}{double Evidence for punishment of a violation of religous norms.} #' \item{\code{Food_viol}}{double Evidence for a violation of food related or food sharing norms.} #' \item{\code{Food_SN_present}}{double Evidence for punishment of a violation of food related or food sharing norms.} #' \item{\code{Adultery_viol}}{double Evidence for a violation of norms against adultry.} #' \item{\code{Adultery_SN_present}}{double Evidence for punishment of a violation of norms against adultry.} #' \item{\code{Reputation_SN_present}}{double Evidence of reputational sanctions, where reputations sanctions are generally expected or specific instance of community endorsed reputational damage (more than gossip, results in net cost or general devaluation of violator). Coded as 1 for evidence for, 0 for no evidence.} #' \item{\code{Material_SN_present}}{double Generally expected or specific instance of community endorsed outcome that imposes direct economic or material costs on violator as a result of their violation. Coded as 1 for evidence for, 0 for no evidence.} #' \item{\code{Physical_SN_present}}{double Generally expected or specific instance of community endorsed outcome that results in a specific instance of physical harm or restraint as a result of their violation (not revenge). Coded as 1 for evidence for, 0 for no evidence.} #' \item{\code{Execution_SN_present}}{double Generally expected or specific instance of community endorsed outcome that results in the death of the violator as a result of their violation (not murder). Coded as 1 for evidence for, 0 for no evidence.} #' \item{\code{soc_strat}}{double Recoded SCCS V158 Social stratification. Coded as 1 for "Stratified", 0 for "Egalitarian".} #' \item{\code{storage}}{double Recoded SCCS V20 Food storage. Coded as 1 for present, 0 for absent.} #' \item{\code{husb}}{double Recoded SCCS V5 Animal husbandry - contribution to food supply. Coded as 1 for "None", 2 for "Present, not food source", 3 for "< 10% Food Supply", 4 for "< 50% Food supply", and 5 for "> 50% Food supply".} #' \item{\code{hunt}}{double Recoded SCCS V9 Hunting - contribution to food supply. Coded as 1 for "None", 2 for "< 10% of Food supply", 3 for "<50%,andlessthan any other single source", 4 for "<50%,andmorethan any other single source", 5 for ">50%".} #' \item{\code{comm_size}}{double Recoded SCCS V63 Community size. Coded as 1 for "< 50", 2 for "50-99", 3 for "100-199", 4 for "200-399", 5 for "400-999", 6 for "1,000-4,999", 7 for "5,000-49,999", and 8 for "> 50,000".} #' \item{\code{trade}}{double Recoded SCCS V1 Intercommunity trade as food source. Coded as 0 for "Minimal/Absent", 1 for "Present".} #' \item{\code{tree_name}}{character Phylogenetic tree name.} #'} "violpundata" #' @title punishments data (long form) #' @description A long-form version of the data of norm violation, punishments, and codings for all cultures. #' @format A data frame with 2620 rows and 5 variables: #' \describe{ #' \item{\code{HRAF_ID}}{character HRAF OWC Culture ID.} #' \item{\code{Coding}}{double Coding for culture by variable. Coded as 1 for evidence for, 0 for no evidence.} #' \item{\code{Domain}}{character Domain of norm violation type being coded.} #' \item{\code{Sanction}}{character Punishment type being coded.} #' \item{\code{Coding_label}}{character Coding for culture by varable, with text as label.} #'} #' "punishments_data_long" #' @title culture map data #' @description A data frame for producing a map of societies in the sample. #' @format A data frame with 131 rows and 4 variables: #' \describe{ #' \item{\code{HRAF_ID}}{character HRAF OWC Culture ID.} #' \item{\code{Subsistence Type}}{character HRAF subsistence type for society.} #' \item{\code{latitude}}{double Latitude for society location.} #' \item{\code{longitude}}{double Longitude for society location.} #'} #' "culturemapdata" #' @title document data #' @description A data frame of document-level metadata. #' @format A data frame with 131 rows and 6 variables: #' \describe{ #' \item{\code{EHRAF_NAME}}{character eHRAF culture name.} #' \item{\code{document_ID}}{character eHRAF document ID for document.} #' \item{\code{culture_ID}}{double eHRAF OWC ID for culture.} #' \item{\code{document_publication_date}}{integer Document publication year.} #' \item{\code{document_page_count}}{integer Documentn page count.} #' \item{\code{female_coauthor_present}}{integer Presence of a female author or co-author. Coded as 1 for present, 0 for absent.} #' \item{\code{tree_order}}{integer Order of societies in phylogentic tree.} #'} #' "documentdata" #' @title phylogenetic tree #' @description A phylogentic tree in list form consisting of 254 edges. #' @format A list of four vectors. # #' "tree"

9078d60c0c43dafd6186c1e61bb51b45def2c90e

198aafbe613df9a2cad68e70329b4cb133572018

/R_passion_tool2.R

7726a13835291661351256ad985465b4b0b3a81a

[]

no_license

josemtnzjmnz/PASSION_WDM_planner

332972968e8b6713f59d357c57cd42fe7403a01d

8e27457d96946e5c8c4b6cdf845caae755a6da18

refs/heads/main

2023-06-01T14:43:00.083688

2021-06-16T08:51:47

377,280,190

0

null

2021-06-15T20:01:38

2021-06-15T20:01:37

null

UTF-8

R

false

36,999

r

R_passion_tool2.R

# EU H2020 PASSION # Planning tool # Jose Alberto Hernandez # May 2021 # Inputs: # Network topology and traffic (nodesLabeling and crossmatrix) # Passion OSNR characterisation for lightpaths # Passion cost values # Output: # Lightpaths, both primary and secondary, and their allocation in the fibre/wavelengths (First-Fit) # Node dimensioning (number of ROADM degrees and Passion S-BVTs) # Cost per node and total # Required libraries library(igraph) setwd("D:/github_projects/PASSION_jose") #setwd("~/Google Drive/Research/Proyectos/PASSION_jose") #setwd("~/github_projects") rm(list=ls()) options(warn=-1) # Auxiliar functions: # Graph preparation get_gPass <- function(nodes.df, connectivity.mat, distCoeff = 1) { gPass = graph_from_adjacency_matrix(connectivity.mat, mode = c("undirected"), weighted = TRUE, diag = TRUE, add.colnames = NULL, add.rownames = NA) V(gPass)$name = paste(nodes.df$Nodes,nodes.df$Types,sep="_") V(gPass)$type = as.character(nodes.df$Types) E(gPass)$name = paste(get.edgelist(gPass)[,1],get.edgelist(gPass)[,2],sep="--") # Removing HL5s gPass = delete_vertices(gPass, V(gPass)[which(V(gPass)$type=="HL5")]) return(gPass) } # Main code alpha = 1 # Loading topology print("Loading Topology and OSNR configuration") # Choose nodesLabeling_Germany.csv, nodesLabeling_Tokyo.csv, nodesLabeling_Milano.csv, nodesLabeling_Mexico_short.csv, nodes.df = read.csv(file="nodesLabeling_Tokyo.csv", sep=";", header=F); colnames(nodes.df) = c("Nodes","Types","Traffic") nodes.df$Types = as.character(nodes.df$Types) # Choose crossMatrix_Germany.csv, crossMatrix_Tokyo.csv, crossMatrix_Milano.csv, crossMatrix_Mexico_short.csv connectivity.mat = alpha*as.matrix(read.csv(file="crossMatrix_Tokyo.csv", sep = ";",header=F)) nodes.df[which(nodes.df$Types=="HL5"),"Types"] = "HL5" nodes.df[which(nodes.df$Types=="HL4"),"Types"] = "HL4" nodes.df[which(nodes.df$Types=="HL3"),"Types"] = "HL3" nodes.df[which(nodes.df$Types=="HL2"),"Types"] = "HL12" nodes.df$Types = factor(nodes.df$Types) rownames(nodes.df)=paste(nodes.df$Nodes,nodes.df$Types,sep="_"); colnames(connectivity.mat)=paste(nodes.df$Nodes,nodes.df$Types,sep="_"); rownames(connectivity.mat)=paste(nodes.df$Nodes,nodes.df$Types,sep="_"); # OSNR values osnr_25G.mat = read.csv("osnr_25_oh_fec.csv",header=TRUE,sep=";") osnr_40G.mat = read.csv("osnr_40_oh_fec.csv",header=TRUE,sep=";") osnr_50G.mat = read.csv("osnr_50_oh_fec.csv",header=TRUE,sep=";") # Load graph gPass = get_gPass(nodes.df,connectivity.mat) N_HL12s = length(which(V(gPass)$type=="HL12")) N_HL3s = length(which(V(gPass)$type=="HL3")) N_HL4s = length(which(V(gPass)$type=="HL4")) if ("Traffic" %in% colnames(nodes.df)) { demand_matrix = nodes.df$Traffic Traff = mean(nodes.df$Traffic) } else { Traff = 600; # 600G per HL4 toward HL12 demand_matrix = rnorm(N_HL4s,mean=Traff,sd=0.2*Traff) } FFallocation = as.data.frame(matrix(NA, nrow=length(E(gPass)), ncol=40*ceiling(ceiling(0.6*Traff/50*length(V(gPass)))/40))) Sallocation = FFallocation; Dallocation = FFallocation nlambdas_HL12s = 0*(1:length(V(gPass)[which(V(gPass)$type=="HL12")])) nlambdas_HL3s = 0*(1:length(V(gPass)[which(V(gPass)$type=="HL3")])) nlambdas_HL4s = 0*(1:length(V(gPass)[which(V(gPass)$type=="HL4")])) speed_HL3s = 0*(1:length(V(gPass)[which(V(gPass)$type=="HL3")])) speed_HL4s = 0*(1:length(V(gPass)[which(V(gPass)$type=="HL4")])) Results.df = data.frame(matrix(c(1:18),nrow=1,ncol=18), stringsAsFactors = FALSE) colnames(Results.df) = c("Source","Destination","prim_sec", "distance_KM","distance_hops", "N_HL5s","N_HL4s","N_HL3s","N_HL12s", "OSNR_e2e","OSNR_req50G","OSNR_req40G","OSNR_req25G", "Can_50G","Can_40G","Can_25G", "FullPath","LinksDistance") E(gPass)$traff = 0; ll_traff = E(gPass)$traff ll_links = E(gPass)$name print("Finding lightpaths for HL4 nodes") n_exec = 0; for (HL4index in (1:N_HL4s)) { #N_HL4s)) { n_exec = n_exec + 1; gPass = get_gPass(nodes.df,connectivity.mat) HL12s = V(gPass)[which(V(gPass)$type=="HL12")] HL3s = V(gPass)[which(V(gPass)$type=="HL3")] HL4s = V(gPass)[which(V(gPass)$type=="HL4")] HL5s = V(gPass)[which(V(gPass)$type=="HL5")] E(gPass)$traff = ll_traff Source_node = V(gPass)[which(V(gPass)$type=="HL4")][HL4index] Source = V(gPass)[which(V(gPass)$type=="HL4")][HL4index]$name aa_minhops = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath', weights = NA) aa_minKm = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath') aux_HL12_winner = unlist(lapply(aa_minhops$epath,length))*1e5 for (ii in c(1:length(aux_HL12_winner))){ aux_HL12_winner[ii] = aux_HL12_winner[ii] + sum(aa_minKm$epath[[ii]]$weight) } HL12_winner_prim = which(aux_HL12_winner==min(aux_HL12_winner)) # Primary path HL12_winner_prim = order(unlist(lapply(aa_minhops$epath,length)),decreasing = F)[1] Destination = HL12s[HL12_winner_prim]$name Destination_node = HL12s[HL12_winner_prim] aa_primary_e = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'epath', weights = NA) aa_primary_v = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'vpath', weights = NA) E(gPass)$traff = ll_traff E(gPass)[which(E(gPass) %in% aa_primary_e$epath[[1]])]$traff = E(gPass)[which(E(gPass) %in% aa_primary_e$epath[[1]])]$traff +1 ll_traff = E(gPass)$traff if (n_exec == 1) { append_var = F; }else{ append_var = T; } write.table( t(aa_primary_v$vpath[[1]]$name), file="primary_path.csv", append = append_var, sep=';', row.names=F, col.names=F ) #write.csv(aa_primary_v$vpath[[1]]$name, file = "primary_path.csv", row.names = FALSE) # guarda un archivo csv PrimaryPath = paste0(aa_primary_v$vpath[[1]]$name,collapse="++++") Node_sequence = PrimaryPath # node sequence metrics disthops_winner = length(aa_primary_v$vpath[[1]])-1 HL5_hops = sum(aa_primary_v$vpath[[1]] %in% HL5s) HL4_hops = sum(aa_primary_v$vpath[[1]] %in% HL4s) HL3_hops = sum(aa_primary_v$vpath[[1]] %in% HL3s) HL12_hops = sum(aa_primary_v$vpath[[1]] %in% HL12s) # 1 amplifier per link dist_links = aa_primary_e$epath[[1]]$weight distKm_winner = sum(aa_primary_e$epath[[1]]$weight) Link_sequence = paste0(dist_links,collapse = " ++++ ") # in km osnr_e2e = -10*log10(sum(10^(-0.1*(58-6-0.25*dist_links)))) # similar to 58-0.25*sum(dist_links)-6-10*log10(length(dist_links)-1) , but the above is exact # osnr of path osnr_req_50G = osnr_50G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_40G = osnr_40G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_25G = osnr_25G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] can50G = ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE) can40G = ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE) can25G = ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE) if (can50G == TRUE) { maxSpeed = 50 } else { if (can40G == TRUE) { maxSpeed = 40 } else { if (can25G == TRUE) { maxSpeed = 25 } else { maxSpeed = 0 } } } nlambdas = ceiling(demand_matrix[HL4index]/maxSpeed) # we do the first fit allocation eindex = which(E(gPass) %in% aa_primary_e$epath[[1]]) conditionFF = FALSE elambda = 0 while (conditionFF == FALSE) { elambda = elambda + 1 if (prod(is.na(FFallocation[eindex,(elambda:(elambda+nlambdas-1))])) == 1) { # hueco libre FFallocation[eindex, elambda:(elambda+nlambdas-1)] = paste("lightpath",Source,Destination, sep = "++") Sallocation[eindex, elambda:(elambda+nlambdas-1)] = Source Dallocation[eindex, elambda:(elambda+nlambdas-1)] = Destination conditionFF = TRUE } else { # ocupado, sigo buscando conditionFF = FALSE } } speed_HL4s[HL4index] = maxSpeed nlambdas_HL4s[HL4index] = nlambdas_HL4s[HL4index] + nlambdas nlambdas_HL12s[which(HL12s$name == Destination)] = nlambdas_HL12s[which(HL12s$name == Destination)] + nlambdas datapoint = c(Source, Destination, "Primary_path", distKm_winner, disthops_winner, HL5_hops, HL4_hops, HL3_hops, HL12_hops, osnr_e2e, osnr_req_50G, osnr_req_40G, osnr_req_25G, ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE), Node_sequence,Link_sequence) Results.df = rbind(Results.df,datapoint) colnames(Results.df) = c("Source","Destination","prim_sec", "distance_KM","distance_hops", "N_HL5s","N_HL4s","N_HL3s","N_HL12s", "OSNR_e2e","OSNR_req50G","OSNR_req40G","OSNR_req25G", "Can_50G","Can_40G","Can_25G", "FullPath","LinksDistance") # Plan-B secondary path all_weights_orig = E(gPass)$weight weights_aux = all_weights_orig weights_aux[(E(gPass) %in% aa_primary_e$epath[[1]])] = weights_aux[(E(gPass) %in% aa_primary_e$epath[[1]])] + 1000 E(gPass)$weight = weights_aux aa_minKm_plan_b = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath') aux_HL12_winner_sec_plan_b = unlist(lapply(aa_minKm_plan_b$epath,length))*1e5 for (ii in c(1:length(aux_HL12_winner_sec_plan_b))){ aux_HL12_winner_sec_plan_b[ii] = aux_HL12_winner_sec_plan_b[ii] + sum(aa_minKm_plan_b$epath[[ii]]$weight) } HL12_winner_sec_planb = which(aux_HL12_winner_sec_plan_b==min(aux_HL12_winner_sec_plan_b)) if (HL12_winner_prim == HL12_winner_sec_planb) { HL12_winner_sec_planb = order(aux_HL12_winner_sec_plan_b,decreasing = F)[2] } Destination = HL12s[HL12_winner_sec_planb]$name Destination_node = HL12s[HL12_winner_sec_planb] aa_secondary_e_planb = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'epath') aa_secondary_v_planb = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'vpath') write.table( t(aa_secondary_v_planb$vpath[[1]]$name), file="secondary_path.csv", append = append_var, sep=';', row.names=F, col.names=F ) SecondaryPath_PlanB = paste0(aa_secondary_v_planb$vpath[[1]]$name,collapse="++++") Destination = HL12s[HL12_winner_sec_planb]$name Node_sequence = SecondaryPath_PlanB # node sequence metrics disthops_winner = length(aa_secondary_v_planb$vpath[[1]])-1 HL5_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL5s) HL4_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL4s) HL3_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL3s) HL12_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL12s) # 1 amplifier per link dist_links = aa_secondary_e_planb$epath[[1]]$weight dist_links[which(dist_links>999)] = dist_links[which(dist_links>999)] -1000 Nshared_links = floor(sum(aa_secondary_e_planb$epath[[1]]$weight)/1000) Nshared_nodes = sum(as.numeric(aa_secondary_v_planb$vpath[[1]] %in% aa_primary_v$vpath[[1]]))-1 distKm_winner = sum(dist_links) Link_sequence = paste0(dist_links,collapse = " ++++ ") # in km osnr_e2e = -10*log10(sum(10^(-0.1*(58-6-0.25*dist_links)))) # osnr of path osnr_req_50G = osnr_50G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_40G = osnr_40G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_25G = osnr_25G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] # Secondary path, link and node disjoint E(gPass)$weight = all_weights_orig gPass = delete_vertices(gPass, aa_primary_v$vpath[[1]][2:length(aa_primary_v$vpath[[1]])]) HL12s = V(gPass)[which(V(gPass)$type=="HL12")] HL3s = V(gPass)[which(V(gPass)$type=="HL3")] HL4s = V(gPass)[which(V(gPass)$type=="HL4")] HL5s = V(gPass)[which(V(gPass)$type=="HL5")] aa_secondary_e = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath', weights=NA) aa_secondary_minKm_e = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath') aux_HL12_winner_sec = unlist(lapply(aa_secondary_minKm_e$epath,length))*1e5 for (ii in c(1:length(aux_HL12_winner_sec))){ aux_HL12_winner_sec[ii] = aux_HL12_winner_sec[ii] + sum(aa_secondary_minKm_e$epath[[ii]]$weight) } HL12s_winner_sec = which(aux_HL12_winner_sec==min(aux_HL12_winner_sec)) if (min(unlist(lapply(aa_secondary_e$epath,length)))==0) { # node unreachable datapoint = c(Source, Destination, paste("Secondary_path_shared_",Nshared_nodes,"nodes",Nshared_links,"links",sep=""), distKm_winner, disthops_winner, HL5_hops, HL4_hops, HL3_hops, HL12_hops, osnr_e2e, osnr_req_50G, osnr_req_40G, osnr_req_25G, ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE), Node_sequence,Link_sequence) Results.df = rbind(Results.df,datapoint) } else { HL12s_winner_sec = order(unlist(lapply(aa_secondary_e$epath,length)),decreasing = F)[1] Destination = V(gPass)[which(V(gPass)$type=="HL12")][HL12s_winner_sec]$name aa_secondary_e = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")][HL12s_winner_sec], output = 'epath', weights = NA) aa_secondary_v = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")][HL12s_winner_sec], output = 'vpath', weights = NA) SecondaryPath = paste0(aa_secondary_v$vpath[[1]]$name,collapse="++++") Node_sequence = SecondaryPath # node sequence metrics disthops_winner = length(aa_secondary_v$vpath[[1]])-1 HL5_hops = sum(aa_secondary_v$vpath[[1]] %in% HL5s) HL4_hops = sum(aa_secondary_v$vpath[[1]] %in% HL4s) HL3_hops = sum(aa_secondary_v$vpath[[1]] %in% HL3s) HL12_hops = sum(aa_secondary_v$vpath[[1]] %in% HL12s) # 1 amplifier per link dist_links = aa_secondary_e$epath[[1]]$weight distKm_winner = sum(aa_secondary_e$epath[[1]]$weight) Link_sequence = paste0(dist_links,collapse = " ++++ ") # in km osnr_e2e = -10*log10(sum(10^(-0.1*(58-6-0.25*dist_links)))) # osnr of path osnr_req_50G = osnr_50G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_40G = osnr_40G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_25G = osnr_25G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] datapoint = c(Source, Destination, "Secondary_path_totally_disjoint", distKm_winner, disthops_winner, HL5_hops, HL4_hops, HL3_hops, HL12_hops, osnr_e2e, osnr_req_50G, osnr_req_40G, osnr_req_25G, ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE), Node_sequence,Link_sequence) Results.df = rbind(Results.df,datapoint) } } print("Finding lightpaths for HL3 nodes") for (HL3index in (1:N_HL3s)) { #N_HL4s)) { gPass = get_gPass(nodes.df,connectivity.mat) HL12s = V(gPass)[which(V(gPass)$type=="HL12")] HL3s = V(gPass)[which(V(gPass)$type=="HL3")] HL4s = V(gPass)[which(V(gPass)$type=="HL4")] HL5s = V(gPass)[which(V(gPass)$type=="HL5")] E(gPass)$traff = ll_traff Source_node = V(gPass)[which(V(gPass)$type=="HL3")][HL3index] Source = V(gPass)[which(V(gPass)$type=="HL3")][HL3index]$name aa_minhops = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath', weights = NA) aa_minKm = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath') aux_HL12_winner = unlist(lapply(aa_minhops$epath,length))*1e5 for (ii in c(1:length(aux_HL12_winner))){ aux_HL12_winner[ii] = aux_HL12_winner[ii] + sum(aa_minKm$epath[[ii]]$weight) } HL12_winner_prim = which(aux_HL12_winner==min(aux_HL12_winner)) # Primary path HL12_winner_prim = order(unlist(lapply(aa_minhops$epath,length)),decreasing = F)[1] Destination = HL12s[HL12_winner_prim]$name Destination_node = HL12s[HL12_winner_prim] aa_primary_e = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'epath', weights = NA) aa_primary_v = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'vpath', weights = NA) E(gPass)$traff = ll_traff E(gPass)[which(E(gPass) %in% aa_primary_e$epath[[1]])]$traff = E(gPass)[which(E(gPass) %in% aa_primary_e$epath[[1]])]$traff +1 ll_traff = E(gPass)$traff PrimaryPath = paste0(aa_primary_v$vpath[[1]]$name,collapse="++++") Node_sequence = PrimaryPath # node sequence metrics disthops_winner = length(aa_primary_v$vpath[[1]])-1 HL5_hops = sum(aa_primary_v$vpath[[1]] %in% HL5s) HL4_hops = sum(aa_primary_v$vpath[[1]] %in% HL4s) HL3_hops = sum(aa_primary_v$vpath[[1]] %in% HL3s) HL12_hops = sum(aa_primary_v$vpath[[1]] %in% HL12s) # 1 amplifier per link dist_links = aa_primary_e$epath[[1]]$weight distKm_winner = sum(aa_primary_e$epath[[1]]$weight) Link_sequence = paste0(dist_links,collapse = " ++++ ") # in km osnr_e2e = -10*log10(sum(10^(-0.1*(58-6-0.25*dist_links)))) # osnr of path osnr_req_50G = osnr_50G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_40G = osnr_40G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_25G = osnr_25G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] can50G = ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE) can40G = ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE) can25G = ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE) if (can50G == TRUE) { maxSpeed = 50 } else { if (can40G == TRUE) { maxSpeed = 40 } else { if (can25G == TRUE) { maxSpeed = 25 } else { maxSpeed = 0 } } } nlambdas = ceiling(demand_matrix[HL3index]/maxSpeed) # we do the first fit allocation eindex = which(E(gPass) %in% aa_primary_e$epath[[1]]) conditionFF = FALSE elambda = 0 while (conditionFF == FALSE) { elambda = elambda + 1 if (prod(is.na(FFallocation[eindex,(elambda:(elambda+nlambdas-1))])) == 1) { # hueco libre FFallocation[eindex, elambda:(elambda+nlambdas-1)] = paste("lightpath",Source,Destination, sep = "++") Sallocation[eindex, elambda:(elambda+nlambdas-1)] = Source Dallocation[eindex, elambda:(elambda+nlambdas-1)] = Destination conditionFF = TRUE } else { # ocupado, sigo buscando conditionFF = FALSE } } speed_HL3s[HL3index] = maxSpeed nlambdas_HL3s[HL3index] = nlambdas_HL3s[HL3index] + nlambdas nlambdas_HL12s[which(HL12s$name == Destination)] = nlambdas_HL12s[which(HL12s$name == Destination)] + nlambdas datapoint = c(Source, Destination, "Primary_path", distKm_winner, disthops_winner, HL5_hops, HL4_hops, HL3_hops, HL12_hops, osnr_e2e, osnr_req_50G, osnr_req_40G, osnr_req_25G, ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE), Node_sequence,Link_sequence) Results.df = rbind(Results.df,datapoint) colnames(Results.df) = c("Source","Destination","prim_sec", "distance_KM","distance_hops", "N_HL5s","N_HL4s","N_HL3s","N_HL12s", "OSNR_e2e","OSNR_req50G","OSNR_req40G","OSNR_req25G", "Can_50G","Can_40G","Can_25G", "FullPath","LinksDistance") # Plan-B secondary path all_weights_orig = E(gPass)$weight weights_aux = all_weights_orig weights_aux[(E(gPass) %in% aa_primary_e$epath[[1]])] = weights_aux[(E(gPass) %in% aa_primary_e$epath[[1]])] + 1000 E(gPass)$weight = weights_aux aa_minKm_plan_b = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath') aux_HL12_winner_sec_plan_b = unlist(lapply(aa_minKm_plan_b$epath,length))*1e5 for (ii in c(1:length(aux_HL12_winner_sec_plan_b))){ aux_HL12_winner_sec_plan_b[ii] = aux_HL12_winner_sec_plan_b[ii] + sum(aa_minKm_plan_b$epath[[ii]]$weight) } HL12_winner_sec_planb = which(aux_HL12_winner_sec_plan_b==min(aux_HL12_winner_sec_plan_b)) if (HL12_winner_prim == HL12_winner_sec_planb) { HL12_winner_sec_planb = order(aux_HL12_winner_sec_plan_b,decreasing = F)[2] } Destination = HL12s[HL12_winner_sec_planb]$name Destination_node = HL12s[HL12_winner_sec_planb] aa_secondary_e_planb = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'epath') aa_secondary_v_planb = get.shortest.paths(gPass, from = Source_node, to = Destination_node, output = 'vpath') SecondaryPath_PlanB = paste0(aa_secondary_v_planb$vpath[[1]]$name,collapse="++++") Destination = HL12s[HL12_winner_sec_planb]$name Node_sequence = SecondaryPath_PlanB # node sequence metrics disthops_winner = length(aa_secondary_v_planb$vpath[[1]])-1 HL5_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL5s) HL4_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL4s) HL3_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL3s) HL12_hops = sum(aa_secondary_v_planb$vpath[[1]] %in% HL12s) # 1 amplifier per link dist_links = aa_secondary_e_planb$epath[[1]]$weight dist_links[which(dist_links>999)] = dist_links[which(dist_links>999)] -1000 Nshared_links = floor(sum(aa_secondary_e_planb$epath[[1]]$weight)/1000) Nshared_nodes = sum(as.numeric(aa_secondary_v_planb$vpath[[1]] %in% aa_primary_v$vpath[[1]]))-1 distKm_winner = sum(dist_links) Link_sequence = paste0(dist_links,collapse = " ++++ ") # in km osnr_e2e = -10*log10(sum(10^(-0.1*(58-6-0.25*dist_links)))) # osnr of path osnr_req_50G = osnr_50G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_40G = osnr_40G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_25G = osnr_25G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] # Secondary path E(gPass)$weight = all_weights_orig gPass = delete_vertices(gPass, aa_primary_v$vpath[[1]][2:length(aa_primary_v$vpath[[1]])]) HL12s = V(gPass)[which(V(gPass)$type=="HL12")] HL3s = V(gPass)[which(V(gPass)$type=="HL3")] HL4s = V(gPass)[which(V(gPass)$type=="HL4")] HL5s = V(gPass)[which(V(gPass)$type=="HL5")] aa_secondary_e = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath', weights=NA) aa_secondary_minKm_e = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")], output = 'epath') aux_HL12_winner_sec = unlist(lapply(aa_secondary_minKm_e$epath,length))*1e5 for (ii in c(1:length(aux_HL12_winner_sec))){ aux_HL12_winner_sec[ii] = aux_HL12_winner_sec[ii] + sum(aa_secondary_minKm_e$epath[[ii]]$weight) } HL12s_winner_sec = which(aux_HL12_winner_sec==min(aux_HL12_winner_sec)) if (min(unlist(lapply(aa_secondary_e$epath,length)))==0) { # node unreachable datapoint = c(Source, Destination, paste("Secondary_path_shared_",Nshared_nodes,"nodes",Nshared_links,"links",sep=""), distKm_winner, disthops_winner, HL5_hops, HL4_hops, HL3_hops, HL12_hops, osnr_e2e, osnr_req_50G, osnr_req_40G, osnr_req_25G, ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE), Node_sequence,Link_sequence) Results.df = rbind(Results.df,datapoint) } else { HL12s_winner_sec = order(unlist(lapply(aa_secondary_e$epath,length)),decreasing = F)[1] Destination = V(gPass)[which(V(gPass)$type=="HL12")][HL12s_winner_sec]$name aa_secondary_e = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")][HL12s_winner_sec], output = 'epath', weights = NA) aa_secondary_v = get.shortest.paths(gPass, from = V(gPass)[which(V(gPass)$name==Source)], #from = V(gPass)[which(V(gPass)$type=="HL4")][HL4index], to = V(gPass)[which(V(gPass)$type=="HL12")][HL12s_winner_sec], output = 'vpath', weights = NA) SecondaryPath = paste0(aa_secondary_v$vpath[[1]]$name,collapse="++++") Node_sequence = SecondaryPath # node sequence metrics disthops_winner = length(aa_secondary_v$vpath[[1]])-1 HL5_hops = sum(aa_secondary_v$vpath[[1]] %in% HL5s) HL4_hops = sum(aa_secondary_v$vpath[[1]] %in% HL4s) HL3_hops = sum(aa_secondary_v$vpath[[1]] %in% HL3s) HL12_hops = sum(aa_secondary_v$vpath[[1]] %in% HL12s) # 1 amplifier per link dist_links = aa_secondary_e$epath[[1]]$weight distKm_winner = sum(aa_secondary_e$epath[[1]]$weight) Link_sequence = paste0(dist_links,collapse = " ++++ ") # in km osnr_e2e = -10*log10(sum(10^(-0.1*(58-6-0.25*dist_links)))) # osnr of path osnr_req_50G = osnr_50G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_40G = osnr_40G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] osnr_req_25G = osnr_25G.mat[HL4_hops+1,HL12_hops+HL3_hops+2] datapoint = c(Source, Destination, "Secondary_path_totally_disjoint", distKm_winner, disthops_winner, HL5_hops, HL4_hops, HL3_hops, HL12_hops, osnr_e2e, osnr_req_50G, osnr_req_40G, osnr_req_25G, ifelse(osnr_e2e>osnr_req_50G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_40G,TRUE,FALSE), ifelse(osnr_e2e>osnr_req_25G,TRUE,FALSE), Node_sequence,Link_sequence) Results.df = rbind(Results.df,datapoint) } } dimFFalloc = ceiling(length(which(colSums(is.na(FFallocation))<dim(FFallocation)[1]))/40)*40 FFallocation_final = FFallocation[,c(1:dimFFalloc)] Sallocation_final = Sallocation[,c(1:dimFFalloc)] Dallocation_final = Dallocation[,c(1:dimFFalloc)] heat_table = apply(FFallocation_final,2,is.na) heat_table2 = matrix(as.numeric(heat_table), nrow=dim(heat_table)[1], ncol=dim(heat_table)[2], byrow=F) heatmap(heat_table2, Colv = NA, Rowv = NA, scale="none", xlab="Freq. Slots", ylab="links", main="First-Fit allocation") colnames(Results.df) = c("Source","Destination","prim_sec", "distance_KM","distance_hops", "N_HL5s","N_HL4s","N_HL3s","N_HL12s", "OSNR_e2e","OSNR_req50G","OSNR_req40G","OSNR_req25G", "Can_50G","Can_40G","Can_25G", "FullPath","LinksDistance") print("Writing results in output files") Results.df = Results.df[-1,] write.csv(Results.df,file="lightpaths.csv") write.csv(FFallocation_final,file="FFlightpaths.csv") # Analysis of primary paths Results.df$Source = as.factor(Results.df$Source) Results.df$Destination = as.factor(Results.df$Destination) Results.df$prim_sec = as.factor(Results.df$prim_sec) Results.df$distance_KM = as.numeric(Results.df$distance_KM) Results.df$distance_hops = as.numeric(Results.df$distance_hops) Results.df$N_HL5s = as.numeric(Results.df$N_HL5s) Results.df$N_HL4s = as.numeric(Results.df$N_HL4s) Results.df$N_HL3s = as.numeric(Results.df$N_HL3s) Results.df$N_HL12s = as.numeric(Results.df$N_HL12s) Results.df$OSNR_e2e = as.numeric(Results.df$OSNR_e2e) Results.df$OSNR_req50G = as.numeric(Results.df$OSNR_req50G) Results.df$OSNR_req40G = as.numeric(Results.df$OSNR_req40G) Results.df$OSNR_req25G = as.numeric(Results.df$OSNR_req25G) Results.df[which(Results.df$OSNR_req50G>100),"OSNR_req50G"] = NA Results.df[which(Results.df$OSNR_req40G>100),"OSNR_req40G"] = NA Results.df[which(Results.df$OSNR_req25G>100),"OSNR_req25G"] = NA Results.df$Can_50G = as.logical(Results.df$Can_50G) Results.df$Can_40G = as.logical(Results.df$Can_40G) Results.df$Can_25G = as.logical(Results.df$Can_25G) Ppaths.df = Results.df[which(Results.df$prim_sec=="Primary_path"),] SecPaths.df = Results.df[-which(Results.df$prim_sec=="Primary_path"),] boxplot(Ppaths.df[,"distance_KM"], SecPaths.df[,"distance_KM"], main = "Distance (KM)", at = c(1,2), names = c("primary","secondary"), las = 2, col = c("green","red"), border = "brown", horizontal = FALSE, notch = TRUE ) boxplot(Ppaths.df[,"distance_hops"], SecPaths.df[,"distance_hops"], main = "# Hops", at = c(1,2), names = c("primary","secondary"), las = 2, col = c("green","red"), border = "brown", horizontal = FALSE, notch = TRUE ) boxplot(Ppaths.df[,"OSNR_e2e"], SecPaths.df[,"OSNR_e2e"], main = "# End-to-End OSNR (dB)", at = c(1,2), names = c("primary","secondary"), las = 2, col = c("green","red"), border = "brown", horizontal = FALSE, notch = TRUE ) Ppath_NA.df = na.omit(Ppaths.df) SecPath_NA.df = na.omit(SecPaths.df) # Node configuration gPass = get_gPass(nodes.df,connectivity.mat) HL12s = V(gPass)[which(V(gPass)$type=="HL12")] HL3s = V(gPass)[which(V(gPass)$type=="HL3")] HL4s = V(gPass)[which(V(gPass)$type=="HL4")] HL5s = V(gPass)[which(V(gPass)$type=="HL5")] Component_cost = read.csv("Passion_cost_components.csv",header=F, sep=";") # HL4 configuration HL4conf.df = data.frame(node_name = HL4s$name, nlambdas = nlambdas_HL4s, speed = speed_HL4s, deg = degree(gPass)[which(V(gPass)$type=="HL4")], degRoadm = NA, cost = rep(NA, length(HL4s))) for (ii in HL4s) { aux = which(E(gPass) %in% incident(gPass,V(gPass)[ii])) deg = 0 for (jj in 1:length(aux)) { mm = (matrix(FFallocation[aux[jj],],nrow=(dim(FFallocation)[2]/40),ncol=40,byrow=TRUE)) deg = deg + ceiling(sum(abs(as.numeric(apply(mm,1,is.na))-1))/40) } HL4conf.df[V(gPass)[ii]$name,"degRoadm"] = deg } HL4conf.df$cost = Component_cost[which(Component_cost=="ROADM_degree"),2] * apply(HL4conf.df[,c("deg","degRoadm")],1,max) + Component_cost[which(Component_cost=="SBVT"),2]/40 * HL4conf.df$nlambdas + Component_cost[which(Component_cost=="HL4_Router"),2] # HL3 configuration HL3conf.df = data.frame(node_name = HL3s$name, nlambdas = nlambdas_HL3s, speed = speed_HL3s, deg = degree(gPass)[which(V(gPass)$type=="HL3")], degRoadm = NA, cost = rep(NA, length(HL3s))) for (ii in HL3s) { aux = which(E(gPass) %in% incident(gPass,V(gPass)[ii])) deg = 0 for (jj in 1:length(aux)) { mm = (matrix(FFallocation[aux[jj],],nrow=(dim(FFallocation)[2]/40),ncol=40,byrow=TRUE)) deg = deg + ceiling(sum(abs(as.numeric(apply(mm,1,is.na))-1))/40) } HL3conf.df[V(gPass)[ii]$name,"degRoadm"] = deg } HL3conf.df$cost = Component_cost[which(Component_cost=="ROADM_degree"),2] * apply(HL3conf.df[,c("deg","degRoadm")],1,max) + Component_cost[which(Component_cost=="SBVT"),2]/40 * HL3conf.df$nlambdas + Component_cost[which(Component_cost=="HL4_Router"),2] # HL12 configuration HL12conf.df = data.frame(node_name = HL12s$name, nlambdas = nlambdas_HL12s, deg = degree(gPass)[which(V(gPass)$type=="HL12")], degRoadm = NA, cost = rep(NA,length(HL12s))) for (ii in HL12s) { aux = which(E(gPass) %in% incident(gPass,V(gPass)[ii])) deg = 0 for (jj in 1:length(aux)) { mm = (matrix(FFallocation[aux[jj],],nrow=(dim(FFallocation)[2]/40),ncol=40,byrow=TRUE)) deg = deg + ceiling(sum(abs(as.numeric(apply(mm,1,is.na))-1))/40) } HL12conf.df[V(gPass)[ii]$name,"degRoadm"] = deg } # cost = roadm-degree + S-BVTs + router HL12conf.df$cost = Component_cost[which(Component_cost=="ROADM_degree"),2] * apply(HL12conf.df[,c("deg","degRoadm")],1,max) + Component_cost[which(Component_cost=="SBVT"),2]/40 * HL12conf.df$nlambdas + Component_cost[which(Component_cost=="HL12_Router"),2] # Total cost TCO = sum(HL12conf.df$cost) + sum(HL3conf.df$cost) + sum(HL4conf.df$cost) write.csv(rbind(HL4conf.df[,colnames(HL4conf.df)[c(1:2,4:6)]],HL3conf.df[,colnames(HL3conf.df)[c(1:2,4:6)]],HL12conf.df), file = "NodeDesign.csv")

4128b90c3e8575be447f152bdc6f0d9128a562df

d121f587f7e0678030d33a4c5428e594c5978dad

/R/quant_txrevise.R

965adf4dc8c166628958eca01c439db4bfbfe4fc

[ "Apache-2.0" ]

permissive

kauralasoo/eQTLUtils

fcf0907721b3a8f19fe68e611cecb4f16d7a0c9d

26242562a4e244334fd9691d03bc1ef4d2d6c1d9

refs/heads/master

2023-03-05T19:10:45.247191

2023-03-03T13:33:08

149,779,618

4

2

null

UTF-8

R

false

758

r

quant_txrevise.R

constructTxreviseRowData <- function(phenotype_ids, transcript_meta){ #Split phenotype ids into components event_metadata = dplyr::data_frame(phenotype_id = phenotype_ids) %>% tidyr::separate(phenotype_id, c("gene_id", "txrevise_grp", "txrevise_pos", "transcript_id"), sep = "\\.", remove = FALSE) %>% dplyr::mutate(group_id = paste(gene_id, txrevise_pos, sep = "."), quant_id = paste(gene_id, txrevise_grp, txrevise_pos, sep = ".")) %>% dplyr::select(phenotype_id, quant_id, group_id, gene_id) #Extract gene metadata gene_meta = extractGeneMetadataFromBiomartFile(transcript_meta) %>% dplyr::select(-phenotype_id, -group_id, -quant_id) row_data = dplyr::left_join(event_metadata, gene_meta, by = "gene_id") return(row_data) }

d67e4bbb18bd81370f47e0fff20cdcdb9f65aa31

15011c6bec5eff7ab07b14b423879b022851c5a6

/TUCUMAN/SMT/Circuitos/grid.circuitos.smt.R

b5410ff0000afdd00954f9da18306c6607a7702b

[ "MIT" ]

permissive

shirosweets/geofacet_ARG

97ae4ee815ccf0cfa7c1a1acf7eb4ece64861e79

48685f8535ae628eeacff97e1a79511b8e139b65

refs/heads/master

2023-03-18T21:54:58.972109

2019-04-22T16:26:58

null

0

null

UTF-8

R

false

773

r

grid.circuitos.smt.R

SMT.circuitos <- data.frame( name = c("B15", "A15", "A13", "A12", "A14", "A16", "16", "15", "13", "12", "14", "A17", "17", "7A", "6", "5", "11", "18", "10", "B18", "7", "2", "1", "8", "2A", "1A", "A18", "A10", "8A", "3", "4", "9A", "19", "9", "20", "21", "22"), code = c("B15", "A15", "A13", "A12", "A14", "A16", "16", "15", "13", "12", "14", "A17", "17", "7A", "6", "5", "11", "18", "10", "B18", "7", "2", "1", "8", "2A", "1A", "A18", "A10", "8A", "3", "4", "9A", "19", "9", "20", "21", "22"), row = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9), col = c(2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 2, 6, 1, 3, 4, 5, 3, 4, 5, 2, 6, 3, 5, 4, 4, 3, 5, 4, 5, 5), stringsAsFactors = FALSE )

cd3d7ee8fd2866ef2d10a31e9d530ee4b07ef47c

beca8e699a02bf123aa98ee5eaacba9ad245aa8c

/T_32_Tables.R

b981e1321a414879647fc66d9d52bbe3f3deb730

[]

no_license

brandtn/R_Code_Misc

d7bd4539a07684c88a032836fc563842628e1426

cc7cdbef22dd75084892717361dd6240d924c7c0

refs/heads/master

2021-09-26T00:32:47.320529

2018-10-26T15:24:21

120,939,745

0

null

UTF-8

R

false

555

r

T_32_Tables.R

#Load libraries library("tidyverse") library("googlesheets") student_data <- gs_read(ss = gs_title("T_32")) glimpse(student_data) #function to convert date #take in x which is a 4 digit number convertdate { #convert x to a character #first charater is either 0 or 1 #0 = 19 #1 = 20 #Second two characters are the year #concate to first character transformation # set to x convert to intergar # Last/Fourth character conver to semseter # 2 = Winter # 4 = Spring # 6 = Summer # 8 = Fall #set to y #return x and y }

4bfb9b4705862e221c4c62d2a7efe0c6df62cd11

ac84d0a57c45731f36048895b604ea2721d5c8ba

/src/figure-cum-mul.R

7e05072f928316444dc805ca7aefbbf36d289dc3

[]

no_license

yshin12/llss-rz

de2a9e84a463243e8627e2936e21c940fc696c9f

d561bdbacd21a27f3ee34ebc1728ff5205fe5d80

refs/heads/master

2020-05-03T10:30:56.360791

2019-11-01T13:22:34

178,581,261

0

1

null

UTF-8

R

false

2,058

r

figure-cum-mul.R

library('ggplot2') junkmultse.original = data.frame(read.csv(file='../output/junkmultse-original.csv', header=T)) junkmultse.original = junkmultse.original[1:20,] junkmultse.original[,'h'] = c(1:20) junkmultse = data.frame(read.csv(file='../output/junkmultse-newsy.csv', header=T)) junkmultse = junkmultse[1:20,] junkmultse[,'h'] = c(1:20) #--------------------------------- # Draw Graphs #--------------------------------- pdf(file="../output/fig-mul-original.pdf", width=12, height=7.5) f1 = ggplot(data=junkmultse.original) f1 = f1 + geom_ribbon(aes(x=h, ymin=multrec1-1.96*seyrec,ymax=multrec1+1.96*seyrec), fill = 'grey70') f1 = f1 + geom_line(aes(x=h,y=multexp1), linetype='F1',color='red') + geom_line(aes(x=h,y=multrec1), color='blue') f1 = f1 + geom_line(aes(x=h,y=multexp1+1.96*seyexp), linetype='longdash', color='red') + geom_line(aes(x=h,y=multexp1-1.96*seyexp), linetype='longdash', color='red') f1 = f1 + geom_line(aes(x=h,y=1), linetype='dotdash') f1 = f1 + labs(x='Quarters', y='Cumulative Multiplier') f1 = f1 + theme(axis.text=element_text(size=16),axis.title=element_text(size=20)) f1 = f1 + coord_cartesian(xlim=c(1.9, 19.5)) f1 = f1 + scale_x_continuous(breaks=seq(2,20,2), limits=c(1,20)) print(f1) dev.off() pdf(file="../output/fig-mul-llss-newsy.pdf", width=12, height=7.5) f2 = ggplot(data=junkmultse) f2 = f2 + geom_ribbon(aes(x=h, ymin=multrec1-1.96*seyrec,ymax=multrec1+1.96*seyrec), fill = 'grey70') + xlim(1,20) f2 = f2 + geom_line(aes(x=h,y=multexp1), linetype='f2',color='red') + geom_line(aes(x=h,y=multrec1), color='blue') f2 = f2 + geom_line(aes(x=h,y=multexp1+1.96*seyexp), linetype='longdash', color='red') + geom_line(aes(x=h,y=multexp1-1.96*seyexp), linetype='longdash', color='red') f2 = f2 + geom_line(aes(x=h,y=1), linetype='dotdash') f2 = f2 + labs(x='Quarters', y='Cumulative Multiplier') f2 = f2 + theme(axis.text=element_text(size=16),axis.title=element_text(size=20)) f2 = f2 + coord_cartesian(xlim=c(1.9, 19.5)) f2 = f2 + scale_x_continuous(breaks=seq(2,20,2), limits=c(1,20)) print(f2) dev.off()

222168fe47da460398f21fa2dda40c7dc5c4e05c

7f9f945c8a02dfd5f38d30abfcbbfa20d24a4391

/man/print.fixest_multi.Rd

1fb8f62071ee5d4ce5ccb472ff1f830a7dacaa5d

[]

no_license

lrberge/fixest

96428663b68c3701f1063f0fb76a87b68333b7d4

6b852fa277b947cea0bad8630986225ddb2d6f1b

refs/heads/master

2023-08-19T22:36:19.299625

2023-04-24T08:25:17

200,205,405

309

64

null

2023-09-13T09:51:03

2019-08-02T09:19:18

R

UTF-8

R

false

true

1,123

rd

print.fixest_multi.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fixest_multi.R \name{print.fixest_multi} \alias{print.fixest_multi} \title{Print method for fixest_multi objects} \usage{ \method{print}{fixest_multi}(x, ...) } \arguments{ \item{x}{A \code{fixest_multi} object, obtained from a \code{fixest} estimation leading to multiple results.} \item{...}{Other arguments to be passed to \code{\link{summary.fixest_multi}}.} } \description{ Displays summary information on fixest_multi objects in the R console. } \examples{ base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # Let's print all that res } \seealso{ The main fixest estimation functions: \code{\link{feols}}, \code{\link[=feglm]{fepois}}, \code{\link[=femlm]{fenegbin}}, \code{\link{feglm}}, \code{\link{feNmlm}}. Tools for mutliple fixest estimations: \code{\link{summary.fixest_multi}}, \code{\link{print.fixest_multi}}, \code{\link{as.list.fixest_multi}}, \code{\link[fixest]{sub-sub-.fixest_multi}}, \code{\link[fixest]{sub-.fixest_multi}}. }

d2ce81b8350fa52993103282ed08aaf4f28c1fe3

37665649d838e477d74d48888be750d15bfeb651

/man/tree_idx.Rd

21411aea8a2a17ed08f57382dd9db7f91993e298

[]

no_license

manueleleonelli/stagedtrees

880c9ecf5b9ec8ba68fd62d2320e8239c803cb5c

f0ebb7ca2f1fa05ccda5558baed2fe086625d7da

refs/heads/master

2020-05-21T00:08:39.125640

2019-03-06T15:50:34

185,819,192

0

null

2019-05-09T14:50:05

null

UTF-8

R

false

true

536

rd

tree_idx.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/0-util-functions.R \name{tree_idx} \alias{tree_idx} \title{return path index} \usage{ tree_idx(path, tree) } \arguments{ \item{path}{a path from root in the tree} \item{tree}{a symmetric tree given as a list of levels This function return the integer index of the node associated with the given path in a symmetric tree defined by \code{tree}.} } \value{ an integer, the index of the node corresponding to \code{path} } \description{ return path index }

eafa270ef41ae4c9ed16853608337ac47e5a029c

58facb39c3292cbfd100b5adae942f313f9e682e

/src/pkgSetup.R

47321bac6ffe77c72fea974a445e91da75c36bca

[]

no_license

amitpatil21/page-2018-mrgsolve

c86f023bccc863b3041808ff62ef1a5be9585a6d

1d3c8227e95d5244976b4d770fce528adee64030

refs/heads/master

2020-07-30T07:19:21.767448

2018-09-18T15:05:25

null

0

null

UTF-8

R

false

3,006

r

pkgSetup.R

author <- c("*") pkgs <- c("tidyverse", "mrgsolve", "knitr", "rmarkdown", "data.table", "caTools", "bitops", "formatR", "git2r") pkgRoot <- "/data/page-packages" pkgDir <- file.path(pkgRoot, "src", "contrib") pkgDir <- normalizePath(pkgDir) libDir <- "/data/page-Rlibs" if(!dir.exists(pkgDir)) dir.create(pkgDir, recursive = TRUE) if(!dir.exists(libDir)) dir.create(libDir) .libPaths(libDir) user <- Sys.info()["user"] fromCRAN <- user %in% author | "*" %in% author local_repos <- paste0("file://",pkgRoot) metrum_repos <- "https://metrumresearchgroup.github.io/r_validated/" cran_repos <- "https://cran.rstudio.com/" repos <- c(mrg = metrum_repos, cran = cran_repos, local = local_repos) deps <- tools::package_dependencies( packages = pkgs, which = c("Depends", "Imports", "LinkingTo"), recursive = TRUE, db = available.packages(repos=repos[c("mrg", "cran")]) ) deps <- unlist(deps, use.names=FALSE) pkgs <- unique(c(pkgs,deps)) base <- rownames(installed.packages(priority=c("base", "recommended"))) pkgs <- setdiff(pkgs,base) tools::write_PACKAGES(pkgDir) if(file.exists(file.path(pkgDir,"PACKAGES"))){ available <- available.packages(repos = repos["local"])[,"Package"] } else{ available <- NULL file.create(file.path(pkgDir,"PACKAGES")) tools::write_PACKAGES(pkgDir) } if(fromCRAN){ newpkgs <- setdiff(pkgs, available) if(length(newpkgs) > 0){ ## These packages are installed either from mrg or cran install.packages(newpkgs, lib=libDir, repos = repos[c("mrg", "cran")], destdir=pkgDir, type="source", INSTALL_opts="--no-multiarch") tools::write_PACKAGES(pkgDir) } ## If multiple authors qcing each other, a package could be available ## but uninstalled. Install from local. uninstalled <- setdiff(pkgs, installed.packages(libDir)) if(length(uninstalled)>0){ install.packages(uninstalled, lib = libDir, repos = repos["local"], type = "source", INSTALL_opts="--no-multiarch") } } if(!fromCRAN){ installed <- row.names(installed.packages(libDir)) newpkgs <- setdiff(pkgs, installed) if(length(newpkgs)>0){ install.packages(newpkgs, lib = libDir, repos = repos["local"], type = "source", INSTALL_opts="--no-multiarch") } } .ignore_libs <- function(root=getwd(),lib="lib", ci=FALSE) { if(!missing(root) & file.exists(root)) { lib <- file.path(root,"lib") } if(!file.exists(lib)) stop("Could not find lib directory") libs <- list.files(lib, full.names=FALSE) libs <- c(libs, "ignore.txt", "PACKAGES", "PACKAGES.gz") writeLines(con=file.path(lib,"ignore.txt"), libs) setwd(lib) system("svn propset svn:ignore -F ignore.txt .") setwd("..") if(ci) system("svn ci -m \"ignoring libs\" .") }

eddced5385bd1dd7e1a5e28bf5c36da0c7036158

969711eebedba44718b75ef6ad2a2a39a070ca08

/ui.R

a4a67d41547b1a00341209401329ec79b8f740c5

[ "MIT" ]

permissive

SubramaniamLab/DEGenR

1f88fee026d6a26f40b63a9bc507824584876495

5f5cf58a46e11f20cd692e920edffdc56edbe158

refs/heads/main

2023-04-07T00:37:02.381321

2021-10-08T15:11:39

339,816,245

2

1

null

UTF-8

R

false

52,377

r

ui.R

source(paste(getwd(),'global.R',sep="/")) header <- dashboardHeader( title = "DEGenR" #titleWidth = 250 ) header$children[[3]]$children[[3]] <- div(tags$img(src='', align="right", height='50px')) sidebar <- dashboardSidebar( width =250, sidebarMenu(id = "sidebarmenu", menuItem("DEGenR Introduction", icon = icon("user"), menuSubItem("Introduction", tabName = "intro")), menuItem("Data Upload", tabName = "Manual", icon = icon("dashboard"), menuItem("Data from recount2", tabName = "", icon = icon("dashboard"), menuSubItem("Select RNA-seq data", tabName="SRPdataset"), menuSubItem("Data Summary", tabName="groupassign_SRP") ), menuItem("Data from GEO", tabName = "", icon = icon("dashboard"), menuSubItem("Select microarray expression data", tabName="GEOdataset"), #menuSubItem("Sample Clustering", tabName="dataCluster"), menuSubItem("Data Summary", tabName="groupassign") ), menuItem("Count Data Upload", tabName = "Manual", icon = icon("dashboard"), menuSubItem("File Upload", tabName="dataInputCounts"), menuSubItem("Data Summary", tabName="dataSummary") ) ), menuItem("Sample Contrast", tabName="limmavoom", icon = icon("dashboard")), menuItem("Differential Gene Expression", icon = icon("dashboard"), menuSubItem("eBayes", tabName="DEGs"), menuSubItem("TREAT", tabName="treatmethod"), menuSubItem("topConfects", tabName="confect") ), menuItem("Ontology Enrichment Analysis", tabName="enrichment", icon = icon("chart-bar"), menuSubItem("camera", tabName="camera"), menuSubItem("fGSEA", tabName="fgsea"), menuSubItem("CERNO", tabName="Cerno"), menuItem("Enrichr","", icon =icon("chart-bar"), menuSubItem("Enrichr Ontology", tabName="enrichrontology"), menuSubItem("Enrichr Plots", tabName="enrichrontologyplots")), menuSubItem("Hypergeometric", tabName="Hypergeometric") ), menuItem("TF Enrichment Analysis", tabName="TFenrichment", icon = icon("chart-bar"), menuItem("Enrichr","", icon =icon("chart-bar"), menuSubItem("Enrichr TF analysis", tabName="enrichr"), menuSubItem("Enrichr Plots", tabName="enrichrTFplots")), menuItem("DoRothEA", tabName="", icon =icon("chart-bar"), menuSubItem("DoRothEA TF analysis", tabName="dorothEA"), menuSubItem("VIPER Shadow analysis", tabName="shadowana")), menuSubItem("fGSEA", tabName="fgseaTF"), menuSubItem("CERNO", tabName="CernoTF"), menuSubItem("Hypergeometric", tabName="HypergeometricTF")) )) body <- dashboardBody( tags$head( tags$link(rel = "stylesheet", type = "text/css", href = "my_style.css"), tags$script( HTML(" $(document).ready(function(){ resize(); }) function resize(){ var h = window.innerHeight - $('.navbar').height() - 150; // Get dashboardBody height $('#box').height(h); }" ) ) ), tags$h4( tags$link(rel = "stylesheet", type = "text/css", href = "my_style.css"), ), tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), tabItems( ############UPLOAD your own########### tabItem(tabName="intro", #img(src="cri.png"), h2("Introduction"), p("We developed DEGenR, an interactive web interface that provides integrated tools for performing differential gene expression, rank-based ontological geneset and pathway enrichment analysis, and transcription factor regulatory analysis from user-uploaded raw read counts as well as microarray and sequencing datasets available at the NCBI Gene Expression Omnibus (GEO) and Sequencing Read Archive (SRA)."), h3("Data Upload", style="padding-left: 1em"), h4("Data from recount2"), p("The recount2 project has processed over 2000 RNA-seq studies in the Sequencing Read Archive (SRA) and other sources, included GTEx and TCGA datasets, using the RNA-seq alignment program Rail-RNA. Entering a SRP dataset here will download the recount2-processed RNA expression and metadata for the dataset you select and prepare it for analysis with DEGenR. For a full list of datasets, see https://jhubiostatistics.shinyapps.io/recount/ and select the accession for the dataset you would like to analyze. References: Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nature Biotechnology, 2017. doi: 10.1038/nbt.3838. Nellore A, Collado-Torres L, Jaffe AE, Alquicira-Hernández J, Wilks C, Pritt J, Morton J, Leek JT, Langmead B. Rail-RNA: scalable analysis of RNA-seq splicing and coverage. Bioinformatics, 2017. doi: 10.1093/bioinformatics/btw575."), h4("Data from GEO"), p("The Gene Expression Omnibus (GEO) housed at NCBI is repository of gene expression data, including numerous human microarray gene expression studies. This step uses the R package GEOquery to download the expression data and metadata for a user-selected microarray study. Search for datasets to analyze at https://www.ncbi.nlm.nih.gov/geo/. Only those datatsets having matrix file can be analyzed."), h4("Count Data Upload"), p("Users are required to upload two files."), p("1. RNA-seq raw count data."), p("2. Metadata table"), p("Please check the example files in .data/public_data folder") ), tabItem(tabName="dataInputCounts", fluidRow( box(title = "Upload Data", solidHeader = T, status = "primary", width = 6, helpText("Upload your RNA-seq raw count data here. Format the count data file for your dataset as a comma separated values (.csv) file, with the first column providing the gene info and all subsequent columns providing the raw counts for each sample. The header for each sample column should correspond to the sample name. To test the DEGenR pipeline with an example dataset (GSE76987, left_colon_processed), click the submit button" ,style="color:black; padding-right:0em; font-size:16px;"), # popify(placement = "bottom", title = "File-input info", fileInput("countFile","Choose CSV File", accept=c( "text/csv", "text/comma-separated-values,text/plain", ".csv")), helpText("Upload your metadata table here, formatted as a tab-delimited file. The first column header should be “Sample” with the sample names provided, and the second column header should be “Condition” with the sample conditions provided." ,style="color:black; padding-right:0em;font-size:16px;"), fileInput("metaTab", "Choose tab-delimited file", accept = c('text/tab-separated-values', 'text/tab-separated-values', '.txt', '.tsv') ), selectInput(inputId = "geneinfo", label = "Choose gene ID information:", choices = c("ENSEMBL", "ENTREZID", "SYMBOL")), actionButton("upload", label = "Submit",icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), ##Input information summary under Data Input Panel box(title = "Input data Summary", solidHeader = T, status = "primary", width = 6, h4("samples in the datasets", style="font-size:20px"), tableOutput("sampleInfo") ) )), ############# GEO Dataset ############## tabItem(tabName="GEOdataset", fluidRow( box(title = "Enter a GEO dataset ID", solidHeader = T, status = "primary", width=12, textInput(inputId="geodataset", label="Enter GSE accession from GEO"), actionButton("uploadGEO", label = "Submit",icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") )), ##Input information summary under Data Input Panel fluidRow( box(title = "Input data Summary", solidHeader = T, status = "primary", width=12, # width = 6, h4("samples in the datasets", style="font-size:20px"), tableOutput("sampleInfo2") ), ) ), ############# SRP Dataset ############## tabItem(tabName="SRPdataset", fluidRow( box(title = "Select RNA-seq data", solidHeader = T, status = "primary", width=12, textInput(inputId="srpdataset", label="Enter SRP accession from recount2. For GTEx data, enter SRP012682; for TCGA, enter TCGA"), actionButton("uploadSRP", label = "Submit",icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") )), ##Input information summary under Data Input Panel fluidRow( box(title = "Input data Summary", solidHeader = T, status = "primary", width=12, # width = 6, h4("samples in the datasets", style="font-size:20px"), tableOutput("sampleInfo3") ) )), ######################################### ## Second tab content for data summarization panel tabItem(tabName="dataSummary", ##First 3 box under Data Summarization panel fluidRow( box(title = "Data summary without filtering", solidHeader = T, status = "primary", width=12, fluidRow( ##Raw Count Summary box under the Data Summarization panel box(title = "Raw Count Summary", status = "primary", width = 4, #fluidRow( # column(4, div(tableOutput("orgLibsizeNormfactor"),style = "font-size:70%"), tags$style("#orgLibsizeNormfactor table {border: 1px solid black; float: center; position:relative;}","#orgLibsizeNormfactor th {border: 1px solid black;}","#orgLibsizeNormfactor td {border: 1px solid black;}") ), box(title = "Density Plot of unfiltered gene expression", status = "primary", width = 4, #column(4, plotOutput("plotdensityunfiltered") ), box(title = "Box Plot of unfiltered gene expression", status = "primary", width = 4, plotOutput("boxplotunfiltered") )))), actionButton("Filter", label = "Filter via EdgeR", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), fluidRow( box(title = "After filtering via EdgeR", solidHeader = T, status = "primary", width=12, ## Sample Normalization box plot under Data Summarization panel fluidRow( box(title = "Plot densities after filteration", status = "primary", width = 4, plotOutput("plotdensities") ), box(title = "Boxplot after filteration", status = "primary", width = 4, plotOutput("sampleBoxplot") ), box(title = "MDS plot", status = "primary", width = 4, plotOutput("mdsplot") ) ))) ), tabItem(tabName='groupassign', box (title ="Group assignment", solidHeader = T, status = "primary", width = 12, helpText('Enter GEO accsion ids separated by comma for both Groups (no space between commas). And name the group accordingly e.g: control, diseased',style="color:black; padding-right:0em;font-size:16px;"), splitLayout( textInput("factor1", "Baseline Group"), textInput("nam1", "Define Group") ), splitLayout( textInput("factor2", "Comparison Group"), textInput("nam2", "Define Group") ), actionButton(inputId="groupassignment", label="Submit", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), helpText('Alternatively, you can enter column name and assign sample names to groups. And name the group accordingly e.g: control, diseased. Use either of the two options.',style="color:black; padding-right:0em;font-size:16px;"), textInput(inputId="colfactor", label="Column name of the factor" ), splitLayout( textInput("factor1geo", "Baseline Group"), textInput("namgeo1", "Define Group") ), splitLayout( textInput("factor2geo", "Comparison Group"), textInput("namgeo2", "Define Group") ), actionButton(inputId="groupassignmentgeo", label="Submit", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4")), fluidRow( box(title = "Box Plot", status = "primary", width = 6, plotOutput("boxplot1") ), box(title = "Expression Density Plot", status = "primary", width = 6, plotOutput("expressiondensityplot") ) ) ), tabItem(tabName='groupassign_SRP', box (title ="Group assignment", solidHeader = T, status = "primary", width = 12, textInput(inputId="colfactor_SRP", label="Column name of the factor" ), textInput(inputId="factor1_SRP", label="Baseline Group" ), textInput(inputId="factor2_SRP", label="Comparison Group" ), actionButton(inputId="groupassignment2", label="Submit", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4")), fluidRow( box(title = "Box Plot", status = "primary", width = 4, plotOutput("boxplot2") ), box(title = "Expression Density Plot", status = "primary", width = 4, plotOutput("expressiondensityplot2") ), box(title = "MDS Plot", status = "primary", width = 4, plotOutput("MDSplot2") ) ) ), tabItem(tabName="limmavoom", ## 1st row with 2 boxes under limma-voom tab panel fluidRow( box(title = "Sample contrast matrix and mean-variance trend", solidHeader = T, status = "primary", width = 12, fluidRow( box(title = "Create the group for contrast matrix and DEGs", status = "primary", uiOutput("grouplevels"), #tags$style("#voomGroupLevel{ font-weight: bold; color: #0033FF; padding-top: .3cm; padding-bottom: .3cm;}"), p("Please select any two groups for comparison" , style="font-weight: bold"), fluidRow( column(6, textInput(inputId="Group1", label="Baseline Group" ) ), column(6, textInput(inputId="Group2", label="Comparison group" ) ), actionButton(inputId="degAnalysis", label="Submit", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ) # ) ), column(6, box(title = "Plot of fitted microarray linear model", width = NULL, status = "primary", plotOutput("voommeanvariance") )))))), tabItem(tabName="DEGs", fluidRow( #column(8, ## Estimated dispersion under limma-voom tab panel box(title = "topTable of differentially expressed genes (eBayes method)", width = 12, solidHeader = T, status = "primary", div(DTOutput("reslimma"),style = "font-size:100%"), downloadButton("voomDownload", label = "Download") ), ## DEGs Summary to summarize the up- and down-regulated DEGs box(title = "DEG Summary", width = 12, solidHeader = T, status = "primary", helpText('Default cutoff is P-value=0.05', style="color:black; padding-right:0em; font-size:16px;"), # h4(textOutput("voomTestDGEtitle"), align="center" ), tableOutput("summarydegs"), textInput(inputId="pvalcutoff", label="FDR adjusted p-value", value="0.05"), actionButton(inputId="pvalfilter", label="Submit", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), ) ) ), tabItem(tabName="treatmethod", ## 1st row with 2 boxes under limma-voom tab panel fluidRow( box(title = "TREAT analysis of differentially expressed genes", solidHeader = T, status = "primary", width = 12, box(title = "topTable of differentially expressed genes (TREAT method)", width = NULL, status = "primary", div(DTOutput("restreat"), style = "font-size:100%"), downloadButton("treatDownload", label = "Download", class = NULL) ), column(6, box(title = "TREAT analysis", width = NULL, status = "primary", h4("TREAT analysis tests differential gene expression relative to a fold-change threshold",style="font-size:20px"), helpText('Default cutoff is Adj.P-value=0.05', style="color:black; padding-right:0em; font-size:16px;"), textInput(inputId="FC", label=HTML("log2FC cutoff"), value="1.5"), textInput(inputId="pvalcutoff2", label="FDR adjusted p-value cutoff", value="0.05"), actionButton(inputId="treatanalysis", label="Treat", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), tableOutput("summaryaftertreat"), )), column(6, box(title = "MD-plot after TREAT analysis", width = NULL, status = "primary", #tableOutput("summaryaftertreat"), downloadButton("MDplotDownload", label = "Download"), plotOutput("MDplot") )) )) ), tabItem(tabName="confect", # # 1st row with 2 boxes under limma-voom tab panel fluidRow( box(title = "topConfects analysis of differentially expressed genes", solidHeader = T, status = "primary", width = 12, box(title = "top differentially expressed genes (topConfects method)", width = NULL, status = "primary", h4("topConfects builds on the TREAT method to rank genes by confident effect size (based on the Confidence Interval) at a fixed FDR",style="font-size:20px"), textInput(inputId="fdr", label="FDR adjusted p-value", value="0.05"), actionButton(inputId="runconfect", label="Run topconfects", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), div(DTOutput("restopconfect"), style = "font-size:100%"), downloadButton("topconfectsDownload", label = "Download") ))), fluidRow( column(4, box(title = "Plot topConfects results", width = NULL, status = "primary", #tableOutput("summaryaftertreat"), plotOutput("confectplot"), downloadButton("confectplotDownload", label = "Download") )), column(4, box(title = "Compare between eBayes and topConfects DEGs", width = NULL, status = "primary", #tableOutput("summaryaftertreat"), plotOutput("voomvsconfectplot"), downloadButton("voomvsconfectplotDownload", label = "Download") )), column(4, box(title = "MD plot of the top n number of genes ranked by eBayes or topConfects", width = NULL, status = "primary", #tableOutput("summaryaftertreat"), textInput(inputId="n", label="No. of genes", value="500"), actionButton(inputId="plotlimma_confect", label="MD plot", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), plotOutput("MD_limma_confects"), downloadButton("MD_limma_confectsDownload", label = "Download") )) ) ), tabItem(tabName="camera", fluidRow( box(title = "Competitive Geneset Test Accounting for Inter-gene Correlation (camera method)", width = NULL, solidHeader = T, status = "primary", #helpText('Choose Enriched gene sets',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "pathwaysname", label = "Select geneset database:", choices = c( "All Gene_Ontology", "Gene Ontology: Biological Process (Full)", "Gene Ontology: Cellular Component (Full)", "Gene Ontology: Molecular Function (Full)", "Human Phenotype Ontology", "Reactome", "MSigDB Hallmark", "BioCarta", "KEGG", "PID", "WikiPathways", "MSigDB Chemical and Genetic Perturbations ", "MSigDB Computational Genesets", "MSigDB Oncogenic Signature Genesets", "MSigDB Immunologic signature Genesets", "MSigDB Cell Types" )), actionButton(inputId="pathwayenrichment", label="Enrichment (CAMERA)", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), div(DTOutput("enrichmentout"), style = "font-size:90%"), downloadButton("enrichmentontologyDownload", label = "Download") )), fluidRow( box(title = "Barcode plot",width = NULL, solidHeader = T, status = "primary", h4("Enter Gene-set for plotting Barcode Plot"), textInput(inputId="geneset", label="Gene Set", value= ""), actionButton(inputId="barcode", label="Barcode Plot", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), plotOutput("barcodeout"), downloadButton("barcodeDownload", label = "Download") ) ) ), tabItem(tabName="fgsea", fluidRow( box(title = "fast Gene Set Enrichment Analysis (fGSEA)", width = NULL, solidHeader = T, status = "primary", #column(4, box(title = "Gene ranking and geneset database selection", width = NULL, solidHeader = T, status = "primary", helpText('Make sure to run TREAT and/or topConfects in order to use as a ranking method',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "genelist", label = "Select a gene ranking method:", choices = c("eBayes_tvalue", "TREAT_tvalue", "topConfects")), helpText('Choose Enriched gene sets',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "pathwaylist", label = "Choose Enriched gene sets:", choices = c("All Gene_Ontology", "Gene Ontology: Biological Process (Full)", "Gene Ontology: Cellular Component (Full)", "Gene Ontology: Molecular Function (Full)", "Human Phenotype Ontology", "Reactome", "MSigDB Hallmark", "BioCarta", "KEGG", "PID", "WikiPathways", "MSigDB Chemical and Genetic Perturbations", "MSigDB Computational Genesets", "MSigDB Oncogenic Signature Genesets", "MSigDB Immunologic signature Genesets", "MSigDB Cell Types")), actionButton(inputId="runfgsea", label="Run fgsea", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), ), box(title = "fGSEA Results", width = NULL, solidHeader = T, status = "primary", div(DTOutput("fgseaout_gobp"), style = "font-size:90%"), downloadButton("fgseaDownload", label = "Download")), box(title = "fGSEA Plot", width = NULL, solidHeader = T, status = "primary", plotOutput('fgseaplot'), downloadButton("fgseaplotDownload", label = "Download")) )) ), tabItem(tabName="fgseaTF", fluidRow( box(title = "Fast Gene Set TF Enrichment Analysis (fgsea)", width = NULL, solidHeader = T, status = "primary", #column(4, box(title = "Filter criteria", width = NULL, solidHeader = T, status = "primary", helpText('Choose from the gene ranking matrix, be careful to run Treat and/or Topconfects if you choose either of these',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "genelistTF", label = "Choose a gene ranking matrix:", choices = c("eBayes_tvalue", "TREAT_tvalue", "topConfects")), helpText('Choose Enriched gene sets',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "pathwaylistTF", label = "Choose Enriched gene sets:", choices = c("ENCODE-ChEA Consensus (Enrichr)", "ChEA 2016 (Enrichr)", "ENCODE 2015 (Enrichr)", "ReMap ChIP-seq 2018 Human", "TRRUST 2019 Human", "ChEA3 Literature ChIP-Seq", "TRANSFAC/JASPAR PWMs (Enrichr)", "Gene Transcription Regulation Database (GTRD v20.06)", "MSigDB Legacy TF targets", "miRTarBase 2017", "miRNA TargetScan 2017", "miRDB v6.0")), actionButton(inputId="runfgseaTF", label="Run fgsea", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4"), ), div(DTOutput("fgseaout_TF"), style = "font-size:90%"), downloadButton("fgseaTFDownload", label = "Download"), plotOutput('fgseaplotTF') )) ), tabItem(tabName="enrichrontology", fluidRow( box(title = "Ontology enrichment using Enrichr", width = NULL, solidHeader = T, status = "primary", box(title = "Filter criteria", width = NULL, solidHeader = T, status = "primary", helpText('Make sure to run TREAT and/or topConfects in order to use as a ranking method',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "genesenrichr", label = "Select a gene ranking method:", choices = c("eBayes_tvalue", "TREAT_tvalue", "topConfects")), selectInput(inputId = "databaseOntology", label = "Select geneset database:", choices = c("GO_Biological_Process_2018", "GO_Biological_Process_2017b", "GO_Molecular_Function_2018", "GO_Molecular_Function_2017b", "GO_Cellular_Component_2018", "GO_Cellular_Component_2017b", "MSigDB_Hallmark_2020", "Reactome_2016", "BioCarta_2016", "KEGG_2019_Human", "Panther_2016", "WikiPathways_2019_Human", "BioPlanet_2019")), textInput(inputId="FC4", label=HTML("log2FC or confect (topConfects) cutoff"), value="0.5"), textInput(inputId="pvalenrichonto", label="P value cutoff", value="0.05" ), actionButton(inputId="runenrichrOntology", label="Run enrichr Ontology", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), box(title = "Ontology enrichment on up-regulated genes", width = NULL, solidHeader = T, status = "primary", div(DTOutput("up_ontologyenrichr"), style = "font-size:90%; width: 90%"), downloadButton("upenrichrontologyDownload", label = "Download") ), box(title = "Ontology Enrichment analysis on down-regulated genes", width = NULL, solidHeader = T, status = "primary", div(DTOutput("down_ontologyenrichr"), style = "font-size:90%; width: 90%"), downloadButton("downenrichrontologyDownload", label = "Download") ) )) ), tabItem(tabName="enrichrontologyplots", fluidRow( box(title = "Filter criteria", width = NULL, solidHeader = T, status = "primary", textInput(inputId="num", label="Number of terms to plot", value="20"), selectInput(inputId = "pval_FDR2", label = "Select P-val or FDR:", choices = c("P.value", "Adjusted.P.value")), actionButton(inputId="filterenrichrOntology", label="Plotting", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), ), fluidRow(column(6, box(title = "Bar plot of up-regulated terms", width = NULL, solidHeader = T, status = "primary", plotOutput('upplots'), downloadButton("upplotsenrichrOntologyDownload", label = "Download") )), column(6, box(title = "Bar plot of down-regulated terms", width = NULL, solidHeader = T, status = "primary", plotOutput('downplots'), downloadButton("downplotsenrichrOntologyDownload", label = "Download") ))), fluidRow( box(title = "Combined scores from up-regulated and down-regulated genes", width = NULL, solidHeader = T, status = "primary", helpText("Combined plot will have double the number of terms used in filter crieria"), plotOutput('updownplots'), downloadButton("updownplotsenrichrOntologyDownload", label = "Download") ))), tabItem(tabName="enrichr", fluidRow( box(title = "TF enrichment using Enrichr", width = NULL, solidHeader = T, status = "primary", box(title = "Filter criteria", width = NULL, solidHeader = T, status = "primary", helpText('Make sure to run TREAT and/or topConfects in order to use as a ranking method',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "genes", label = "Select a gene ranking method:", choices = c("eBayes_tvalue", "TREAT_tvalue", "topConfects")), selectInput(inputId = "database", label = "Select geneset database:", choices = c("ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X", "ENCODE_TF_ChIP-seq_2015", "ChEA_2016", "TRANSFAC_and_JASPAR_PWMs", "TargetScan_microRNA", "ARCHS4_TFs_Coexp", "TRRUST_Transcription_Factors_2019", "TargetScan_microRNA_2017", "miRTarBase_2017")) , textInput(inputId="FC3", label=HTML("log2FC or confect (topConfects) cutoff"), value="0.5"), textInput(inputId="pvalenrichTF", label="P value cutoff", value="0.05" ), actionButton(inputId="runenrichr", label="Run enrichr", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), box(title = "TF enrichment on up-regulated genes", width = NULL, solidHeader = T, status = "primary", div(DTOutput("up_enrichr"), style = "font-size:90%; width: 90%"), downloadButton("upenrichrDownload", label = "Download") ), box(title = "TF Enrichment analysis on down-regulated genes", width = NULL, solidHeader = T, status = "primary", div(DTOutput("down_enrichr"), style = "font-size:90%; width: 90%"), downloadButton("downenrichrDownload", label = "Download") ) )) #,uiOutput("Next_stepdorothEA", align="center") ), tabItem(tabName="enrichrTFplots", fluidRow( box(title = "Filter criteria", width = NULL, solidHeader = T, status = "primary", textInput(inputId="num2", label="Number of terms to plot", value="20"), selectInput(inputId = "pval_FDR", label = "Select P-val or FDR:", choices = c("P.value", "Adjusted.P.value")), actionButton(inputId="filterenrichrTF", label="Plotting", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), ), fluidRow(column(6, box(title = "Bar plot of up-regulated terms", width = NULL, solidHeader = T, status = "primary", plotOutput('upTFplots'), downloadButton("upTFplotsDownload", label = "Download") )), column(6, box(title = "Bar plot of down-regulated terms", width = NULL, solidHeader = T, status = "primary", plotOutput('downTFplots'), downloadButton("downTFplotsDownload", label = "Download") ))), fluidRow( box(title = "Combined scores from up-regulated and down-regulated genes", width = NULL, solidHeader = T, status = "primary", helpText("Combined plot will have double the number of terms used in filter crieria"), plotOutput('updownTFplots'), downloadButton("updownTFplotsDownload", label = "Download") ))), tabItem(tabName="dorothEA", fluidRow( box(title = "TF activity analysis using viper algorithm and DoRothEA regulons", width = NULL, solidHeader = T, status = "primary", selectInput(inputId = "dorothearegulon", label = "Select the DoRothEA regulon:", choices = c("regulon_a", "regulon_b", "regulon_c", "regulon_d", "regulon_e")), helpText('Make sure to run TREAT and/or topConfects in order to use as a ranking method',style="color:black; padding-right:0em; font-size:16px;"), selectInput(inputId = "genesl", label = "Select a gene ranking method:", choices = c("eBayes_tvalue", "TREAT_tvalue", "topConfects")), actionButton(inputId="rundorothea", label="Run DoRothEA", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4")), box(title = "DoRothEA TF Activity Analysis", width = NULL, solidHeader = T, status = "primary", div(DTOutput("dorothea"), style = "font-size:90%"), downloadButton("dorotheaDownload", label = "Download") )), fluidRow( box(title = "Genes contributing most to these TF activity", width = NULL, solidHeader = T, status = "primary", tableOutput("genesummary") ), box(title = "A graphics representation of the results (msVIPER plot)", width = NULL, solidHeader = T, status = "primary", plotOutput("Plotdorothea"), downloadButton("dorotheaplotDownload", label = "Download") ) )), tabItem(tabName="shadowana", fluidRow( box(title = "Shadow analysis", width = NULL, solidHeader = T, status = "primary", helpText('A regulator may appear to be significantly activated because it may share its regulon of its with a activated TF (shadow effect). To account for this shadow analysis is performed which can list shadow pairs.',style="color:black; padding-right:0em; font-size:16px;"), textInput(inputId="number", label="Number of top regulators", value="25"), actionButton(inputId="runshadow", label="Run shadow analysis", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4")), box(title = "Result of Shadow Analysis", width = NULL, solidHeader = T, status = "primary", tableOutput("shadowanalysis"), downloadButton("shadowDownload", label = "Download") ), box(title = "Shadow pairs", width = NULL, solidHeader = T, status = "primary", tableOutput("shadowpairssummary") ) )), tabItem(tabName="Cerno", fluidRow( box(title = "Coincident Extreme Ranks in Numerical Observations (CERNO)", width = NULL, solidHeader = T, status = "primary", selectInput(inputId = "cernogene", label = "Select geneset database:", choices = c("Hallmark", "Reactome", "Gene Ontology Biological Process (MSigDB Filtered)", "Gene Ontology Biological Process (Full)", "Biocarta", "Gene Ontology Molecular Function (MSigDB Filtered)", "Gene Ontology Molecular Function (Full)", "Gene Ontology Cellular Compartment (MSigDB Filtered)", "Gene Ontology Cellular Compartment (Full)", "Human Phenotype Ontology", "KEGG", "Pathway Interaction Database", "Wikipathways", "MSigDB Chemical and Genetic Perturbations", "MSigDB Computational Genesets", "MSigDB Oncogenic Signature Genesets", "MSigDB Immunologic signature Genesets", "MSigDB Cell Types" )), textInput(inputId="textsize", label="Text size for the Panel Plot", value="0.5"), actionButton(inputId="runcerno", label="Run cerno", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), box(title = "Results from CERNO analysis", width = NULL, solidHeader = T, status = "primary", #tableOutput("cernoanalysis"), div(DTOutput("cernoanalysis"), style = "font-size:90%"), downloadButton("cernoDownload", label = "Download") ), box(title = "Panel Plot", width = NULL, status = "primary", downloadButton("PanelplotDownload", label = "Download"), plotOutput("Panelplot") ) )), tabItem(tabName="Hypergeometric", fluidRow( box(title = "Hypergeometric Enrichment", width = NULL, solidHeader = T, status = "primary", helpText("Make sure to run TREAT and/or topConfects in order to use as a ranking method"), selectInput(inputId = "geneshyper", label = "Select a gene ranking method:", choices = c("eBayes_tvalue", "TREAT_tvalue", "topConfects")), selectInput(inputId = "hypergene", label = "Select geneset database:", choices = c("Hallmark", "Reactome", "Gene Ontology Biological Process (MSigDB Filtered)", "Gene Ontology Biological Process (Full)", "Biocarta", "Gene Ontology Molecular Function (MSigDB Filtered)", "Gene Ontology Molecular Function (Full)", "Gene Ontology Cellular Compartment (MSigDB Filtered)", "Gene Ontology Cellular Compartment (Full)", "Human Phenotype Ontology", "KEGG", "Pathway Interaction Database", "Wikipathways", "MSigDB Chemical and Genetic Perturbations", "MSigDB Computational Genesets", "MSigDB Oncogenic Signature Genesets", "MSigDB Immunologic signature Genesets", "MSigDB Cell Types")), textInput(inputId="pvalcutoff3", label="FDR adjusted p-value cutoff", value="0.05"), textInput(inputId="FC2", label=HTML("log2FC or confect (topConfects) cutoff"), value="0.5"), actionButton(inputId="runhyper", label="Run Hypergeometric", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), box(title = "Hypergeometric test of up-regulated genes", width = NULL, solidHeader = T, status = "primary", #tableOutput("hyperanalysis"), div(DTOutput("hyperanalysis"), style = "font-size:90%"), downloadButton("hyperDownload", label = "Download") ), box(title = "Hypergeometric test of down-regulated genes", width = NULL, solidHeader = T, status = "primary", #tableOutput("hyperanalysisdown"), div(DTOutput("hyperanalysisdown"), style = "font-size:90%"), downloadButton("hyperdownDownload", label = "Download") ) )), tabItem(tabName="CernoTF", fluidRow( box(title = "TF-gene target enrichment using Coincident Extreme Ranks in Numerical Observations (CERNO)", width = NULL, solidHeader = T, status = "primary", selectInput(inputId = "cernoTFgene", label = "Select geneset database:", choices = c("ENCODE/ChEA Consensus (Enrichr)", "ReMap ChIP-Seq", "TRRUST" , "TRANSFAC/JASPAR PWMs (Enrichr)", "Gene Transcription Regulation Database (GTRD v20.06)", "miRTarBase 2017" , "miRDB v6.0")), textInput(inputId="textsize2", label="Text size for the Panel Plot", value="0.5"), actionButton(inputId="runTFcerno", label="Run cerno", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), box(title = "Results from CERNO TF analysis", width = NULL, solidHeader = T, status = "primary", #tableOutput("cernoTFanalysis"), div(DTOutput("cernoTFanalysis"), style = "font-size:90%"), downloadButton("cernoTFDownload", label = "Download") ), box(title = "Panel Plot", width = NULL, status = "primary", downloadButton("PanelplotTFDownload", label = "Download"), plotOutput("PanelplotTF") ) )), tabItem(tabName="HypergeometricTF", fluidRow( box(title = "Hypergeometric TF Enrichment", width = NULL, solidHeader = T, status = "primary", helpText("Make sure to run TREAT and/or topConfects in order to use as a ranking method"), selectInput(inputId = "geneshyperTF", label = "Select a gene ranking method:", choices = c("eBayes_tvalue", "TREAT_tvalue", "topConfects")), selectInput(inputId = "hyperTFgene", label = "Select geneset database:", choices = c("ENCODE/ChEA Consensus (Enrichr)", "ReMap ChIP-Seq", "TRRUST" , "TRANSFAC/JASPAR PWMs (Enrichr)", "Gene Transcription Regulation Database (GTRD v20.06)", "miRTarBase 2017" , "miRDB v6.0")), textInput(inputId="pvalcutoff4", label="FDR adjusted p-value cutoff", value="0.05"), textInput(inputId="FC4", label=HTML("log2FC or confect (topConfects) cutoff"), value="0.5"), actionButton(inputId="runTFhyper", label="Run Hypergeometric", icon("paper-plane"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4") ), box(title = "Hypergeometric TF enrichment of up-regulated genes", width = NULL, solidHeader = T, status = "primary", #tableOutput("hyperTFanalysis"), div(DTOutput("hyperTFanalysis"), style = "font-size:90%"), downloadButton("hyperTFDownload", label = "Download") ), box(title = "Hypergeometric TF enrichment of down-regulated genes", width = NULL, solidHeader = T, status = "primary", #tableOutput("hyperTFanalysisdown"), div(DTOutput("hyperTFanalysisdown"), style = "font-size:90%"), downloadButton("hyperTFdownDownload", label = "Download") ) )) ) ) ui <- dashboardPage(header, sidebar, body)

f512374c6dae0825b6b15a0695b4648f4be9d210

4cee6dec70875ca85f20dd738932be86f361a63e

/pkg/tests/testthat/test-ci.R

3049404f27ec88de239ab86e2f5db38c74d2f6e7

[]

no_license

dieterich-lab/pulseR

9b7114769b48a305ba0a11357226e8f774b73a20

1323b378e95b483c8bda99d6c71befccd45c810f

refs/heads/master

2021-01-18T20:40:00.474158

2018-10-26T10:45:32

72,013,067

2

4

null

UTF-8

R

false

2,458

r

test-ci.R

context("Confidence intervals") set.seed(259) formulas <- MeanFormulas(X = mu, Y = nu) formulaIndexes <- list( EX = 'X', EXandY = c('X', 'Y')) normFactors <- list( EX = c(1), EXandY = c(1, .1) ) nTime <- 1 nReplicates <- 4 conditions <- data.frame(condition = rep(names(formulaIndexes), each = nTime), time = rep(1:nTime, length(formulas) * nReplicates)) rownames(conditions) <- paste0("sample_", seq_along(conditions$condition)) known <- addKnownToFormulas(formulas, formulaIndexes, conditions) normFactors <- known$formulaIndexes[unique(names(known$formulaIndexes))] fractions <- as.character(interaction(conditions)) nGenes <- 2 par <- list(size = 1e4) par <- c(par, list( mu = runif(nGenes, 100, 1000), nu = runif(nGenes,100,1000))) allNormFactors <- multiplyList(normFactors, fractions) counts <- generateTestDataFrom( formulas, formulaIndexes, allNormFactors, par, conditions) pd <- PulseData( counts = counts, conditions = conditions, formulas = formulas, formulaIndexes = formulaIndexes, groups = fractions ) options <- list() options$lb <- list(mu = 1, nu = 1) options$lb <- pulseR:::.b(options$lb, par) options$ub <- list(mu = 1e4, nu = 1e4) options$ub <- pulseR:::.b(options$ub, par) options$lb$size <- 1 options$ub$size <- 1e6 options$lb$normFactors <- pulseR:::assignList(normFactors, .01) options$ub$normFactors <- pulseR:::assignList(normFactors, 20) options <- setTolerance(.01,shared = .01, normFactors = .01,options = options) options$verbose <- "silent" par$normFactors <- normFactors fit <- fitModel(pd, par,options) test_that("plGene is zero at optimum", { expect_lte( abs(plGene("mu",1,fit, pd,options)(fit$mu[1])$value), 1e-4) expect_lte( abs(pl(list("mu",1),fit, pd,options)(fit$mu[1])$value), 1e-4) }) test_that("profile estimations on the interval", { prof <- profile(list("mu", 1), pd, fit, options, interval = rep(fit$mu[1], 2), numPoints = 1) expect_lte(abs(prof$logL), 1e-6) prof <- profileGene("mu", 1, pd, fit, options, interval = rep(fit$mu[1], 2), numPoints = 1) expect_lte(abs(prof$logL), 1e-6) }) test_that("ci calculation", { cis <- ciGene("mu",1,pd,fit,options) optimum <- evaluateLikelihood(fit, pd) vapply(cis, function(x) { p <- .assignElement(fit, list("mu",1), x) evaluateLikelihood(p, pd) - optimum }, double(1)) cis <- ci(list("mu", 1), pd, fit, options) })

7a03d797ceddba1aaeaa34dcac0741b0e31da057

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/tangram/examples/table_builder.Rd.R

51a33b43f43385016eb8a56b090d5f625acccfe6

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

1,077

r

table_builder.Rd.R

library(tangram) ### Name: table_builder ### Title: Table Construction Toolset ### Aliases: table_builder col_header row_header write_cell home cursor_up ### cursor_down cursor_left cursor_right cursor_pos carriage_return ### line_feed new_line new_row new_col table_builder_apply add_col ### add_row ### ** Examples library(magrittr) table_builder() %>% col_header("One","Two","Three","Four") %>% row_header("A", "B", "C") %>% write_cell("A1") %>% cursor_right() %>% add_col("A2", "A3") %>% home() %>% new_line() %>% table_builder_apply(1:3, FUN=function(tb, x) { tb %>% write_cell(paste0("B",x)) %>% cursor_right() }) %>% new_col() %>% add_row(paste0(c("A","B","C"), 4)) %>% cursor_up(2) %>% line_feed() %>% cursor_left(3) %>% add_col(paste0("C", 1:4))

ee78e6c58da9897236571c83da25a21cebe58de4

948b78fc214a1b9981790c83abb6284758dbfa89

/r-library/man/locfitGrowthEstimate.Rd

aa1ce924d42b0597c62fdea0ec965936be6454fc

[ "MIT" ]

permissive

terminological/jepidemic

4ea81235273649b21cf11108c5e78dd7612fdf6e

f73cc26b0d0c431ecc31fcb03838e83d925bce7a

refs/heads/main

2023-04-14T10:13:56.372983

2022-05-24T22:07:10

309,675,032

0

null

UTF-8

R

false

true

900

rd

locfitGrowthEstimate.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/estimators.R \name{locfitGrowthEstimate} \alias{locfitGrowthEstimate} \title{Generate a smoothed estimate of the absolute growth rate of cases using a poisson model.} \usage{ locfitGrowthEstimate( simpleTimeseries, degree = 2, window = 14, weightByWeekday = FALSE, ... ) } \arguments{ \item{simpleTimeseries}{- a minimal time-series including date, value, and if available total. If total is present the proportion is value/total. otherwise it is value.} \item{degree}{the polynomial degree} \item{window}{the data window in days} \item{...}{may include "nearestNeigbour=FALSE" to disable the tail behaviour of locfit} } \value{ a timeseries with growth rate estimates (columns starting with "Growth") } \description{ Generate a smoothed estimate of the absolute growth rate of cases using a poisson model. }

99d4bac7d4b24acd7e4faec33478c5d50b3598d4

bf6eeabe8154eb0c192c1f27603dbd53fca4bdec

/R/class.R

7f1acfa1bb5f9e9c8f510d2c147ed8908d45ba9b

[]

no_license

gokmenzararsiz/MLSeq

a81484c77bc77cc43a9317a5cc71ec10eb751a63

f58cf5297d97b1f338d1748cc38df75f2e2accd3

refs/heads/master

2021-01-17T09:21:45.177052

2016-12-22T07:42:47

18,428,996

1

null

2016-04-19T13:10:50

2014-04-04T05:58:29

R

UTF-8

R

false

3,387

r

class.R

setOldClass(c("confusionMatrix","train")) #' \code{MLSeq} object #' #' For classification, this is the main class for the \code{MLSeq} package. #' #' Objects can be created by calls of the form \code{new("MLSeq", ...)}. This type #' of objects is created as a result of \code{classify} function of \code{MLSeq} package. #' It is then used in \code{predictClassify} function for predicting the class labels of new samples. #' #' @section Slots: #' #' \describe{ #' \item{\code{method}:}{stores the name of used classification method in the classification model} #' \item{\code{transformation}:}{stores the name of used transformation method in the classification model} #' \item{\code{normalization}:}{stores the name of used normalization method in the classification model} #' \item{\code{confusionMat}:}{stores the information of classification performance results} #' \item{\code{trainedModel}:}{stores the information about training process and model parameters that used in the corresponding model} #' \item{\code{ref}:}{stores user defined reference class} #' } #' #' @note An \code{MLSeq} class stores the results of \code{classify} function and offers further slots that are populated #' during the analysis. The slot \code{confusionMat} stores the information of classification performance results. These #' results contain the classification table and several statistical measures including accuracy rate, sensitivity, specifity, #' positive and negative predictive rates, etc. \code{method}, \code{normalization} and \code{deseqTransform} slots store #' the name of used classification method, normalization method and transformation method in the classification model respectively. #' Lastly, the slot \code{trained} stores the information about training process and model parameters that used in the corresponding model. #' #' @author Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver, Ahmet Ozturk #' #' @docType class #' @name MLSeq-class #' @rdname MLSeq-class #' @aliases MLSeq-class #' @exportClass MLSeq setClass("MLSeq", slots = c(method = "character", transformation = "character", normalization = "character", confusionMat = "confusionMatrix", trainedModel = "train", ref = "character"), prototype = prototype(confusionMat=structure(list(), class="confusionMatrix"), trainedModel = structure(list(), class="train"))) setValidity("MLSeq", function( object ) { if (!(method(object) %in% c("svm", "bagsvm", "randomforest", "cart"))) return("Error: 'method' slot must be in one of the following methods: \"svm\", \"bagsvm\", \"randomforest\", \"cart\" ") if (!(normalization(object) %in% c("deseq", "none", "tmm"))) return("Error: 'normalization' slot must be in one of the following: \"deseq\", \"none\", \"tmm\" ") if (!(transformation(object) %in% c("vst", "voomCPM", "NULL"))) return("Error: 'transformation' slot must be in one of the following: \"vst\", \"voomCPM\" ") if (!is.character(ref(object))) return("Error: 'ref' slot must be a character ") if ((normalization(object) == "tmm" & transformation(object) == "vst")) return("Warning: \"vst\" transformation can be applied only with \"deseq\" normalization. \"voom-CPM\" transformation is used. ") TRUE } )

fe6c6bb235b114dd84c17b6c5e246700037bdeaa

548f28065c18662debd5b6514fc634913a77b49c

/medium_case_animate.R

72b2467944c25b0926ca917558f536c463484b5d

[ "MIT" ]

permissive

EngyMa/animated-case

34a5f6ab669d008af3d473961ac08b57b7c59d80

cb762351ac6ecc7d218babe4b9a9c46e01558d18

refs/heads/master

2020-04-25T17:11:30.399169

2018-12-04T12:35:46

172,938,451

1

0

MIT

2019-02-27T15:15:01

2019-02-27T15:15:00

null

UTF-8

R

false

6,451

r

medium_case_animate.R

# ggplot2 theme to use later theme_chris <- function (base_size = 12, base_family = "serif", ticks = TRUE) { ret <- theme_bw(base_family = base_family, base_size = base_size) + theme(legend.background = element_blank(), legend.key = element_blank(), panel.border = element_blank(), strip.background = element_blank(), panel.background = element_rect(fill = "#94B1C533", colour = NA), plot.background = element_rect(fill = "#ffffff"), axis.line = element_blank(), panel.grid = element_blank(), axis.text.x = element_text(colour = "#2a3132"), axis.title.x = element_text(colour = "#2a3132"), axis.title.y = element_text(colour="#2a3132"), axis.text.y = element_text(colour="#2a3132"), axis.title = element_text(colour = "#2a3132"), plot.title = element_text(colour = "#2a3132", margin = margin(0,0,10,0)), plot.subtitle = element_text(colour = "#2a3132"), plot.caption = element_text(colour = "#2a3132"), legend.title = element_text(colour = "#2a3132"), legend.text = element_text(colour = "#2a3132")) if (!ticks) { ret <- ret + theme(axis.ticks = element_blank()) } ret } # import yearly data (total, summed values, not means or medians) # dataset compiled from historical Ross-CASE reports library(readr) fund_df <- read_csv("year_sum.csv") # quick look at data library(dplyr) glimpse(fund_df) library(ggplot2) ggplot(fund_df, aes(x = year, y = new_funds_raised)) + geom_line() # create contactable alumni x100 variable to place values on equivalent scale fund_df <- fund_df %>% mutate(contact_alum_x100 = contactable_alumni * 100) # create tidy dataframe library(tidyr) fund_tidy <- fund_df %>% gather(kpi, value, - year) %>% mutate(kpi = as.factor(kpi)) glimpse(fund_tidy) # create animated plot library(gganimate) library(transformr) first_animate <- fund_tidy %>% filter(kpi != "contactable_alumni") %>% ggplot(aes(x = year, y = value, colour = kpi)) + geom_line() + transition_reveal(kpi, year) + labs(title = "Trends in University Fundraising KPIs Over Time", subtitle = "Data from Ross-CASE reports", x = "Year", y = 'Value', caption = "y axis labelling omitted due to differences in scale between KPIs", colour = "KPI") + scale_colour_discrete(labels = c("Cash received", "Contactable alumni", "Fundraising staff", "New funds raised")) + scale_y_discrete(labels = NULL) + theme_chris() # animate and save first_animated <- animate(first_animate, height = 500, width = 800) anim_save("first_animated.gif", animation = first_animated) # create non-animated plot with trendlines fund_tidy %>% filter(kpi != "contactable_alumni") %>% ggplot(aes(x = year, y = value, colour = kpi)) + geom_line() + geom_smooth(method = "lm", linetype = "dashed", se = FALSE) + labs(title = "Trends in University Fundraising KPIs Over Time", subtitle = "Data from Ross-CASE reports", x = "Year", y = 'Value', caption = "y axis labelling omitted due to differences in scale between KPIs", colour = "KPI") + scale_colour_discrete(labels = c("Cash received", "Contactable alumni", "Fundraising staff", "New funds raised")) + scale_y_discrete(labels = NULL) + theme_chris() #---- create linear model and augmented dataframe ---- # build pre-filtered dataframe fund_tidy2 <- fund_tidy %>% filter(kpi != "contactable_alumni") # build linear model lin_mod <- lm(value ~ year + kpi, data = fund_tidy2) # augment linear model to produce tidy dataframe with fitted values library(broom) aug_mod <- augment(lin_mod) # create animated graph aug_animate <- aug_mod %>% ggplot(aes(x = year, y = value, colour = kpi)) + geom_line(aes(group = kpi, y = .fitted), size = 0.5, linetype = "dashed") + geom_point(size = 2) + geom_line(aes(group = kpi)) + transition_reveal(kpi, year) + labs(title = "Trends in University Fundraising KPIs Over Time", subtitle = "Data from Ross-CASE reports", x = "Year", y = 'Value', caption = "y axis labelling omitted due to differences in scale between KPIs", colour = "KPI") + scale_colour_discrete(labels = c("Cash received", "Contactable alumni", "Fundraising staff", "New funds raised")) + theme_chris() # animate and save aug_animated <- animate(aug_animate, height = 500, width = 800) anim_save("aug_animated.gif", animation = aug_animated) #---- build multiple models for animated plot with trendlines ---- # build nested tibble fund_nested <- fund_tidy2 %>% group_by(kpi) %>% nest() # build separate regression models fund_models <- fund_nested %>% mutate(lm_mod = map(data, ~lm(formula = value ~ year, data = .x))) # augment models and unnest tibble fund_models_aug <- fund_models %>% mutate(aug = map(lm_mod, ~augment(.x))) %>% unnest(aug) case_animate <- fund_models_aug %>% ggplot(aes(x = year, y = value, colour = kpi)) + geom_line(aes(group = kpi, y = .fitted), size = 0.5, linetype = "dashed") + geom_point(size = 2) + geom_line(aes(group = kpi)) + transition_reveal(kpi, year) + labs(title = "Trends in University Fundraising KPIs Over Time", subtitle = "Data from Ross-CASE reports", x = "Year", y = 'Value', caption = "y axis labelling omitted due to differences in scale between KPIs", colour = "KPI") + scale_colour_discrete(labels = c("Cash received", "Contactable alumni", "Fundraising staff", "New funds raised")) + scale_fill_discrete() + theme_chris() # animate and save case_animation <- animate(case_animate, height = 500, width = 800) anim_save("case_animation.gif", animation = case_animation)

cea4f1e96f40a1c560cae6bf13fcdae1c6895e44

0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb

/cran/paws.storage/man/backup_create_backup_vault.Rd

83e644fc1c102fc6d87ea7aa771a1800b8fbf9d5

[ "Apache-2.0" ]

permissive

paws-r/paws

196d42a2b9aca0e551a51ea5e6f34daca739591b

a689da2aee079391e100060524f6b973130f4e40

refs/heads/main

2023-08-18T00:33:48.538539

2023-08-09T09:31:24

154,419,943

293

45

NOASSERTION

2023-09-14T15:31:32

2018-10-24T01:28:47

R

UTF-8

R

false

true

1,609

rd

backup_create_backup_vault.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/backup_operations.R \name{backup_create_backup_vault} \alias{backup_create_backup_vault} \title{Creates a logical container where backups are stored} \usage{ backup_create_backup_vault( BackupVaultName, BackupVaultTags = NULL, EncryptionKeyArn = NULL, CreatorRequestId = NULL ) } \arguments{ \item{BackupVaultName}{[required] The name of a logical container where backups are stored. Backup vaults are identified by names that are unique to the account used to create them and the Amazon Web Services Region where they are created. They consist of letters, numbers, and hyphens.} \item{BackupVaultTags}{Metadata that you can assign to help organize the resources that you create. Each tag is a key-value pair.} \item{EncryptionKeyArn}{The server-side encryption key that is used to protect your backups; for example, \verb{arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab}.} \item{CreatorRequestId}{A unique string that identifies the request and allows failed requests to be retried without the risk of running the operation twice. This parameter is optional. If used, this parameter must contain 1 to 50 alphanumeric or '-_.' characters.} } \description{ Creates a logical container where backups are stored. A \code{\link[=backup_create_backup_vault]{create_backup_vault}} request includes a name, optionally one or more resource tags, an encryption key, and a request ID. See \url{https://www.paws-r-sdk.com/docs/backup_create_backup_vault/} for full documentation. } \keyword{internal}

2a5dd8493029ecad3647acc8f67fef555fec4628

3f0498c8f6463302b1a8ca3ea5d357f41cd08e60

/Composite.R

601773e41093ee63684eaf75fa32dd10e3033a63

[]

no_license

YTTom/R

3f30f29597e8d41724678b670e674948753b569a

38fa42889ff1abc856eefe086e225962e07c6062

refs/heads/master

2022-11-06T15:50:29.478437

2020-06-27T06:16:47

273,939,345

0

null

UTF-8

R

false

814

r

Composite.R

#讀取檔案(路徑要改成自己的) data <- read.csv('~/Downloads/data.csv') #抓取106和106年度的資料 year106<-data[年度=='106',] year107<-data[年度=='107',] #抓取總人數的資料 year106_people <- year106[,c(4)] year107_people <- year107[,c(4)] #將總人數轉換為matrix(向量) matrix106<-matrix(year106_people) matrix107<-matrix(year107_people) #合併兩年的matrix merge_matrix<-cbind(matrix106,matrix107) #補上欄位名稱 rownames(merge_matrix) <- c('英國','美國','日本') colnames(merge_matrix) <- c('106','107') #設定中文編碼和字體(原來會顯示亂碼) Sys.setlocale(category="LC_ALL",locale="en_US.UTF-8") par(family='宋體-繁細體') #繪製長條圖 barplot(merge_matrix,beside=TRUE,, xlab="國別", ylab="留學生人數",family="宋體-繁細體")

444efeea20bd9a73b9c2aa86708aa506aedc0142

d2c7b6f677eb501b6f08c54fce7aebaf4119ae15

/man/plot.ssgraph.Rd

b62403c6c493c09e29afc4b460b8d0f2b36129de

[]

no_license

cran/ssgraph

9b792a284ee5ca70c24bbeeaf998fe769ef323db

15e27003a9ef1bf99ccc881f255853e309e17914

refs/heads/master

2023-01-12T03:58:58.043221

2022-12-24T12:30:02

130,663,048

2

0

null

UTF-8

R

false

1,583

rd

plot.ssgraph.Rd

\name{plot.ssgraph} \alias{plot.ssgraph} \title{ Plot function for \code{S3} class \code{"ssgraph"} } \description{ Visualizes structure of the selected graphs which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability. } \usage{ \method{plot}{ssgraph}( x, cut = 0.5, ... ) } \arguments{ \item{x }{An object of \code{S3} class \code{"ssgraph"}, from function \code{\link{ssgraph}}. } \item{cut }{Threshold for including the links in the selected graph based on the estimated posterior probabilities of the links; See the examples. } \item{\dots}{System reserved (no specific usage).} } \references{ Mohammadi, R. and Wit, E. C. (2019). \pkg{BDgraph}: An \code{R} Package for Bayesian Structure Learning in Graphical Models, \emph{Journal of Statistical Software}, 89(3):1-30 Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, \emph{Bayesian Analysis}, 10(1):109-138 Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, \emph{Journal of the Royal Statistical Society: Series C}, 66(3):629-645 } \author{ Reza Mohammadi \email{a.mohammadi@uva.nl} } \seealso{ \code{\link{ssgraph}} } \examples{ \dontrun{ # Generating multivariate normal data from a 'scale-free' graph data.sim <- bdgraph.sim( n = 60, p = 7, graph = "scale-free", vis = TRUE ) ssgraph.obj <- ssgraph( data = data.sim ) plot( ssgraph.obj ) plot( ssgraph.obj, cut = 0.3 ) } } \keyword{hplot}

01d4a7aaddbf9e56e4582b228f734bf4033eba3d

bd454c45d38cc48f6247d9dec829de0533793549

/man/piat.feedback.no_score.Rd

242e74f2aad06c5a6ef4c5bd284b9d50ff5d6064

[ "MIT" ]

permissive

pmcharrison/piat

f445431e6d59cbf63228619547ad4e078af58c2f

73c77acf379c233480819738214187cd9b1ba3f7

refs/heads/master

2023-08-14T17:02:04.665315

2023-07-26T21:27:39

131,727,383

2

3

NOASSERTION

2022-12-21T10:09:03

2018-05-01T15:09:06

R

UTF-8

R

false

true

406

rd

piat.feedback.no_score.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/feedback.R \name{piat.feedback.no_score} \alias{piat.feedback.no_score} \title{PIAT feedback (no score)} \usage{ piat.feedback.no_score(dict = piat::piat_dict) } \arguments{ \item{dict}{The psychTestR dictionary used for internationalisation.} } \description{ Here the participant is given no feedback at the end of the test. }

876aa0e93b35623f6b4d50db30e6392b133124df

1522b308afd42bc80bf4b5192c2d1670f8579c26

/man/Fragman-package.Rd

4de27f2a5cee6ffb60d2a65dd7431b30bf8cead1

[]

no_license

covaruber/Fragman

2c1830036ccd968c1d4df82983c0cb74d7c84651

55fd3627d9f6699ad97f1643883ce93387b382c3

refs/heads/master

2020-04-11T21:44:30.948814

2018-12-17T10:45:13

162,114,727

0

null

UTF-8

R

false

7,194

rd

Fragman-package.Rd

\name{Fragman-package} \alias{Fragman} \docType{package} \author{ Giovanny Covarrubias-Pazaran, Luis Diaz-Garcia, Brandon Schlautman, Walter Salazar, Juan Zalapa. } \title{Fragment analysis and automatic scoring} \description{Fragman is a package designed for Fragment analysis and automatic scoring of biparental populations (such as F1, F2, BC types) and populations for diversity studies. The program is designed to read files with FSA extension (which stands for FASTA-type file and contains lectures for DNA fragments), and .txt files from Beckman CEQ 8000 system, and extract the DNA intensities from the channels/colors where they are located, based on ABi machine plattforms to perform sizing and allele scoring. The core of the package and the workflow of the fragment analysis rely in the following 4 functions; 1) \code{\link{storing.inds}}(function in charge of reading the FSA or txt(CQS) files and storing them with a list structure) 2) \code{\link{ladder.info.attach}} (uses the information read from the FSA files and a vector containing the ladder information (DNA size of the fragments) and matches the peaks from the channel where the ladder was run with the DNA sizes for all samples. Then loads such information in the R environment for the use of posterior functions) 3) \code{\link{overview2}} (create friendly plots for any number of individuals specified and can be used to design panels (\code{\link{overview2}}) for posterior automatic scoring (like licensed software does), or make manual scoring (\code{\link{overview}}) of individuals such as parents of biparental populations or diversity populations) 4) The \code{\link{score.markers}} (function score the alleles by finding the peaks provided in the panel (if provided), otherwise returns all peaks present in the channel). Thisfinal function can be automatized if several markers are located in the same channel by creating lists of panels taking advantage of R capabilities and data structures. ** Sometimes during the ladder sizing process some samples can go wrong for several reasons related to the sample quality (low intensity in ladder channel, extreme number of noisy peaks, etc.), because of that we have introduced \code{\link{ladder.corrector}} function which allows the user to correct the bad samples by clicking over the real peaks, by default the \code{\link{ladder.info.attach}} function returns the names of the samples that had a low correlation with the expected peaks. When automatic scoring is not desired the function \code{\link{overview}} can be used for getting an interactive session and click over the peaks (using the \code{\link{locator}} function) in order to get the allele sizes. } \section{Contact}{ Feel free to contact us with questions and improvement suggestions at: covarrubiasp@wis.edu Just send a sample file with your question to recreate the issue or bug reported along with vector for your ladder. } \section{Citation}{ We have spent valuable time developing this package, please cite it in your publication: Covarrubias-Pazaran G, Diaz-Garcia L, Schlautman B, Salazar W, Zalapa J. Fragman: An R package for fragment analysis. 2016. BMC Genetics 17(62):1-8. } \references{ Covarrubias-Pazaran G, Diaz-Garcia L, Schlautman B, Salazar W, Zalapa J. Fragman: An R package for fragment analysis. 2016. BMC Genetics 17(62):1-8. Robert J. Henry. 2013. Molecular Markers in Plants. Wiley-Blackwell. ISBN 978-0-470-95951-0. Ben Hui Liu. 1998. Statistical Genomics. CRC Press LLC. ISBN 0-8493-3166-8. } \keyword{ package } \seealso{ http://cggl.horticulture.wisc.edu/home-page/ } \examples{ ## ================================= ## ## ================================= ## ## Fragment analysis requires ## 1) loading your data ## 2) matching your ladder ## 3) define a panel for scoring ## 4) score the samples ## ================================= ## ## ================================= ## ##################### ## 1) Load your data ##################### ### you would use something like: # folder <- "~/myfolder" # my.plants <- storing.inds(folder) ### here we just load our sample data and use the first 2 plants ?my.plants data(my.plants) my.plants <- my.plants[1:2] class(my.plants) <- "fsa_stored" # plot(my.plants) # to visualize the raw data ####################### ## 2) Match your ladder ####################### ### create a vector indicating the sizes of your ladder and do the match my.ladder <- c(50, 75, 100, 125, 129, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375) ladder.info.attach(stored=my.plants, ladder=my.ladder) ### matching your ladder is a critical step and should only happen once per batch of ### samples read ###****************************************************************************************### ### OPTIONAL: ### If the ladder.info attach function detects some bad samples ### that you can correct them manually using ### the ladder.corrector() function ### For example to correct one sample in the previous data ### ladder.corrector(stored=my.plants, #to.correct="FHN152-CPN01_01A_GH1x35_152-148-209_717-704-793_367-382-381.fsa", #ladder=my.ladder) ###****************************************************************************************### ####################### ## 3) Define a panel ####################### ### In fragment analysis you usually design a panel where you indicate ### which peaks are real. You may use the overview2 function which plots all the ### plants in the channel you want in the base pair range you want overview2(my.inds=my.plants, channel = 2:3, ladder=my.ladder, init.thresh=5000) ### You can click on the peaks you think are real, given that the ones ### suggested by the program may not be correct. This can be done by using the ### 'locator' function and press 'Esc' when you're done, i.e.: # my.panel <- locator(type="p", pch=20, col="red")$x ### That way you can click over the peaks and get the sizes ### in base pairs stored in a vector named my.panel ### Just for demonstration purposes I will use the suggested peaks by ### the program using overview2, which will return a vector with ### expected DNA sizes to be used in the next step for scoring ### we'll do it in the 160-190 bp region my.panel <- overview2(my.inds=my.plants, channel = 3, ladder=my.ladder, init.thresh=7000, xlim=c(160,190)); my.panel ########################## ## 4) Score the samples ########################## ### When a panel is created is time to score the samples by providing the initial ### data we read, the ladder vector, the panel vector, and our specifications ### of channel to score (other arguments are available) ### Here we will score our samples for channel 3 with our panel created previously res <- score.markers(my.inds=my.plants, channel = 3, panel=my.panel$channel_3, ladder=my.ladder, electro=FALSE) ### Check the plots and make sure they were scored correctly. In case some samples ### are wrong you might want to use the locator function again and figure out ### the size of your peaks. To extract your peaks in a data.frame do the following: final.results <- get.scores(res) final.results }

e45e5c9ae5fa18baaaaeacfe138207c9c584a6c6

0e6d8c50bd6c0ef5e3c97b17626bb42c9e3d8eff

/R/RcppExports.R

834ce8db68665acb5c17090dbe87c748e4492e6b

[]

no_license

tobiasmuetze/gscounts

e04903db1993df538065cc427c45f01d2904796f

1c614a3fd36be86a5608b83df91df040fbf0d98d

refs/heads/master

2021-11-23T23:36:17.203662

2021-11-01T16:14:35

92,069,741

2

0

null

UTF-8

R

false

503

r

RcppExports.R

# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 cpp_calc_critical <- function(r, lower, upper, error_spend, information, theta, side) { .Call('_gscounts_cpp_calc_critical', PACKAGE = 'gscounts', r, lower, upper, error_spend, information, theta, side) } cpp_pmultinorm <- function(r, lower, upper, information, theta) { .Call('_gscounts_cpp_pmultinorm', PACKAGE = 'gscounts', r, lower, upper, information, theta) }

f9f1bc3dd54383ff64b71930259786ac326b7109

9580717f9f09fe026dee8224b35f3f72c9f78675

/man/create_net_animate.Rd

516314018fae56531e704ad4bdaf72c2282197d8

[]

no_license

sctyner/netvizinf

dc0fed9791ae7c29255ff133b010ed5f6bc39a17

d9cd0e249ad6b351b59b9478d238dbaf0a8762ce

refs/heads/master

2021-05-03T22:30:58.889245

2017-10-26T19:52:35

71,607,611

1

0

null

UTF-8

R

false

true

372

rd

create_net_animate.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/crete-net-animate.R \name{create_net_animate} \alias{create_net_animate} \title{Construct plots to be animated.} \usage{ create_net_animate(dat) } \arguments{ \item{dat}{\code{data.frame} The output of a call to \code{\link{tween_microsteps}}} } \description{ Construct plots to be animated. }

3f87ab69eb4dfe1532b38316ecaf047610ebed19

b8aed4a0a0f267d15c97ddc8957949999b9f5004

/R/fsf_query.R

b0151eb27f60cac5ecd00670603d577317d8860e

[]

no_license

jtbradt/firstStreetAPI

af0d721bd9c76c8055a407043bf79ff33902a356

7870fc59dfb0e7d9e630a486ba356723ea9bdd0f

refs/heads/master

2023-06-12T14:01:03.068212

2021-07-13T15:27:50

null

0

null

UTF-8

R

false

718

r

fsf_query.R

#' FSF query function #' #' This function constructs a fsf API request: #' @param api.cat is one of FSF's 7 API categories #' @param api is one of FSF's 18 APIs #' @param arg is a query argument #' @keywords fsf.query #' @export fsf.query <- function(api.cat, api, arg) { # Create path: path <- paste(pkg.env$api.version, api.cat, api, sep = "/") # Add arguments to path: path <- paste0(path, "/", paste(arg, collapse = ";")) # Query FSF API: url <- httr::modify_url("https://api.firststreet.org/", path = path) resp <- httr::GET(url, query = list(key = pkg.env$api.key)) if (resp$status_code == "200") { return(resp) } else { return(resp$status_code) } }

73e3f96948fee51fb6f06e96037aa95aa32f324b

05884bd8afb3222aec86c6a2b363e67ed3c64590

/toolbox/examples/ecoex.R

d1171d45a50f9684f63aa3a1fb5dbde781072cc7

[]

no_license

nmarticorena/mineria_datos

bcfbea31e6de6f292e4404068b360638ab8a3cbb

6e3f22c2fb79fe551a5d8c94136f495638088813

refs/heads/master

2020-03-09T00:36:28.806062

2018-06-14T03:12:35

128,492,056

0

null

UTF-8

R

false

118

r

ecoex.R

# eco example 1 x=1:10 z=eco(x,3) #x_1=(0,1,2,3...) #x_2=(0,0,1,2,...) #x_3=(0,0,0,1,2...) #z=x_1+x_2+x_3 z

738f7243f740f9e22f605a88da362a4f9ee50987

5a3e9cad940ab62c63177618397f4f7fa91069cc

/Destructive_harvest_2018_BRA_LAU_GAL_WUU.R

6cbcfe8c2da0db18db49a16cc0725f5697ecdaf7

[]

no_license

mirodemol/destr_valid_BGLW

ade749e5aca48937ed4b38cf974fa4f5abfd1287

312461570b48ee7301c36471e00754758bb9d983

refs/heads/master

2020-03-15T11:34:34.753516

2018-05-15T13:52:05

132,123,223

0

null

UTF-8

R

false

7,925

r

Destructive_harvest_2018_BRA_LAU_GAL_WUU.R

#### # This code is to analyse the 2018 field data from destructive harvests # written by Miro #### remove(list=ls()) # load packages and functions ---- delete.na <- function(DF, n=0) { DF[rowSums(is.na(DF)) <= n,]} # read data, check data ---- setwd('C:/Users/midemol/Dropbox/Doctoraat/fun_in_R/destr_valid_BGLW') inventory = read.csv('inventory_BRA_LAU_WUU_GAL.csv') str(inventory) # clean up inventory inventory=inventory[!(inventory$Tree_Code=='GAL-12'), ] # remove GAL12 inventory$site_code=factor(substring(inventory$Tree_Code,1,3)) # make site code factors (4 factor levels, one per site) levels(inventory$Tree_height_felled_flb_.m.)[levels(inventory$Tree_height_felled_flb_.m.)=="9,3 / 7,6"] <- "9.3" # forking tree. keep only max height of flb for now. inventory$Tree_height_felled_flb_.m.=as.numeric(as.character(inventory$Tree_height_felled_flb_.m.)) # the dead braches of WUU-01 are not in the main database, we add them here: id=c('pom','tien1','tien2');fresh_volume=c(5050,1084,3235);fresh_mass=c(5501,797,2814);dry_mass=c(2787,501,1540);WUU01=data.frame(id,fresh_volume,fresh_mass,dry_mass) # add columns inventory$WSG_coreA=inventory$coreA_dry_mass_.g./inventory$coreA_fresh_volume_.mL. inventory$WSG_coreB=inventory$coreB_dry_mass_.g./inventory$coreB_fresh_volume_.mL. inventory$WSG_cores=(inventory$WSG_coreA+inventory$WSG_coreB)/2 # exploratory analyses & descriptive statistics ---- summary(inventory) attach(inventory) mean_dbh=mean(DBH) mean_th=mean(Tree_height_felled_.m.) aggregate(Tree_height_felled_.m., list(site=site_code), FUN=function(x) c(mean(x,na.rm = T), min(x,na.rm = T), max(x,na.rm = T))) par(pty="s") plot(Circumference_standing_.cm.,Circ_felled_.cm.,col=site_code,pch=c(0,1,2,3)[site_code]) plot(Tree_height_felled_.m.,Tree_height_.m.,col=site_code,pch=c(0,1,2,3)[site_code],xlim = c(15,26),ylim = c(15,26),pty='s');abline(0,1) plot(Tree_height_felled_.m.,Tree_height_felled_flb_.m.,col=site_code,pch=c(0,1,2,3)[site_code],pty='s') # relations between fresh mass and dbh/h #pdf("C:/Users/midemol/Dropbox/Doctoraat/fun_in_R/figures/DBH2TH_biomass.pdf",4,4) par(mar = c(4, 4, 1, 0.2)) plot(Tree_height_felled_.m.,Crown_weight_.kg. + Stem_weight_.kg.,col=site_code,pch=c(0,1,2,3)[site_code],pty='s',xlab = 'Total tree length (m)',ylab = 'Tree total fresh mass (kg)') plot(DBH,Crown_weight_.kg. + Stem_weight_.kg.,col=site_code,pch=c(0,1,2,3)[site_code],pty='s',xlab = 'DBH (cm)',ylab = 'Tree total fresh mass (kg)') with(inventory,plot(DBH^2*Tree_height_felled_.m.,Crown_weight_.kg. + Stem_weight_.kg.,xlab=expression(~ DBH^{2} ~ x~Tree ~length ~ (cm^{2} ~ m)),ylab='Tree total fresh mass (kg)',col=site_code,pch=c(0,1,2,3)[site_code])) #abline(lm(Crown_weight_.kg. + Stem_weight_.kg.~I(DBH^2*Tree_height_felled_.m.)+0,data=inventory)) for(i in c(1:4)){ lm_temp=lm(Crown_weight_.kg. + Stem_weight_.kg.~I(DBH^2*Tree_height_felled_.m.)+0,data=inventory[inventory$site_code==c("BRA","GAL","LAU","WUU")[i],]) abline(lm_temp,col=i)} legend('topleft',title='Site',c("BRA (.995)","GAL (.991)","LAU (.983)","WUU (.987)"), col=seq_along(levels(factor(site_code))), pch=c(0,1,2,3),bty='n', cex=.75) dev.off detach(inventory) # standing vs felled ---- #pdf("C:/Users/midemol/Dropbox/Doctoraat/fun_in_R/figures/DBH_DBH.pdf",4,4) #pdf("C:/Users/midemol/Dropbox/Doctoraat/fun_in_R/figures/TH_TH.pdf",4,4) #pdf("C:/Users/midemol/Dropbox/Doctoraat/fun_in_R/figures/THflb_THflb.pdf",4,4) par(mar = c(4, 3, 1, 0)) with(inventory,plot(Circumference_standing_.cm.,Circumference_standing_.cm.-Circ_felled_.cm., xlab='Circumference of standing tree (cm)',ylab='Difference (standing - felled, cm)',col=site_code,pch=c(0,1,2,3)[site_code])) abline(h = 0, lty = 2) with(inventory,plot(Tree_height_.m.,Tree_height_.m.- Tree_height_felled_.m.,ylim=c(-4,4),xlab='Forestry Pro tree height (m)',ylab='Difference (Forestry Pro - felled, m)',col=site_code,pch=c(0,1,2,3)[site_code])) abline(h = 0, lty = 2) with(inventory,plot(Tree_height_flb_.m.,Tree_height_flb_.m.- Tree_height_felled_flb_.m.,ylim=c(-4,4),xlab='Forestry Pro tree height till first living branch (m)',ylab='Difference (Forestry Pro - felled, m)',col=site_code,pch=c(0,1,2,3)[site_code])) abline(h = 0, lty = 2) dev.off() legend('topleft',title='Site',c("BRA","GAL","LAU","WUU"), col=seq_along(levels(factor(site_code))), pch=c(0,1,2,3),bty='n', cex=.75) # wood cores and wood density ---- with(inventory,plot(DBH,WSG_cores,xlab='DBH (cm)',ylab='WSG cores',col=site_code,pch=c(0,1,2,3)[site_code])) with(inventory,plot(DBH,Stem_disks_fresh_mass_.g._POM/Stem_disks_fresh_volume_.ml._POM,xlab='DBH (cm)',ylab='Fresh disc density',col=site_code,pch=c(0,1,2,3)[site_code])) with(inventory,plot(DBH,Dry_mass_POM../Stem_disks_fresh_volume_.ml._POM,xlab='DBH (cm)',ylab='Fresh disc density',col=site_code,pch=c(0,1,2,3)[site_code])) with(inventory,plot(WSG_coreA,abs(WSG_coreA- WSG_coreB),xlab='WSG core A',ylab='WSG core A - WSG core B',col=site_code,pch=c(0,1,2,3)[site_code])); abline(h = 0, lty = 2) boxplot(WSG_cores ~ site_code, data = inventory,ylab='WSG from cores (g/cu. cm)') # dead wood from WUU01 (WSG=wood specific gravity, dmc=dry matter content, FWD=fresh wood density) WUU01$WSG=WUU01$dry_mass/WUU01$fresh_volume;WUU01$DMC=WUU01$dry_mass/WUU01$fresh_mass;WUU01$FWD=WUU01$fresh_mass/WUU01$fresh_volume #pdf("C:/Users/midemol/Dropbox/Doctoraat/fun_in_R/figures/fresh_biomass_boxplot.pdf",4,4) par(mar = c(3, 4, 1, 1)) boxplot(DBH ~ site_code, data = inventory,ylab='DBH (cm)') boxplot(Tree_height_felled_.m. ~ site_code, data = inventory,ylab='Tree length (m)') boxplot(Stem_weight_.kg. ~ site_code, data = inventory) boxplot(Crown_weight_.kg. ~ site_code, data = inventory) boxplot(Crown_weight_.kg. + Stem_weight_.kg.~ site_code, data = inventory,ylab='Tree total fresh mass (kg)') dev.off() # WD and water content at different heights ---- #make a dataframe "discs" with columns (height, site code, fresh mass and volume and dry mass) discs=data.frame(130,inventory$site_code,inventory$Stem_disks_fresh_mass_.g._POM,inventory$Stem_disks_fresh_volume_.ml._POM,inventory$Dry_mass_POM..) names(discs)=c('h','site_code','fresh_mass','fresh_volume','dry_mass') for (i in c(3,6,9,12,15,18,21,24)){ discs_temp=data.frame(i*100,inventory$site_code,inventory[[paste('Fresh_mass_POM.',i,'m',sep = '')]],inventory[[paste('Fresh_volume_POM.',i,'m',sep = '')]],inventory[[paste('Dry_mass_POM.',i,'m',sep = '')]]) names(discs_temp)=c('h','site_code','fresh_mass','fresh_volume','dry_mass') discs=rbind(discs,discs_temp) rm(discs_temp) } #discs0=delete.na(discs) discs=delete.na(discs,n=1) # max number of NA in discs per row is n=1 #discs2=delete.na(discs,n=2) # max number of NA in discs per row is n=2 # calculate wsg etc in discs discs$WSG=discs$dry_mass/discs$fresh_volume;discs$DMC=discs$dry_mass/discs$fresh_mass;discs$FWD=discs$fresh_mass/discs$fresh_volume # plot the results! with(discs,plot(WSG,h,xlab='WSG',ylab='Height of disc (cm)',col=site_code,pch=c(0,1,2,3)[site_code])) with(discs,plot(DMC,h,xlab='DMC',ylab='Height of disc (cm)',col=site_code,pch=c(0,1,2,3)[site_code])) with(discs,plot(FWD,h,xlab='FWD',ylab='Height of disc (cm)',col=site_code,pch=c(0,1,2,3)[site_code])) legend('topleft',title='Site',c("BRA","GAL","LAU","WUU"), col=seq_along(levels(factor(discs$site_code))), pch=c(0,1,2,3),bty='n', cex=.75)

fd77c009690f53f04b653ddeaef5046ed9c6a99a

b71ce56fa3133ad7040e493a525cfe7ca0b07b2f

/man/metroTilesGrid.Rd

ebbe396c09dc2f2561e1458a7cc66806e471417e

[]

no_license

bright-spark/shinyMetroUi

2a22ede6e35b91fe5780e4f2bebb83d57332d1fb

2c2acfe9abd3f1d444d3f3d95c99d441532f4d8a

refs/heads/master

2023-03-19T03:42:01.896849

2019-12-20T16:04:10

null

0

null

UTF-8

R

false

true

1,948

rd

metroTilesGrid.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metro-tiles.R \name{metroTilesGrid} \alias{metroTilesGrid} \title{Create a Metro 4 Tiles Grid} \usage{ metroTilesGrid(..., group = FALSE, title = NULL, size = 2) } \arguments{ \item{...}{Insert metroTile inside.} \item{group}{Whether tiles are displayed by group. FALSE by default.} \item{title}{If group is TRUE, the group title.} \item{size}{Tile group size: between 1 and 10.} } \description{ Build a Metro grid for tiles } \examples{ if(interactive()){ library(shiny) library(shinyMetroUi) shiny::shinyApp( ui = metroPage( metroTilesGrid( metroTile(size = "small", color = "red"), metroTile(size = "small", color = "green"), metroTile(size = "small", color = "blue", col_position = 1, row_position = 2), metroTile(size = "small", color = "orange", col_position = 2, row_position = 2), metroTile(size = "wide", color = "brown"), metroTile(size = "medium", color = "green", selected = TRUE) ), br(), br(), br(), metroTilesGrid( group = TRUE, size = 2, metroTile( size = "small", color = "indigo", icon = "github", url = "https://github.com/olton/Metro-UI-CSS"), metroTile(size = "small", color = "green", icon = "envelop"), metroTile(size = "small", color = "blue", col_position = 1, row_position = 2), metroTile(size = "small", color = "orange", col_position = 2, row_position = 2), metroTile( size = "wide", color = "pink", sliderInput("obs", "Number of observations:", min = 0, max = 1000, value = 500 ) ), metroTile( size = "large", color = "green", selected = TRUE, plotOutput("distPlot") ) ) ), server = function(input, output) { output$distPlot <- renderPlot({ hist(rnorm(input$obs)) }) } ) } } \author{ David Granjon, \email{dgranjon@ymail.com} }

76b3f7b269a9d39bb81acef8af72bad24010d85d

9f89cc309f9ddf8765f43605409de498d5e8f0e3

/Assignment4/Assignment4.R

d61482a4d208286425df02ab9ce9bfa243b26822

[]

no_license

htdrajiv/r_programming

386f9266c04778d59a335ab5c0b1f4918259dd80

bb780777069df22958aafdaa203a813be90cd54c

refs/heads/master

2021-01-20T18:27:21.465794

2016-08-17T02:09:35

65,594,777

2

0

null

UTF-8

R

false

3,384

r

Assignment4.R

data1 <- read.csv("E:/Projects/R_Programming/Data/Divvy_Stations_Trips_2014_Q1Q2/Divvy_Trips_2014_Q1Q2.csv") data2 <- read.csv("E:/Projects/R_Programming/Data/Divvy_Stations_Trips_2014_Q3Q4/Divvy_Trips_2014-Q3-07.csv") data3 <- read.csv("E:/Projects/R_Programming/Data/Divvy_Stations_Trips_2014_Q3Q4/Divvy_Trips_2014-Q3-0809.csv") data4 <- read.csv("E:/Projects/R_Programming/Data/Divvy_Stations_Trips_2014_Q3Q4/Divvy_Trips_2014-Q4.csv") fileData <- rbind(data1,data2,data3,data4) library(sqldf) subscriberData <- sqldf("select * from fileData fd where fd.usertype = 'Subscriber' ") customerData <- sqldf("select * from fileData fd where fd.usertype = 'Customer' ") subscriberData$numericGender[subscriberData$gender=="Male"] <- "1" subscriberData$numericGender[subscriberData$gender=="Female"] <- "0" asDateStartTimeSubscriber <- strptime(subscriberData$starttime, format = c("%m/%d/%Y %H:%M")) asDateStartTimeCustomer <- strptime(customerData$starttime, format = c("%m/%d/%Y %H:%M")) dfSubscriber <- data.frame(months = months(asDateStartTimeSubscriber), dayOfWeek = weekdays(asDateStartTimeSubscriber), hours = as.numeric(format(asDateStartTimeSubscriber,"%H")), lengthOfRentalsInHours = subscriberData$tripduration/3600, age = as.numeric(format(Sys.Date(),'%Y')) - as.numeric(subscriberData$birthyear,format("%Y")), gender = subscriberData$gender ) dfSubscriberNumeric <- data.frame(months = as.numeric(format(asDateStartTimeSubscriber,"%m")), dayOfWeek = as.numeric(format(asDateStartTimeSubscriber,"%u")), hours = as.numeric(format(asDateStartTimeSubscriber,"%H")), lengthOfRentalsInHours = as.numeric(subscriberData$tripduration/3600), age = as.numeric(format(Sys.Date(),'%Y')) - as.numeric(subscriberData$birthyear,format("%Y")), gender = as.numeric(format(subscriberData$numericGender)) ) View(dfSubscriberNumeric) dfCustomer <- data.frame(months = months(asDateStartTimeCustomer), dayOfWeek = weekdays(asDateStartTimeCustomer), hours = as.numeric(format(asDateStartTimeCustomer,"%H")), lengthOfRentalsInHours = customerData$tripduration/3600 ) dfCustomerNumeric <- data.frame(months = as.numeric(format(asDateStartTimeCustomer,"%m")), dayOfWeek = as.numeric(format(asDateStartTimeCustomer,"%u")), hours = as.numeric(format(asDateStartTimeCustomer,"%H")), lengthOfRentalsInHours = customerData$tripduration/3600 ) write.csv(dfCustomer,"E:/Projects/R_Programming/Data/customerData.csv") write.csv(dfSubscriber,"E:/Projects/R_Programming/Data/subscriberData.csv") write.csv(dfCustomerNumeric,"E:/Projects/R_Programming/Data/customerDataNumeric.csv") write.csv(dfSubscriberNumeric,"E:/Projects/R_Programming/Data/subscriberDataNumeric.csv") install.packages("xlsx") library("xlsx") write.xlsx(dfCustomer,"C:/Users/985176/Desktop/WorkingDirectory/customerData.xlsx") write.xlsx(dfSubscriber,"C:/Users/985176/Desktop/WorkingDirectory/subscriberData.xlsx") fit <- kmeans(dfCustomer, 5)

9282d5297e11663bfaf6f87ee3ebadaab9403ae1

c3f09d043409d3f30cc1de6732a55d4a91d6a0a7

/scripts/script_0.R

336d20cdbe7fbf8f15a7cb86b6a40a1c4efe67bf

[]

no_license

mark-andrews/repdemoproj

77afdbeab6e56bc02fb26b88b0321070bd571a41

f51871f1b22347f39decf708a21c617da10bc3c6

refs/heads/master

2022-11-11T14:20:28.881053

2020-07-04T15:22:44

2020-07-04T15:28:35

276,920,438

1

0

null

UTF-8

R

false

139

r

script_0.R

library(here) library(bayeslmm) result <- lmm(rt ~ day + (1|subject), data = sleepstudy_df) saveRDS(result, file=here('tmp/model_0.rds'))

b6ead726b3b371b0617b94fa377c1af584f168de

b98ece6254219513180cc730f7e26f7f9a277124

/plot3.R

c38b2b6a76673b8bce2e6acb0865ce2e47cc68b4

[]

no_license

OlgaRusyaeva/ExData_Plotting1

543fdf16bba72a3cac2d60170f56325b362a3e63

0c0988359a8be37155b5c0142b09eb3a9a26f6c5

refs/heads/master

2021-01-14T10:23:38.695401

2015-06-07T14:41:30

35,160,355

0

null

2015-05-06T13:33:03

null

UTF-8

R

false

920

r

plot3.R

#read data from a file with the dates 2007-02-01 and 2007-02-02 library(sqldf) fileName <- "household_power_consumption.txt" df <- read.csv.sql(fileName, sql='select * from file where Date="1/2/2007" OR Date="2/2/2007"',sep=";",header=T) closeAllConnections() #create new column out of Date and Time columns df$DateTime <- strptime(paste(df$Date,df$Time),"%d/%m/%Y %H:%M:%S") #create a graph of Energy sub metering (three types) in days of the week png("plot3.png") #DateTime-Sub_metering_1 in black plot(df$DateTime,df$Sub_metering_1,type="l",xlab="",ylab="Energy sub metering",ylim=yrange,col="black") #DateTime-Sub_metering_2 in red lines(df$DateTime,df$Sub_metering_2,type="l",col="red") #DateTime-Sub_metering_3 in blue lines(df$DateTime,df$Sub_metering_3,type="l",col="blue") #legend legend("topright",legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),lty=c(1,1,1),col=c("black","red","blue")) dev.off()

06cf0ad90de9811f7e889ae93d8c0014e442490d

0f709b508989fc77d8f1d62ad97ef050e1bbed48

/Week_06_HypothesisTesting/exercises.week.06.hypothesis.testing.applied.R

3742be067332f33af1180cd1a7795d88298989d7

[]

no_license

alekssro/DataScienceBioinf

b1bcf5afa4e0edb38d6cc25cc694bc948d47a54f

968dba2e87af764223e6d15a7a1e510581176a8b

refs/heads/master

2021-08-29T01:43:21.753379

2017-12-13T09:56:16

112,108,656

0

null

UTF-8

R

false

3,485

r

exercises.week.06.hypothesis.testing.applied.R

library(tidyverse) #### Hypothesis testing #### # Our null hypothesis is: minor allele frequencies are the same everywhere on chromosome 6 # Our alternative hypothesis: some places have higher minor allele frequencies than expected (balancing selection) # Load data and calculate maf d = read_delim(file="1000g.allele.frequencies.tsv.gz",delim=c("\t")) d = d %>% filter(population=="EUR") %>% mutate(maf=ifelse(frequency>0.5, 1-frequency, frequency)) %>% filter(maf > 0) %>% mutate(population=factor(population), reference_allele=factor(reference_allele), alternative_allele=factor(alternative_allele)) summary(d) # For all snps calculate a "bin25k" variable based on position, each bin should be 25 kb d = d %>% mutate(bin25k = position %/% 25000) %>% ungroup() #Checkpoint names(d) #[1] "position" "reference_allele" "alternative_allele" "population" "frequency" "maf" "bin25k" d %>% head(3) #1 63979 C T EUR 0.1044 0.1044 2 #2 63980 A G EUR 0.1044 0.1044 2 #3 73938 A G EUR 0.0010 0.0010 2 # For each bin calculate number of snps with maf > 0.2 (n20) , number of snps (n) , a test statistic (ts) = n20/n # Also calculate the position of the bin midpoint (x) # Call the binned results (1 row pr bin) for "br" (binned results) # Checkpoint names(br) #[1] "bin25k" "n20" "n" "ts" "x" br %>% head(5) #1 2 0 3 0.0000000 68958.5 #2 3 0 2 0.0000000 88089.0 #3 4 0 7 0.0000000 111903.5 #4 5 1 7 0.1428571 146700.5 #5 6 2 25 0.0800000 162538.5 # Q: Plot this teststatistic along the chromosome and also visualise the number of snps in each bin # Q: What is the observed p20 = n20/n for the entire chromosome #### Continuing from last week #### # Q: select the best/easiest way of tesing if the observed proportion of SNPs with maf > 0.2 in the bin is higher than expected # Q: List the 10 most significant bins # What is the lowest pvalue? # How many of your bins have p < 0.001? # If all bins followed H0: how many would you expect to have p > 0.001 # Q: Plot the p value as function of bin position # Q: It is really difficult to see the small pvalues - try to mutate a new pvalue2 = -log10(pvalue) and plot it # This is called a Manhattan plot - strong signals will be skycrapers of significance # Q: Do you see a skyscraper? #### Dividing the chromosome into bins with same number of snps #### # Q: Do the same analysis as before but using bins of size 500 snps instead # Basically we just want the Manhattan plot # HINT: d %>% arrange(position) %>% mutate(SNPnumber = row_number()) #### Testing a specific hypothesis #### # Assume that I speculate that the overall frequency of SNPs with maf > 0.05 is really 50% in humans. # NOTE: maf > 0.05 - I call these "high maf snps" # Can you test if some bins of size 100 kb have significantly more high maf SNPs? # Visualize the test results so it is easy to see where significant bins # HINT: Make manhattan plots but also try and remove all bins with less than 50% of the snps having maf > 0.05 #### Bonus question #### # Can you repeat the final test for all three populations? # Do you see a difference between the populations?

871c330a074ba89c695e7142b8fe0694ebc916f3

b98c5cbe6ab6887097e0337376fc56b6cec15996

/man/methyvolc.Rd

375f88455f04ed25e312ac3e9c68c1d869356a00

[ "MIT" ]

permissive

nhejazi/methyvim

bebd0758f8aff2ad06430aac43b82cf0ec4f45b1

7b4ee9f83aa7d2cfd11645fcb0658de2ea7a0df7

refs/heads/master

2021-03-24T13:29:34.450771

2020-04-27T19:11:00

79,256,902

1

MIT

2020-02-06T01:09:32

2017-01-17T18:15:07

TeX

UTF-8

R

false

true

1,512

rd

methyvolc.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{methyvolc} \alias{methyvolc} \title{Volcano plot for methytmle objects} \usage{ methyvolc(x, param_bound = 2, pval_bound = 0.2) } \arguments{ \item{x}{Object of class \code{methytmle} as produced by an appropriate call to \code{methyvim}.} \item{param_bound}{Numeric for a threshold indicating the magnitude of the size of the effect considered to be interesting. This is used to assign groupings and colors to individual CpG sites.} \item{pval_bound}{Numeric for a threshold indicating the magnitude of p-values deemed to be interesting. This is used to assign groupings and colors to individual CpG sites.} } \value{ Object of class \code{ggplot} containing a volcano plot of the estimated effect size on the x-axis and the -log10(p-value) on the y-axis. The volcano plot is used to detect possibly false positive cases, where a test statistic is significant due to low variance. } \description{ Volcano plot for methytmle objects } \examples{ suppressMessages(library(SummarizedExperiment)) library(methyvimData) data(grsExample) var_int <- as.numeric(colData(grsExample)[, 1]) # TMLE procedure for the ATE parameter over M-values with Limma filtering methyvim_out_ate <- suppressWarnings( methyvim( data_grs = grsExample, sites_comp = 25, var_int = var_int, vim = "ate", type = "Mval", filter = "limma", filter_cutoff = 0.1, parallel = FALSE, tmle_type = "glm" ) ) methyvolc(methyvim_out_ate) }

316bd99da2b5173f5fc5951b2891de6ac84c0699

745d585395acad1376d84f8ca1284c13f2db70f0

/man/make.ISOyear.Rd

5837e68752a5193145a0f08625b8db9ce73bcec4

[]

no_license

pik-piam/quitte

50e2ddace0b0e2cbfabf8539a0e08efe6bb68a0b

4f5330695bd3d0e05d70160c1af64f0e436f89ea

refs/heads/master

2023-08-20T04:15:16.472271

2023-08-09T08:14:32

206,053,101

0

8

null

2023-08-09T08:14:34

2019-09-03T10:39:07

R

UTF-8

R

false

true

748

rd

make.ISOyear.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/make.ISOyear.R \name{make.ISOyear} \alias{make.ISOyear} \title{speedily converting years to POSIXct values} \usage{ make.ISOyear(years) } \arguments{ \item{years}{ignored.} } \value{ The \code{\link[=ISOyear]{ISOyear()}} function. } \description{ \ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} } \details{ This function was deprecated because the \code{\link[=ISOyear]{ISOyear()}} function can be used directly. } \examples{ ISOyear <- make.ISOyear() ISOyear(c(2005, 2010, 2100, 1900)) # -> ISOyear(c(2005, 2010, 2100, 1900)) } \keyword{internal}

ba2dde2cda9c740ea85db49eff513c31406aa4a1

1bd99e7010d5314765a4fa482176ee2963e460d5

/tests/testthat/test-expectation.R

d19c12563cda42191971647fbc9e5f536a534820

[]

no_license

hadley/rv2

6470147c77b7ffacd2ea5d4546c27113aed9ab24

a56d359026f9fd8d57fa47aa81430c84af922c89

refs/heads/master

2021-01-24T03:58:07.773158

2017-01-12T02:08:37

15,084,986

7

4

null

UTF-8

R

false

736

r

test-expectation.R

context("Expectation") dice <- rv(1:6) coin <- rv(c(-1, 1)) test_that("expectation correct for known cases", { expect_equal(E(dice), 3.5) expect_equal(E(coin), 0) }) test_that("expectation is additive", { expect_equal(E(dice + coin), E(dice) + E(coin)) expect_equal(E(dice + dice), 2 * E(dice)) expect_equal(E(dice + dice + dice), 3 * E(dice)) }) test_that("expectation is multiplicatve", { expect_equal(E( 6 * dice), 6 * E(dice)) expect_equal(E( 1 * dice), 1 * E(dice)) expect_equal(E(-1 * dice), -1 * E(dice)) expect_equal(E( 0 * dice), 0 * E(dice)) }) test_that("expectation throws error if input not an rv", { expect_error(E(5), "must be an rv object") expect_error(E("a"), "must be an rv object") })

d03e0b1eac3a7b66d792f25ab586fc815b161e76

cbdfc6b1ee1121090a538a74a9408fa5c206a4f8

/R/prev.R

dbc462f6341ca94287f429bae0edd4ca865c03eb

[]

no_license

mplex/multiplex

9d1eb7e1289f2fce4181094f831f6f020f1526d9

4153f723ac0c8d1e42c78fce91c26977d955f329

refs/heads/master

2023-07-21T17:23:08.732663

2023-07-10T12:10:46

65,552,701

23

4

null

UTF-8

R

false

4,249

r

prev.R

prev <- function (x) { if (is.array(x) == FALSE) stop("Data must be a stacked array of square matrices.") if (is.na(dim(x)[3]) == TRUE) { s0 <- data.frame(matrix(ncol = 1L, nrow = 1L)) if (isTRUE(all.equal(replace(x %*% x, x %*% x >= 1L, 1L), x) == TRUE)) s0[1, 1] <- 1L Bx <- array(dim = c(dim(x)[1], dim(x)[2], 2L)) Bx[, , 1] <- as.matrix(x) Bx[, , 2] <- replace(x %*% x, x %*% x >= 1L, 1L) } if (is.na(dim(x)[3]) == FALSE) { tmp0 <- data.frame(matrix(ncol = (dim(x)[1] * dim(x)[2]), nrow = 0L)) for (i in 1:dim(x)[3]) { ifelse(isTRUE(dim(x)[3] > 1L) == TRUE, tmp0[i, ] <- as.vector(x[, , i]), tmp0 <- as.vector(x)) } rm(i) if (isTRUE(is.null(dim(tmp0)) == FALSE) == TRUE) rownames(tmp0) <- dimnames(x)[[3]] if (isTRUE(dim(x)[3] < 2L) == TRUE) x <- array(tmp0, c(dim(x)[1], dim(x)[2])) if (isTRUE(dim(x)[3] > 1L) == TRUE) { tmp <- array(dim = c(dim(x)[1], dim(x)[2], nrow(unique(tmp0)))) for (i in 1:nrow(unique(tmp0))) { tmp[, , i][1:(dim(x)[1] * dim(x)[2])] <- as.numeric(unique(tmp0)[i, ]) } rm(i) if (is.null(dimnames(tmp)[[1]]) == FALSE) dimnames(tmp)[[3]] <- rownames(unique(tmp0)) if (is.null(dimnames(x)[[1]]) == FALSE) dimnames(tmp)[[1]] <- dimnames(tmp)[[2]] <- dimnames(x)[[1]] x <- tmp dimnames(x)[[3]] <- as.list(rownames(unique(tmp0))) } rm(tmp0, tmp) s0 <- data.frame(matrix(ncol = dim(x)[3], nrow = dim(x)[3])) for (k in 1:dim(x)[3]) { for (j in 1:dim(x)[3]) { tmp <- x[, , j] %*% x[, , k] tmp <- replace(tmp, tmp >= 1L, 1L) for (i in dim(x)[3]:1) { if (isTRUE(all.equal(tmp, x[, , i]) == TRUE)) s0[j, k] <- i } } } rm(i, j, k) dimnames(s0)[[1]] <- 1:dim(x)[3] dimnames(s0)[[2]] <- 1:dim(x)[3] if (sum(as.numeric(is.na(s0))) == 0L) Bx <- x if (sum(as.numeric(is.na(s0))) > 0L) { Bx <- array(dim = c(dim(x)[1], dim(x)[2], 0L)) for (i in 1:nrow(s0)) { for (j in 1:length(which(is.na(s0[i, ])))) { if (length(which(is.na(s0[i, ]))) > 0L) Bx <- zbnd(Bx, (replace(x[, , i] %*% x[, , which(is.na(s0[i, ]))[j]], x[, , i] %*% x[, , which(is.na(s0[i, ]))[j]] >= 1L, 1L))) } } rm(i, j) tmp <- data.frame(matrix(ncol = (dim(x)[1] * dim(x)[2]), nrow = 0L)) for (i in 1:dim(Bx)[3]) { tmp[i, ] <- as.vector(Bx[, , i]) } rm(i) xBx <- array(dim = c(dim(x)[1], dim(x)[2], nrow(unique(tmp)))) for (i in 1:nrow(unique(tmp))) { xBx[, , i][1:(dim(Bx)[1] * dim(Bx)[2])] <- as.numeric(unique(tmp)[i, ]) } rm(i) if (is.null(dimnames(xBx)) == FALSE) dimnames(xBx)[[3]] <- (dim(x)[3] + 1L):(dim(xBx)[3] + dim(x)[3]) Bx <- zbnd(x, xBx) rm(xBx, tmp) } } if (is.null(dimnames(x)[[3]]) == FALSE) dimnames(s0)[[2]] <- dimnames(x)[[3]] pct <- round(length(attr(stats::na.omit(as.vector(unlist(s0))), "na.action"))/length(as.vector(unlist(s0))), 2) d <- as.numeric(sort(unlist(s0), decreasing = TRUE))[1] if (isTRUE(d > 7L) == TRUE) { if (isTRUE(pct < 0.5) == TRUE) return(list(`2stpT` = s0, PcU2stpT = pct, ordr = d)) if (isTRUE(pct > 0.5) == TRUE) return(list(`2stpT` = s0, PcU2stpT = pct, ordr = d, Note = c("Complete semigroup construction may take long time"))) } return(list(`2stpT` = s0, PcU2stpT = pct, ordr = d)) }

39e8763bd1cfd9ac71e7333d1e9507853f851e47

236f960cf07b0b68034821234dc6ae45c1bf2e79

/Bayesian Statistics/multiparameter models,HW5-1.R

7a4489ccf4435d36a7b335e23c549af4ea7b9198

[]

no_license

xiaojianzhang/R_life

b001b65eeab429a394f841f62d1c76e6676d2db1

62ad4072079b0fe814f3c5ba57bc3db57358cf6d

refs/heads/master

2021-01-01T04:11:51.997116

2016-05-17T04:12:39

58,959,582

0

null

UTF-8

R

false

2,886

r

multiparameter models,HW5-1.R

#Chapter 5 Exercise 5 #setup library(geoR) ydata <- c(10, 10, 12, 11, 9) n = 5 y_bar = 10.4 s_square = 1.3 #(c) How do the incorrect and correct posterior # distributions differ? #(1)Consider incorrect posterior #draw sigma_square from inverse chi square(n-1, s^2) sample_sigma_square <- rinvchisq(1000, n-1, s_square) #draw miu from normal(y_bar, sigma_square/n) sample_mu <- rnorm(1000, mean=y_bar, sd=sqrt(sample_sigma_square/n)) #sample mean mean_mu <- mean(sample_mu) mean_sigma_square <- mean(sample_sigma_square) print(mean_mu) print(mean_sigma_square) #sample variance var_mu <- var(sample_mu) var_sigma_square <- var(sample_sigma_square) print(var_mu) print(var_sigma_square) #contour plot mu <- seq(0,20,0.05) log_sigma <- seq(-2,4,0.02) sigma <- exp(log_sigma) log_post_mu_log_sigma <- function(mu, sigma, y){ z <- 0 for(i in 1:length(y)){ z <- z + log(dnorm(y[i], mean=mu, sd=sigma)) } return(z) } log_post <- outer(mu, sigma, log_post_mu_log_sigma, ydata) post <- exp(log_post - max(log_post)) contours <- c(.0001,.001,.01,seq(.05,.95,.05)) contour (main="contour plot of incorrect posterior dist", mu, log_sigma, post, levels=contours, xlab="mu", ylab="log sigma", cex=2) #(2)Consider correct posterior log_post_mu_log_sigma <- function(mu, sigma, y){ z <- 0 for(i in 1:length(y)){ z <- z + log(pnorm(y[i] + 0.5, mean=mu, sd=sigma) - pnorm(y[i] - 0.5, mean=mu, sd=sigma)) } return(z) } log_post <- outer(mu, sigma, log_post_mu_log_sigma, ydata) post <- exp(log_post - max(log_post)) contours <- c(.0001,.001,.01,seq(.05,.95,.05)) contour (main="contour plot of correct posterior dist", mu, log_sigma, post, levels=contours, xlab="mu", ylab="log sigma", cex=2) normalized_post <- post / sum(post) post_mu <- rowSums(post) mu_index <- sample (1:length(mu), 500, replace=T, prob=post_mu) mu_sample <- mu[mu_index] for(i in 1:length(post_mu)){ normalized_post[i, ] <- normalized_post[i, ] / post_mu[i] } sigma_square_sample <- rep(NA, 500) for(i in 1:500){ sigma_square_sample[i] <- exp(sample(log_sigma, 1, prob=normalized_post[mu_index[i], ]))^2 } #sample mean mean_mu <- mean(mu_sample) mean_sigma_square <- mean(sigma_square_sample) print(mean_mu) print(mean_sigma_square) #sample variance var_mu <- var(mu_sample) var_sigma_square <- var(sigma_square_sample) print(var_mu) print(var_sigma_square) #(d) draw simulatons from the posterior dist of z. # compute the posterior mean of (z_1 - z_2)^2 z <- matrix(0, 500, 5) for(i in 1:500){ for(j in 1:5){ rn = rnorm(1,mean=mu_sample[i],sd=sqrt(sigma_square_sample[i])) while(rn >= ydata[j] + 0.5 || rn <= ydata[j] - 0.5){ rn = rnorm(1,mean=mu_sample[i],sd=sqrt(sigma_square_sample[i])) } z[i,j] <- rn } } #posterior mean of (z[1]-z[2])^2 print(mean((z[,1]-z[,2])^2))

850d20a2b39db6b1820fb55060ac69129bc20e89

d9112b28db3cdc905fa4ee5abb223b969da81579

/man/PerformPeakAnnotation.Rd

2713d3b7bc6501bd4b3857aee145324b82fa42ca

[]

no_license

wangyongdalt/OptiLCMS

323d4a483fc01ae5ffede01e474640bca31285f4

2ed8b90f7cd54cd38275240c6801d779b00398c0

refs/heads/master

2023-07-27T01:45:54.421922

2021-09-02T13:28:08

null

0

null

UTF-8

R

false

true

2,033

rd

PerformPeakAnnotation.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Perform_functions.R \name{PerformPeakAnnotation} \alias{PerformPeakAnnotation} \title{Perform peak annotation} \usage{ PerformPeakAnnotation(mSet, annotaParam, ncore = 1, running.controller = NULL) } \arguments{ \item{mSet}{mSet object, usually generated by 'PerformPeakProfiling' here.} \item{annotaParam}{The object created using the SetAnnotationParam function, containing user's specified or default parameters for downstream raw MS data pre-processing.} \item{ncore}{annotation running core. Default is 1. Parallel running will be supported soon.} \item{running.controller}{The resuming pipeline running controller. Optional. Don't need to define by hand.} } \value{ will return an mSet object wirh annotation finished } \description{ This function performs peak annotation on the xset object created using the PerformPeakPicking function. } \examples{ data(mSet); newPath <- dir(system.file("mzData", package = "mtbls2"), full.names = TRUE, recursive = TRUE)[c(10, 11, 12)] mSet <- updateRawSpectraPath(mSet, newPath); annParams <- SetAnnotationParam(polarity = 'positive', mz_abs_add = 0.035); ## Perform peak annotation with newly deinfed annParams # mSet <- PerformPeakAnnotation(mSet = mSet, # annotaParam = annParams, # ncore =1) } \references{ Kuhl C, Tautenhahn R, Boettcher C, Larson TR, Neumann S (2012). "CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets." Analytical Chemistry, 84, 283-289. http://pubs.acs.org/doi/abs/10.1021/ac202450g. } \seealso{ \code{\link{ExecutePlan}} and \code{\link{PerformPeakProfiling}} for the whole pipeline. } \author{ Zhiqiang Pang \email{zhiqiang.pang@mail.mcgill.ca}, Jasmine Chong \email{jasmine.chong@mail.mcgill.ca}, and Jeff Xia \email{jeff.xia@mcgill.ca} McGill University, Canada License: GNU GPL (>= 2) }

12350b16a7859436c7676b5714ace1a108826f5f

4d3672136d43264176fe42ea42196f113532138d

/man/Rehab.Rd

5b8691b589fafd59ae224175eb582eb15332d637

[]

no_license

alanarnholt/BSDA

43c851749a402c6fe73213c31d42c26fa968303e

2098ae86a552d69e4af0287c8b1828f7fa0ee325

refs/heads/master

2022-06-10T10:52:15.879117

2022-05-14T23:58:15

52,566,969

5

13

null

2017-07-27T02:06:33

2016-02-26T00:28:07

R

UTF-8

R

false

true

939

rd

Rehab.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/BSDA-package.R \docType{data} \name{Rehab} \alias{Rehab} \title{Rehabilitative potential of 20 prison inmates as judged by two psychiatrists} \format{ A data frame/tibble with 20 observations on four variables \describe{ \item{inmate}{inmate identification number} \item{psych1}{rating from first psychiatrist on the inmates rehabilative potential} \item{psych2}{rating from second psychiatrist on the inmates rehabilative potential} \item{differ}{\code{psych1} - \code{psych2}} } } \usage{ Rehab } \description{ Data for Exercise 7.61 } \examples{ boxplot(Rehab$differ) qqnorm(Rehab$differ) qqline(Rehab$differ) t.test(Rehab$differ) # Or t.test(Rehab$psych1, Rehab$psych2, paired = TRUE) } \references{ Kitchens, L. J. (2003) \emph{Basic Statistics and Data Analysis}. Pacific Grove, CA: Brooks/Cole, a division of Thomson Learning. } \keyword{datasets}

971f4407e94b3a1ee886d555c0f4a50c8c5c9d81

062355817e0682b8eaaefaf0e7477c031895e02f

/test_web_scraping.R

a75394dc903cb26d35e7b54299a6478e0ce20c7d

[]

no_license

informationista/wikiscraping

5f8b7b6e2b5f1211fbbca9805e7e2a53d8a000c8

3fda53a5a91efcc71ca6f90d2dc85ec9cef95946

refs/heads/master

2021-01-12T07:26:47.316999

2017-01-04T20:48:47

76,962,730

0

null

UTF-8

R

false

3,600

r

test_web_scraping.R

library(rvest) library(RCurl) library(plyr) library(tidyr) library(ggplot2) library(httr) library(dplyr) ##create a list of URLs to retrieve ##create the list of years of interest year_list <- as.list(c(2006:2016)) url_df <- function(year){ url <- paste("https://en.wikipedia.org/wiki/Deaths_in_", month.name, "_", year, sep = "") data.frame(year, month.name, url) } df <- lapply(year_list, url_df) %>% rbind.fill() ##this part got the day only - this got combined with the next part where we get the day and the deaths at the same time #get_day <- function(year, month, url){ # day <- read_html(as.character(url)) %>% html_nodes(xpath = '//h3') %>% html_text() # day <- gsub("\\[edit\\]", "", day) %>% as.numeric(as.character(day)) #remove the edit text and non-numerics # day <- day[!is.na(day)] #get rid of NAs introduced in last part # data.frame(year, month, day) #} ##this function takes the data frame and retrieves the days and deaths on those days for each url get_data <- function(year, month, url){ day <- read_html(as.character(url)) %>% html_nodes(xpath = '//h3') %>% html_text() day <- gsub("\\[edit\\]", "", day) %>% as.numeric(as.character(day)) #remove the edit text and non-numerics day <- day[!is.na(day)] #get rid of NAs introduced in last part df_mid <- data.frame(year, month, day, death = as.character(length(day)), stringsAsFactors = FALSE) death <- read_html(as.character(url)) %>% html_nodes("div#mw-content-text.mw-content-ltr") %>% html_nodes("ul") %>% html_text() death <- death[c(-1, -2)] #remove the extraneous content for (i in 1:nrow(df_mid)){ df_mid[i, 4] <- as.character(death[i]) } return(df_mid) } ##use the get day function to retrieve all data for all years/months in the list df_data <- data.frame() for (i in 1:nrow(df)){ test1 <- get_data(df[i, 1], df[i, 2], df[i, 3]) df_data <- rbind(df_data, test1) } ##Now to clean up the death data ##split each person into their own line df_data <- df_data %>% mutate(death = strsplit(as.character(death), "\n")) %>% unnest(death) ##remove any blank lines df_data <- subset(df_data, death != "") ##split the name and age into separate columns, dump the other info cleaned_df <- separate(df_data, col = death, into = c("name", "age"), sep = ",", remove = TRUE, extra = "drop") ##remove any stuff that got into the age column that's not an actual age cleaned_df$age <- as.numeric(as.character(cleaned_df$age)) ##put months in ordered factor for nice charts cleaned_df$month <- factor(cleaned_df$month, levels = month.name) ##Some visualizations #create a style to use in all charts style <- theme(plot.title = element_text(size = 20, face = "bold"), text = element_text(family = "serif")) ##create a chart of deaths by year ggplot(cleaned_df, aes(x = as.factor(year))) + geom_bar(fill = "skyblue", color = "black") + xlab("Year") + ylab("Number of deaths") + ggtitle("Deaths of Notable People on Wikipedia, 2006 - 2016") + style ##chart of deaths by month, all years ggplot(cleaned_df, aes(x = month)) + geom_bar(fill = "khaki1", color = "black") + xlab("Month") + ylab("Number of deaths") + ggtitle("Deaths of Notable People on Wikipedia by Month, 2006 - 2016") + style ##boxplot of age at death ggplot(cleaned_df, aes(x = as.factor(year), y = age)) + geom_boxplot(outlier.colour = "red") + style + ggtitle("Age at Death of Notable People on Wikipedia by Year") + xlab("Year") + ylab("Age at Death") ##get summary data about age at death mean_age <- with(cleaned_df, aggregate(list(age), by = list(year), FUN = function(x) mean(x, na.rm = TRUE)))

58c18088279abc304368b5464c9ff4471711a07c

8ab151cc5bfb154cc4ae4b1d97ddd6b2bedc95fa

/R/filter.date.R

4919c2821e4eb20b7159cec9598b6f0dbec4f709

[]

no_license

arturochian/MetFns

5eafd4bc404edbbdefd27223c5b8a99d32cd048d

5ce9fc52efdac3c2a12aa18282ab71e53aacf115

refs/heads/master

2020-04-06T04:20:15.871591

2014-09-16T00:00:00

null

0

null

UTF-8

R

false

425

r

filter.date.R

filter.date<-function(data,year,month, day.beg,day.end=day.beg) { if(!is.data.frame(data) || !is.numeric(c(year,month,day.beg,day.end)) || year<1984 || (month<1) || (month>12) || (any(c(day.beg,day.end)<1)) || any(c(day.beg,day.end)>31)) stop("invalid input parameter(s) specification") day<-day.mid(data)[,2] data[(data$year==year%%100) & (data$month==month) & (day>=day.beg & day<=day.end),] }

ace5b9f69c36119e52d9e0c60e198f8ed2c11ab3

36b14b336e0efdda255fa3f163af65127e88105f

/man/Problem2.33.Rd

62417be1c52bea0442a5820d811c9f75a3bb5f6d

[]

no_license

ehassler/MontgomeryDAE

31fcc5b46ae165255446e13beee9540ab51d98b3

43a750f092410208b6d1694367633a104726bc83

refs/heads/master

2021-06-24T13:46:19.817322

2021-03-11T16:36:37

2021-03-11T17:13:18

199,803,056

8

1

null

UTF-8

R

false

388

rd

Problem2.33.Rd

\name{Problem2.33} \alias{Problem2.33} \docType{data} \title{Exercise 2.33} \usage{data("Problem2.33")} \format{A data frame with 20 observations on the following variable(s).\describe{ \item{\code{Uniformity}}{a numeric vector} }} \references{Montgomery, D.C.(2017, 10th ed.) \emph{Design and Analysis of Experiments}, Wiley, New York.} \examples{data(Problem2.33)} \keyword{{datasets}}

cf9c260afef9aa5ee359dea14bd8fde0d43ddb4c

9aafde089eb3d8bba05aec912e61fbd9fb84bd49

/codeml_files/newick_trees_processed_and_cleaned/10304_2/rinput.R

416b7b832a32829cf8369914c1225bd00dbfdca9

[]

no_license

DaniBoo/cyanobacteria_project

6a816bb0ccf285842b61bfd3612c176f5877a1fb

be08ff723284b0c38f9c758d3e250c664bbfbf3b

refs/heads/master

2021-01-25T05:28:00.686474

2013-03-23T15:09:39

null

0

null

UTF-8

R

false

137

r

rinput.R

library(ape) testtree <- read.tree("10304_2.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="10304_2_unrooted.txt")

449c26ee542d0ee91b917b3679fb1feb3e841e3b

7d125cf7b30e9be0ef1f02e24ad13495b4481f4e

/src/Library/filterGeneExpSamples.R

03f628624b8199ed3cc3ec42fccf7c6af481f605

[]

no_license

DToxS/Differential-Comparison

0616004e275cfa17d605505cecc6842a0baa4b2a

d6b3d4cc7c4ef2bdb21527655fb927c146453942

refs/heads/master

2022-04-06T22:12:37.767298

2020-02-27T22:19:56

105,199,221

0

null

UTF-8

R

false

11,661

r

filterGeneExpSamples.R

# Filter outlier samples by removing outlier samples. filterGeneExpSamples <- function(exprt_design_merged, read_counts_merged, drug_names, dist_cutoffs, subset_field_name=NULL, dist_cutoff_outlier=0.01, dist_cutoff_group=0.015, min_samples=3, filter_outlier=TRUE, keep_under_samples=FALSE, plot_orig_clust=FALSE, plot_filter_clust=FALSE, plot_pass_clust=FALSE, plot_empty_clust=FALSE, color_sample_groups=TRUE, group_colors=NULL, color_field_pos=3, color_field_type="numeric", hline_color="deeppink", hline_type=4, hline_width=1, clust_title_elems_names=c(State="State",Culture="Culture",Measurement="Measurement",Subject="Subject"), clust_title_elems_flags=c(State=TRUE,Culture=TRUE,Measurement=TRUE,Subject=TRUE), clust_title_elems_keys=c("State", "Culture", "Measurement", "Subject"), title_cex=1.75, branch_line_width=2, leaf_lab_cex=0.75, ylab_cex=1.5, leg_title="Culture", leg_pos="topright", leg_cex=1, leg_title_col="black", leg_title_adj=1, verbose=FALSE, func_dir=NULL) { # Load required library require("matrixStats") # Load user-defined functions. if(is.null(func_dir)) func_dir <- getwd() source(file.path(func_dir, "getCutoff.R"), local=TRUE) source(file.path(func_dir, "filterGeneExpSamplesSubset.R"), local=TRUE) # Check input arguments. # Check subset_field_name. if(!is.null(subset_field_name) && !all(subset_field_name %in% colnames(exprt_design_merged))) { warning("subset_field_name must be one of the column names of experiment design table!") return(NULL) } # The title text for sample cluster plots is composed of: # # - the number of samples # - the name of treatment state # - the name of cell culture experiment # - the name of measurement plate # - the name of cell subject # # in the form of: # # [#] [State] in [Culture] [Measurement] [Subject] # # e.g. 4 TRS in Exp 15 Assay 6 iPSC B if(!is.null(clust_title_elems_names) && !is.null(clust_title_elems_flags)) { if(!(is.character(clust_title_elems_names) && is.vector(clust_title_elems_names) && length(clust_title_elems_names)==length(clust_title_elems_keys))) { warning(paste0("clust_title_elems_names must be a vector of character strings with a length of ", length(clust_title_elems_keys), "!")) return(exprt_design_merged) } if(names(clust_title_elems_names) != clust_title_elems_keys) { warning(paste0("The names of clust_title_elems_names must be ", paste0(clust_title_elems_keys,collapse=", "), "!")) return(exprt_design_merged) } if(!(is.logical(clust_title_elems_flags) && is.vector(clust_title_elems_flags) && length(clust_title_elems_flags)==length(clust_title_elems_keys))) { warning(paste0("clust_title_elems_flags must be a vector of logical values with a length of ", length(clust_title_elems_keys), "!")) return(exprt_design_merged) } if(names(clust_title_elems_flags) != clust_title_elems_keys) { warning(paste0("The names of clust_title_elems_flags must be ", paste0(clust_title_elems_keys,collapse=", "), "!")) return(exprt_design_merged) } } else { warning("Neither of clust_title_elems_names and clust_title_elems_flags can be NULL!") return(exprt_design_merged) } # Retrieve subset names from experiment design table if a subset # category is specified. # Note: the name of a subset category must be one of the column # names of experiment design table. if(!is.null(subset_field_name)) subset_names <- exprt_design_merged[,subset_field_name] else subset_names <- NULL # Sort and filter sample replicates in each drug-treated condition for # all drug groups and cell lines. exprt_design_merged_sorted <- NULL for(drug_name in drug_names) { sample_drug_flags <- exprt_design_merged$State==drug_name exprt_design_merged_drug <- exprt_design_merged[sample_drug_flags,,drop=FALSE] if(!is.null(subset_names)) subset_names_drug <- subset_names[sample_drug_flags] else subset_names_drug <- NULL for(cell_line in sort(unique(exprt_design_merged$Cell))) { sample_drug_cell_flags <- exprt_design_merged_drug$Cell==cell_line exprt_design_merged_drug_cell <- exprt_design_merged_drug[sample_drug_cell_flags,,drop=FALSE] if(!is.null(subset_names_drug)) subset_names_drug_cell <- subset_names_drug[sample_drug_cell_flags] else subset_names_drug_cell <- NULL for(plate in sort(unique(exprt_design_merged_drug_cell$Plate))) { # Prepar the dataset of current condition. sample_drug_cell_plate_flags <- exprt_design_merged_drug_cell$Plate==plate exprt_design_merged_drug_cell_plate <- exprt_design_merged_drug_cell[sample_drug_cell_plate_flags,,drop=FALSE] sample_names_drug_cell_plate <- exprt_design_merged_drug_cell_plate$ID read_counts_drug_cell_plate <- read_counts_merged[,sample_names_drug_cell_plate,drop=FALSE] if(is.matrix(read_counts_drug_cell_plate)) read_counts_drug_cell_plate <- read_counts_drug_cell_plate[!rowAnys(is.na(read_counts_drug_cell_plate)),,drop=FALSE] else read_counts_drug_cell_plate <- read_counts_drug_cell_plate[!is.na(read_counts_drug_cell_plate)] if(!is.null(subset_names_drug_cell)) subset_names_drug_cell_plate <- subset_names_drug_cell[sample_drug_cell_plate_flags] else subset_names_drug_cell_plate <- NULL # Calculate the cutoff line for outlier samples. # Set cutoff values for outlier samples. cutoff <- getCutoff(state=drug_name, cell=cell_line, plate=plate, cutoffs=dist_cutoffs, single=dist_cutoff_outlier, group=dist_cutoff_group) if(is.matrix(read_counts_drug_cell_plate) && ncol(read_counts_drug_cell_plate)>min_samples) hline <- cutoff[1] else hline <- cutoff[2] # Filter outlier samples for each subset of samples at current drug, cell and plate. if(!is.null(subset_names_drug_cell_plate)) { exprt_design_merged_drug_cell_plate_filtered <- NULL for(subset_name in sort(unique(subset_names_drug_cell_plate))) { # Prepar the dataset of current condition. sample_drug_cell_plate_subset_flags <- subset_names_drug_cell_plate==subset_name exprt_design_merged_drug_cell_plate_subset <- exprt_design_merged_drug_cell_plate[sample_drug_cell_plate_subset_flags,,drop=FALSE] sample_names_drug_cell_plate_subset <- exprt_design_merged_drug_cell_plate_subset$ID read_counts_drug_cell_plate_subset <- read_counts_drug_cell_plate[,sample_names_drug_cell_plate_subset,drop=FALSE] # Set title text for sample cluster plots. clust_title_elems_values <- c(State=drug_name, Culture=subset_name, Measurement=plate, Subject=cell_line) clust_title_elems <- trimws(paste(clust_title_elems_names, clust_title_elems_values)) names(clust_title_elems) <- names(clust_title_elems_names) clust_title_elems <- clust_title_elems[clust_title_elems_flags] if("State"%in%names(clust_title_elems)) clust_title <- paste(clust_title_elems["State"], "in", paste0(clust_title_elems[names(clust_title_elems)!="State"],collapse=" ")) else clust_title <- paste0(clust_title_elems[names(clust_title_elems)!="State"],collapse=" ") # Filter outlier samples for current subset of samples. exprt_design_merged_drug_cell_plate_subset_filtered <- filterGeneExpSamplesSubset(subset_name=subset_name, exprt_design=exprt_design_merged_drug_cell_plate_subset, read_counts=read_counts_drug_cell_plate_subset, cutoff=cutoff, hline=hline, dist_cutoff_outlier=dist_cutoff_outlier, dist_cutoff_group=dist_cutoff_group, min_samples=min_samples, filter_outlier=filter_outlier, keep_under_samples=keep_under_samples, plot_orig_clust=plot_orig_clust, plot_filter_clust=plot_filter_clust, plot_pass_clust=plot_pass_clust, plot_empty_clust=plot_empty_clust, color_sample_groups=color_sample_groups, group_colors=group_colors, color_field_pos=color_field_pos, color_field_type=color_field_type, hline_color=hline_color, hline_type=hline_type, hline_width=hline_width, clust_title=clust_title, title_cex=title_cex, branch_line_width=branch_line_width, leaf_lab_cex=leaf_lab_cex, ylab_cex=ylab_cex, leg_title=leg_title, leg_pos=leg_pos, leg_cex=leg_cex, leg_title_col=leg_title_col, leg_title_adj=leg_title_adj, verbose=verbose, func_dir=func_dir) # Save filtered samples for current subset of samples. if(nrow(exprt_design_merged_drug_cell_plate_subset_filtered)>0) exprt_design_merged_drug_cell_plate_filtered <- rbind(exprt_design_merged_drug_cell_plate_filtered, exprt_design_merged_drug_cell_plate_subset_filtered) } } else { # Set title text for sample cluster plots. clust_title_elems_values <- c(State=drug_name, Culture="", Measurement=plate, Subject=cell_line) clust_title_elems <- trimws(paste(clust_title_elems_names, clust_title_elems_values)) names(clust_title_elems) <- names(clust_title_elems_names) clust_title_elems <- clust_title_elems[clust_title_elems_flags] if("State"%in%names(clust_title_elems)) clust_title <- paste(clust_title_elems["State"], "in", paste0(clust_title_elems[names(clust_title_elems)!="State"],collapse=" ")) else clust_title <- paste0(clust_title_elems[names(clust_title_elems)!="State"],collapse=" ") # Filter outlier samples for all the samples at current drug, cell and plate. exprt_design_merged_drug_cell_plate_filtered <- filterGeneExpSamplesSubset(exprt_design=exprt_design_merged_drug_cell_plate, read_counts=read_counts_drug_cell_plate, cutoff=cutoff, hline=hline, dist_cutoff_outlier=dist_cutoff_outlier, dist_cutoff_group=dist_cutoff_group, min_samples=min_samples, filter_outlier=filter_outlier, keep_under_samples=keep_under_samples, plot_orig_clust=plot_orig_clust, plot_filter_clust=plot_filter_clust, plot_pass_clust=plot_pass_clust, plot_empty_clust=plot_empty_clust, color_sample_groups=color_sample_groups, group_colors=group_colors, color_field_pos=color_field_pos, color_field_type=color_field_type, hline_color=hline_color, hline_type=hline_type, hline_width=hline_width, clust_title=clust_title, title_cex=title_cex, branch_line_width=branch_line_width, leaf_lab_cex=leaf_lab_cex, ylab_cex=ylab_cex, leg_title=leg_title, leg_pos=leg_pos, leg_cex=leg_cex, leg_title_col=leg_title_col, leg_title_adj=leg_title_adj, verbose=verbose, func_dir=func_dir) } # Save filtered samples at current drug, cell and plate. if(nrow(exprt_design_merged_drug_cell_plate_filtered)>0) exprt_design_merged_sorted <- rbind(exprt_design_merged_sorted, exprt_design_merged_drug_cell_plate_filtered) } } } # Return filtered experiment design table. return(exprt_design_merged_sorted) }

05d6e4fc665c68b0fce9a9d40098b9ca605600ec

202684be012c3153a9791a6430a8f7eae997a036

/data_handling.R

d6b46d9ac821c9c5c415d3f483245d82e9ee9965

[]

no_license

fbaffie/NVE_API_readR

cfc2090df2a9e8e737a85278486059ff6b38790f

b8dc5b1bb25f6374bfe3ee6f1ddf23584220a4b5

refs/heads/master

2020-03-21T12:48:16.703179

2018-07-03T09:16:00

138,572,644

0

null

UTF-8

R

false

1,787

r

data_handling.R

# Construct metadata table from data list metadata_for_app <- function(data_main) { df_meta <- c() for (i in 1:length(data_main)) { df_meta <- rbind(df_meta, data_main[[i]]$metadata) } df_meta$prec_mean <- sapply(data_main, function(x) x$prec_mean) df_meta$runoff_mean <- sapply(data_main, function(x) x$runoff_mean) df_meta$runoff_eff <- sapply(data_main, function(x) x$runoff_eff) return(df_meta) } # Compute mean runoff/precipitation and runoff efficiency comp_stats <- function(data_list) { # Remove missing data df <- data.frame(prec = data_list$Prec, runoff = data_list$Runoff) df <- na.omit(df) # Annual average precipitation data_list$prec_mean <- 365*mean(df$prec, na.rm = TRUE) # Annual average runoff data_list$runoff_mean <- 365*mean(df$runoff, na.rm = TRUE) # Runoff efficiency data_list$runoff_eff <- sum(df$runoff)/sum(df$prec) return(data_list) } # Read metadata file (excel table) read_metadata_file <- function(filename) { # Read station metadata meta_data <- read_excel(filename) meta_data <- tbl_df(meta_data) # # Keep rows with runoff data (parameter == 1001) # # meta_data <- filter(meta_data, param_key==1001) # Remove duplicated stations idup <- duplicated(meta_data[, 1:3]) meta_data <- meta_data[!idup, ] # Add station name as 'regine_area.main_no' meta_data <- mutate(meta_data, regine_main = paste(regine_area, main_no, sep = ".")) # Add observation series as 'regine_area.main_no.point_no.param_key.version_no_end' meta_data <- mutate(meta_data, obs_series = paste(regine_area, main_no, point_no, param_key, version_no_end, sep = ".")) return(meta_data) }

687ed98d7f3390038dfb73757cfc1bbdc83cdb95

20a9435ef4586a43a4e55502d0f0ac40aa185821

/tests/testthat/test_binary_single.R

2d140af5d1cce490b5ea74adea763cd3b70e4dbe

[]

no_license

cran/hmi

a9df9353e459bfe45d9952370a962fa879c8f5a1

6d1edb0d025c182cedb325fa9826f4ba00e988d1

refs/heads/master

2021-01-23T06:20:51.264453

2020-10-01T22:20:02

86,358,162

0

null

UTF-8

R

false

672

r

test_binary_single.R

context("binary_single") library(testthat) library(hmi) library(mice) set.seed(123) y_imp <- sample(c(0, 1, NA), size = 150, replace = TRUE) y_imp2 <- sample(c("A", "B", NA), size = 150, replace = TRUE) X_imp <- cbind(1, iris[, 1:4]) #test_check("hmi") test_that("binary_single returns plausible values", { expect_equal(unique(imp_binary_single(y_imp = y_imp, X_imp = X_imp, pvalue = 1)$y_ret), c(0, 1)) expect_equal(sort(as.character(unique(imp_binary_single(y_imp = y_imp2, X_imp = X_imp, pvalue = 1)$y_ret))), c("A", "B")) })

5a306bb094f0a9483b86faae18f0446d2356e1f5

184180d341d2928ab7c5a626d94f2a9863726c65

/issuestests/SpatialEpi/man/binomialLogLkhd.Rd

51099b4faaff8ebf088a74d98fc2d920ff6c8782

[]

no_license

akhikolla/RcppDeepStateTest

f102ddf03a22b0fc05e02239d53405c8977cbc2b

97e73fe4f8cb0f8e5415f52a2474c8bc322bbbe5

refs/heads/master

2023-03-03T12:19:31.725234

2021-02-12T21:50:12

254,214,504

2

1

null

UTF-8

R

false

687

rd

binomialLogLkhd.Rd

\name{binomialLogLkhd} \alias{binomialLogLkhd} \title{Compute Binomial Likelihoods} \description{Compute binomial likelihood ratio test statistic for Kulldorff method} \usage{binomialLogLkhd(cz, nz, N, C)} \arguments{ \item{cz}{count inside zone} \item{nz}{expected count inside zone} \item{N}{total expected count in region} \item{C}{total number of cases in region} } \value{Binomial likelihood ratio test statistic} \references{ Kulldorff M. and Nagarwalla N. (1995) Spatial disease clusters: Detection and Inference.\emph{Statistics in Medicine}, \bold{14}, 799--810.} \author{Albert Y. Kim} \seealso{\code{\link{poissonLogLkhd}}, \code{\link{kulldorff}}} \keyword{internal}

5678eedf2d3ee288e53c1b24919ae308952191b1

29585dff702209dd446c0ab52ceea046c58e384e

/s2dverification/R/ConfigApplyMatchingEntries.R

ae976662923794df22ef1ffcbf9d0b14d4fe2349

[]

no_license

ingted/R-Examples

825440ce468ce608c4d73e2af4c0a0213b81c0fe

d0917dbaf698cb8bc0789db0c3ab07453016eab9

refs/heads/master

2020-04-14T12:29:22.336088

2016-07-21T14:01:14

null

0

null

UTF-8

R

false

5,647

r

ConfigApplyMatchingEntries.R

ConfigApplyMatchingEntries <- function(configuration, var, exp = NULL, obs = NULL, show_entries = FALSE, show_result = TRUE) { ## Function to tell if a regexpr() match is a complete match to a specified name isFullMatch <- function(x, name) { ifelse(x > 0 && attributes(x)$match.length == nchar(name), TRUE, FALSE) } var_entries_in_exps <- c() if (length(unlist(configuration$experiments, recursive = FALSE)) > 0) { var_entries_in_exps <- which(unlist(lapply(lapply(as.list(unlist(lapply(configuration$experiments, lapply, "[[", 2))), regexpr, var), isFullMatch, var) > 0)) } var_entries_in_obs <- c() if (length(unlist(configuration$observations, recursive = FALSE)) > 0) { var_entries_in_obs <- which(unlist(lapply(lapply(as.list(unlist(lapply(configuration$observations, lapply, "[[", 2))), regexpr, var), isFullMatch, var) > 0)) } exp_info <- list() jmod <- 1 for (mod in exp) { mod_var_matching_entries <- mod_var_matching_indices <- mod_var_matching_entries_levels <- c() if (length(unlist(configuration$experiments, recursive = FALSE)) > 0) { mod_entries_in_exps <- which(unlist(lapply(lapply(unlist(lapply(configuration$experiments, lapply, "[[", 1), recursive = FALSE), regexpr, mod), isFullMatch, mod))) if (length(mod_entries_in_exps) > 0) { mod_var_matching_indices <- intersect(var_entries_in_exps, mod_entries_in_exps) mod_var_matching_entries <- unlist(configuration$experiments, recursive = FALSE)[mod_var_matching_indices] exps_levels <- lapply(as.list(1:4), f <- function(x) {x <- array(x, length(configuration$experiments[[x]]))}) mod_var_matching_entries_levels <- unlist(exps_levels)[intersect(var_entries_in_exps, mod_entries_in_exps)] } } if (length(mod_var_matching_entries) == 0) { stop(paste('Error: There are no matching entries in the configuration file for the experiment', mod, 'and the variable', var, '. Please check the configuration file.)')) } else { if (show_entries) { header <- paste0("# Matching entries for experiment '", exp[jmod], "' and variable '", var, "' #\n") cat(paste0(paste(rep("#", nchar(header) - 1), collapse = ''), "\n")) cat(header) cat(paste0(paste(rep("#", nchar(header) - 1), collapse = ''), "\n")) ConfigShowTable(list(experiments = list(mod_var_matching_entries)), 'experiments', mod_var_matching_indices) cat("\n") } result <- .ConfigGetDatasetInfo(mod_var_matching_entries, 'experiments') if (show_result) { cat(paste0("The result of applying the matching entries to experiment name '", exp[jmod], "' and variable name '", var, "' is:\n")) configuration$definitions[["VAR_NAME"]] <- var configuration$definitions[["EXP_NAME"]] <- exp[jmod] fields <- c("MAIN_PATH: ", "FILE_PATH: ", "NC_VAR_NAME: ", "SUFFIX: ", "VAR_MIN: ", "VAR_MAX: ") values <- lapply(result, lapply, function (x) .ConfigReplaceVariablesInString(x, configuration$definitions, TRUE)) lapply(paste0(fields, unlist(values), "\n"), cat) cat("\n") } exp_info <- c(exp_info, list(result)) } jmod <- jmod + 1 } obs_info <- list() jobs <- 1 for (ref in obs) { ref_var_matching_entries <- ref_var_matching_indices <- ref_var_matching_entries_levels <- c() if (length(unlist(configuration$observations, recursive = FALSE)) > 0) { ref_entries_in_obs <- which(unlist(lapply(lapply(unlist(lapply(configuration$observations, lapply, "[[", 1), recursive = FALSE), regexpr, ref), isFullMatch, ref))) if (length(ref_entries_in_obs) > 0) { ref_var_matching_indices <- intersect(var_entries_in_obs, ref_entries_in_obs) ref_var_matching_entries <- unlist(configuration$observations, recursive = FALSE)[ref_var_matching_indices] obs_levels <- lapply(as.list(1:4), f <- function(x) {x <- array(x, length(configuration$observations[[x]]))}) ref_var_matching_entries_levels <- unlist(obs_levels)[intersect(var_entries_in_obs, ref_entries_in_obs)] } } if (length(ref_var_matching_entries) == 0) { stop(paste('Error: There are no matching entries in the configuration file for the observation', ref, 'and the variable', var, '. Please check the configuration file.)')) } else { if (show_entries) { header <- paste0("# Matching entries for observation '", obs[jobs], "' and variable '", var, "' #\n") cat(paste0(paste(rep("#", nchar(header) - 1), collapse = ''), "\n")) cat(header) cat(paste0(paste(rep("#", nchar(header) - 1), collapse = ''), "\n")) ConfigShowTable(list(observations = list(ref_var_matching_entries)), 'observations', ref_var_matching_indices) cat("\n") } result <- .ConfigGetDatasetInfo(ref_var_matching_entries, 'observations') if (show_result) { cat(paste0("The result of applying the matching entries to observation name '", obs[jobs], "' and variable name '", var, "' is:\n")) configuration$definitions[['VAR_NAME']] <- var configuration$definitions[["OBS_NAME"]] <- obs[jobs] fields <- c("MAIN_PATH: ", "FILE_PATH: ", "NC_VAR_NAME: ", "SUFFIX: ", "VAR_MIN: ", "VAR_MAX: ") values <- lapply(result, lapply, function (x) .ConfigReplaceVariablesInString(x, configuration$definitions, TRUE)) lapply(paste0(fields, unlist(values), "\n"), cat) cat("\n") } obs_info <- c(obs_info, list(result)) } jobs <- jobs + 1 } invisible(list(exp_info = exp_info, obs_info = obs_info)) }

a19f31d7c42618633cf4da9c30927048770be22a

efa60dd053fbeb2c176315ee269eae9f2ecb1a58

/enseignements/rcode/xgboost.R

014767664f1bb3ab7ae70b688cd0d9cd235766ef

[ "MIT" ]

permissive

masedki/masedki.github.io

b5e5f5ac2fc9d2241b1b022560fb1b63fe067f26

ee79edc83016714151ebf2cddcf951e1da214f2e

refs/heads/master

2023-07-10T05:19:27.703327

2023-07-07T07:35:32

89,248,820

0

null

UTF-8

R

false

2,698

r

xgboost.R

setwd("~/Dropbox/enseignement/M1/supports/Rscripts") rm(list=ls()) load("insurance.rda") require(rpart) require(rpart.plot) require(caret) require(doParallel) require(xgboost) # lecture du jeu de données summary(insurance) set.seed(11) train = sample(1:nrow(insurance), round(0.75*nrow(insurance))) insurance.tr = insurance[train,] insurance.te = insurance[-train,] ## CART sans élagage cart.0 <- rpart(charges~., data=insurance.tr, control=rpart.control(minsplit=7,cp=0, xval=5)) rpart.plot(cart.0) pred.0 <- predict(cart.0, insurance.te) sqrt(mean((insurance.te$charges - pred.0)**2)) plotcp(cart.0) which.min(cart.0$cptable[,"xerror"]) cart.0$cptable ## CART avec élagage cart.pruned <- prune(cart.0, cp = cart.0$cptable[which.min(cart.0$cptable[,"xerror"]),"CP"]) rpart.plot(cart.pruned) pred.pruned <- predict(cart.pruned, insurance.te) sqrt(mean((insurance.te$charges - pred.pruned)**2)) ##xboost ?xgboost y.tr = insurance.tr[,7] x.tr = data.matrix(insurance.tr[,-7]) y.te = insurance.te[,7] x.te = data.matrix(insurance.te[,-7]) boosted = xgboost(data = x.tr, label = y.tr, objective = "reg:linear" , # la fonction de perte quadratique booster = "gbtree", # règle faible : arbre max.depth = 2, # nombre de feuilles de la règle faible eta = 1, # le paramètre de régularisation appelé lambda en cours nrounds = 5) # le nombre d'itération du GB ## réglage avec caret fitControl <- trainControl(method = "repeatedcv", number = 3, repeats = 2, search = "random") # boost.grid = expand.grid(eta =c(0.1, 0.2, 0.3), # nrounds = 10*(5:20), # max_depth = c(2, 3, 4, 5), # subsample = 1, # min_child_weight = 1., # colsample_bytree = 1, # gamma = 0.) #cl <- makePSOCKcluster(7) #registerDoParallel(cl) boosted.cv <- train(x.tr, y.tr, method = "xgbTree", trControl = fitControl) #tuneGrid = boost.grid) #stopCluster(cl) plot(boosted.cv) boosted.cv$bestTune pred.boost = predict(boosted.cv, x.te) sqrt(mean((y.te - pred.boost)^2)) ## Paramètres optimaux d'après Etienne boost.opt = xgboost(data = x.tr, label = y.tr, objective = "reg:linear", booster = "gbtree", max.depth = 9, eta = 0.1111882, nrounds = 913) pred.boost = predict(boost.opt,x.te) sqrt(mean((y.te - pred.boost)^2))

a20424ef3e73f44cb485f17581ff47eb2cf31404

9e1d5eaa04362bd5c2669b62cef8da6e5f99586f

/project-prototype/dataprocessing.R

2ca2c51f1091668b01c8d2a038ab76be4cf57989

[]

no_license

deekshachugh/msan622

6e4f672bad23d28ebc5716e9ad59fead9a36aea4

49ba336831cd3d75845c0504b39bc2d67eaf307e

refs/heads/master

2021-01-21T03:50:37.782150

2014-05-16T06:03:58

null

0

null

UTF-8

R

false

1,064

r

dataprocessing.R

data <- read.csv("/home/deeksha/github/msan622/project-dataset/COmpleteweatherdata.csv") head(data) colnames(data) <- c("Date", "Temperature", "Dew Point Temperature","Precipitation","Humidity","Wind Speed","Percent Cloud Cover", "City") latlongdata <- read.csv("/home/deeksha/Desktop/airports/airports_fsx_icao_lat_lon_alt_feet.txt",header=F) head(latlongdata) colnames(latlongdata) <- c("City","Latitude","Longitude","x") library(plyr) joineddata1 <- join(data, latlongdata[,1:3], by = "City") head(joineddata1) mapping <- read.csv("/media/deeksha/e/Deeksha/Dropbox/Coursework/PracticumIII/Data/MappingCityCOde.csv",header =F) head(mapping) names(mapping)<-c("CityCode","City") library(plyr) joineddata <- join(joineddata1, mapping[,1:2], by = "City") head(joineddata) #joineddata <- joineddata[,c(1:7,9:11)] ncol(joineddata) colnames(joineddata)[8] <- "CityCode" colnames(joineddata)[11] <- "City" joineddata <- joineddata[!is.na(joineddata$City),] write.csv(joineddata,"/home/deeksha/github/msan622/project-prototype/weatherdata.csv") summary(joineddata)

155f75e448a195e0aef9e2220fb93a52bef4d63a

01b8fa708e8e0318871d0ee7b4155ae35d64dd9a

/R/rd_sens_cutoff.R

25ab22e4d0ef98b3f8f6df95ef06ced38e9123be

[]

no_license

felixthoemmes/rddapp

b313f32dd89248de26b173be077ac28ea90bf022

f81091ab1978c1ee0a50f7608a4a42ae56f6e4b1

refs/heads/main

2023-04-12T23:46:30.030859

2023-04-07T01:49:56

119,074,922

10

4

null

2022-01-31T14:50:54

2018-01-26T16:24:45

HTML

UTF-8

R

false

2,423

r

rd_sens_cutoff.R

#' Cutoff Sensitivity Simulation for Regression Discontinuity #' #' \code{rd_sens_cutoff} refits the supplied model with varying cutoff(s). #' All other aspects of the model, such as the automatically calculated bandwidth, are held constant. #' #' @param object An object returned by \code{rd_est} or \code{rd_impute}. #' @param cutoffs A numeric vector of cutoff values to be used for refitting #' an \code{rd} object. #' #' @return \code{rd_sens_cutoff} returns a dataframe containing the estimate \code{est} and standard error \code{se} #' for each cutoff value (\code{A1}). Column \code{A1} contains varying cutoffs #' on the assignment variable. The \code{model} column contains the parametric model (linear, quadratic, or cubic) or #' non-parametric bandwidth setting (Imbens-Kalyanaraman 2012 optimal, half, or double) used for estimation. #' #' @references Imbens, G., Kalyanaraman, K. (2012). #' Optimal bandwidth choice for the regression discontinuity estimator. #' The Review of Economic Studies, 79(3), 933-959. #' \url{https://academic.oup.com/restud/article/79/3/933/1533189}. #' #' @export #' #' @examples #' set.seed(12345) #' x <- runif(1000, -1, 1) #' cov <- rnorm(1000) #' y <- 3 + 2 * x + 3 * cov + 10 * (x >= 0) + rnorm(1000) #' rd <- rd_est(y ~ x | cov, t.design = "geq") #' rd_sens_cutoff(rd, seq(-.5, .5, length.out = 10)) rd_sens_cutoff <- function(object, cutoffs) { if (!inherits(object, "rd")) stop("Not an object of class rd.") sim_results <- lapply(cutoffs, function(cutoff) { object$call$cutpoint <- cutoff object$call$est.cov <- FALSE object$call$bw <- object$bw["Opt"] new_model <- eval.parent(object$call, 3) return( data.frame( est = new_model$est, se = new_model$se, A1 = cutoff, model = c("linear", "quadratic", "cubic", "optimal", "half", "double"), stringsAsFactors = FALSE, row.names = 1:6) ) } ) combined_sim_results <- do.call(rbind.data.frame, sim_results) original_results <- data.frame( est = object$est, se = object$se, A1 = if (is.null(object$call$cutpoint)) 0 else eval.parent(object$call$cutpoint), model = c("linear", "quadratic", "cubic", "optimal", "half", "double"), stringsAsFactors = FALSE, row.names = 1:6) return(rbind(combined_sim_results, original_results)) }

bb8ba4055b3d45e6a187e43d5e93cdef6be15354

a56e7a0ce097b8da6ae95f750b5bf1a6bbb251c5

/r/tests/testthat/test_trading_api.R

128205dcff8814edb09fb75b8f28bfd1e4bab268

[]

no_license

harshabakku/deribit_options

d08f7a61386f6a047ec0c4726d882fead8fbb7a7

4f344f2fbf0b761cc5378d852a38d33849223b53

refs/heads/master

2022-12-08T20:58:47.873520

2020-09-01T16:38:04

292,051,793

0

1

null

UTF-8

R

false

18,327

r

test_trading_api.R

# Automatically generated by openapi-generator (https://openapi-generator.tech) # Please update as you see appropriate context("Test TradingApi") api.instance <- TradingApi$new() test_that("PrivateBuyGet", { # tests for PrivateBuyGet # base path: https://www.deribit.com/api/v2 # Places a buy order for an instrument. # @param character instrument.name Instrument name # @param numeric amount It represents the requested order size. For perpetual and futures the amount is in USD units, for options it is amount of corresponding cryptocurrency contracts, e.g., BTC or ETH # @param character type The order type, default: `\"limit\"` (optional) # @param character label user defined label for the order (maximum 32 characters) (optional) # @param numeric price The order price in base currency (Only for limit and stop_limit orders) When adding order with advanced=usd, the field price should be the option price value in USD. When adding order with advanced=implv, the field price should be a value of implied volatility in percentages. For example, price=100, means implied volatility of 100% (optional) # @param character time.in.force Specifies how long the order remains in effect. Default `\"good_til_cancelled\"` <ul> <li>`\"good_til_cancelled\"` - unfilled order remains in order book until cancelled</li> <li>`\"fill_or_kill\"` - execute a transaction immediately and completely or not at all</li> <li>`\"immediate_or_cancel\"` - execute a transaction immediately, and any portion of the order that cannot be immediately filled is cancelled</li> </ul> (optional) # @param numeric max.show Maximum amount within an order to be shown to other customers, `0` for invisible order (optional) # @param character post.only If true, the order is considered post-only. If the new price would cause the order to be filled immediately (as taker), the price will be changed to be just below the bid. Only valid in combination with time_in_force=`\"good_til_cancelled\"` (optional) # @param character reduce.only If `true`, the order is considered reduce-only which is intended to only reduce a current position (optional) # @param numeric stop.price Stop price, required for stop limit orders (Only for stop orders) (optional) # @param character trigger Defines trigger type, required for `\"stop_limit\"` order type (optional) # @param character advanced Advanced option order type. (Only for options) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateCancelAllByCurrencyGet", { # tests for PrivateCancelAllByCurrencyGet # base path: https://www.deribit.com/api/v2 # Cancels all orders by currency, optionally filtered by instrument kind and/or order type. # @param character currency The currency symbol # @param character kind Instrument kind, if not provided instruments of all kinds are considered (optional) # @param character type Order type - limit, stop or all, default - `all` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateCancelAllByInstrumentGet", { # tests for PrivateCancelAllByInstrumentGet # base path: https://www.deribit.com/api/v2 # Cancels all orders by instrument, optionally filtered by order type. # @param character instrument.name Instrument name # @param character type Order type - limit, stop or all, default - `all` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateCancelAllGet", { # tests for PrivateCancelAllGet # base path: https://www.deribit.com/api/v2 # This method cancels all users orders and stop orders within all currencies and instrument kinds. # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateCancelGet", { # tests for PrivateCancelGet # base path: https://www.deribit.com/api/v2 # Cancel an order, specified by order id # @param character order.id The order id # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateClosePositionGet", { # tests for PrivateClosePositionGet # base path: https://www.deribit.com/api/v2 # Makes closing position reduce only order . # @param character instrument.name Instrument name # @param character type The order type # @param numeric price Optional price for limit order. (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateEditGet", { # tests for PrivateEditGet # base path: https://www.deribit.com/api/v2 # Change price, amount and/or other properties of an order. # @param character order.id The order id # @param numeric amount It represents the requested order size. For perpetual and futures the amount is in USD units, for options it is amount of corresponding cryptocurrency contracts, e.g., BTC or ETH # @param numeric price The order price in base currency. When editing an option order with advanced=usd, the field price should be the option price value in USD. When editing an option order with advanced=implv, the field price should be a value of implied volatility in percentages. For example, price=100, means implied volatility of 100% # @param character post.only If true, the order is considered post-only. If the new price would cause the order to be filled immediately (as taker), the price will be changed to be just below the bid. Only valid in combination with time_in_force=`\"good_til_cancelled\"` (optional) # @param character advanced Advanced option order type. If you have posted an advanced option order, it is necessary to re-supply this parameter when editing it (Only for options) (optional) # @param numeric stop.price Stop price, required for stop limit orders (Only for stop orders) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetMarginsGet", { # tests for PrivateGetMarginsGet # base path: https://www.deribit.com/api/v2 # Get margins for given instrument, amount and price. # @param character instrument.name Instrument name # @param numeric amount Amount, integer for future, float for option. For perpetual and futures the amount is in USD units, for options it is amount of corresponding cryptocurrency contracts, e.g., BTC or ETH. # @param numeric price Price # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetOpenOrdersByCurrencyGet", { # tests for PrivateGetOpenOrdersByCurrencyGet # base path: https://www.deribit.com/api/v2 # Retrieves list of user's open orders. # @param character currency The currency symbol # @param character kind Instrument kind, if not provided instruments of all kinds are considered (optional) # @param character type Order type, default - `all` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetOpenOrdersByInstrumentGet", { # tests for PrivateGetOpenOrdersByInstrumentGet # base path: https://www.deribit.com/api/v2 # Retrieves list of user's open orders within given Instrument. # @param character instrument.name Instrument name # @param character type Order type, default - `all` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetOrderHistoryByCurrencyGet", { # tests for PrivateGetOrderHistoryByCurrencyGet # base path: https://www.deribit.com/api/v2 # Retrieves history of orders that have been partially or fully filled. # @param character currency The currency symbol # @param character kind Instrument kind, if not provided instruments of all kinds are considered (optional) # @param integer count Number of requested items, default - `20` (optional) # @param integer offset The offset for pagination, default - `0` (optional) # @param character include.old Include in result orders older than 2 days, default - `false` (optional) # @param character include.unfilled Include in result fully unfilled closed orders, default - `false` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetOrderHistoryByInstrumentGet", { # tests for PrivateGetOrderHistoryByInstrumentGet # base path: https://www.deribit.com/api/v2 # Retrieves history of orders that have been partially or fully filled. # @param character instrument.name Instrument name # @param integer count Number of requested items, default - `20` (optional) # @param integer offset The offset for pagination, default - `0` (optional) # @param character include.old Include in result orders older than 2 days, default - `false` (optional) # @param character include.unfilled Include in result fully unfilled closed orders, default - `false` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetOrderMarginByIdsGet", { # tests for PrivateGetOrderMarginByIdsGet # base path: https://www.deribit.com/api/v2 # Retrieves initial margins of given orders # @param character ids Ids of orders # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetOrderStateGet", { # tests for PrivateGetOrderStateGet # base path: https://www.deribit.com/api/v2 # Retrieve the current state of an order. # @param character order.id The order id # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetSettlementHistoryByCurrencyGet", { # tests for PrivateGetSettlementHistoryByCurrencyGet # base path: https://www.deribit.com/api/v2 # Retrieves settlement, delivery and bankruptcy events that have affected your account. # @param character currency The currency symbol # @param character type Settlement type (optional) # @param integer count Number of requested items, default - `20` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetSettlementHistoryByInstrumentGet", { # tests for PrivateGetSettlementHistoryByInstrumentGet # base path: https://www.deribit.com/api/v2 # Retrieves public settlement, delivery and bankruptcy events filtered by instrument name # @param character instrument.name Instrument name # @param character type Settlement type (optional) # @param integer count Number of requested items, default - `20` (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetUserTradesByCurrencyAndTimeGet", { # tests for PrivateGetUserTradesByCurrencyAndTimeGet # base path: https://www.deribit.com/api/v2 # Retrieve the latest user trades that have occurred for instruments in a specific currency symbol and within given time range. # @param character currency The currency symbol # @param integer start.timestamp The earliest timestamp to return result for # @param integer end.timestamp The most recent timestamp to return result for # @param character kind Instrument kind, if not provided instruments of all kinds are considered (optional) # @param integer count Number of requested items, default - `10` (optional) # @param character include.old Include trades older than 7 days, default - `false` (optional) # @param character sorting Direction of results sorting (`default` value means no sorting, results will be returned in order in which they left the database) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetUserTradesByCurrencyGet", { # tests for PrivateGetUserTradesByCurrencyGet # base path: https://www.deribit.com/api/v2 # Retrieve the latest user trades that have occurred for instruments in a specific currency symbol. # @param character currency The currency symbol # @param character kind Instrument kind, if not provided instruments of all kinds are considered (optional) # @param character start.id The ID number of the first trade to be returned (optional) # @param character end.id The ID number of the last trade to be returned (optional) # @param integer count Number of requested items, default - `10` (optional) # @param character include.old Include trades older than 7 days, default - `false` (optional) # @param character sorting Direction of results sorting (`default` value means no sorting, results will be returned in order in which they left the database) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetUserTradesByInstrumentAndTimeGet", { # tests for PrivateGetUserTradesByInstrumentAndTimeGet # base path: https://www.deribit.com/api/v2 # Retrieve the latest user trades that have occurred for a specific instrument and within given time range. # @param character instrument.name Instrument name # @param integer start.timestamp The earliest timestamp to return result for # @param integer end.timestamp The most recent timestamp to return result for # @param integer count Number of requested items, default - `10` (optional) # @param character include.old Include trades older than 7 days, default - `false` (optional) # @param character sorting Direction of results sorting (`default` value means no sorting, results will be returned in order in which they left the database) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetUserTradesByInstrumentGet", { # tests for PrivateGetUserTradesByInstrumentGet # base path: https://www.deribit.com/api/v2 # Retrieve the latest user trades that have occurred for a specific instrument. # @param character instrument.name Instrument name # @param integer start.seq The sequence number of the first trade to be returned (optional) # @param integer end.seq The sequence number of the last trade to be returned (optional) # @param integer count Number of requested items, default - `10` (optional) # @param character include.old Include trades older than 7 days, default - `false` (optional) # @param character sorting Direction of results sorting (`default` value means no sorting, results will be returned in order in which they left the database) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateGetUserTradesByOrderGet", { # tests for PrivateGetUserTradesByOrderGet # base path: https://www.deribit.com/api/v2 # Retrieve the list of user trades that was created for given order # @param character order.id The order id # @param character sorting Direction of results sorting (`default` value means no sorting, results will be returned in order in which they left the database) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") }) test_that("PrivateSellGet", { # tests for PrivateSellGet # base path: https://www.deribit.com/api/v2 # Places a sell order for an instrument. # @param character instrument.name Instrument name # @param numeric amount It represents the requested order size. For perpetual and futures the amount is in USD units, for options it is amount of corresponding cryptocurrency contracts, e.g., BTC or ETH # @param character type The order type, default: `\"limit\"` (optional) # @param character label user defined label for the order (maximum 32 characters) (optional) # @param numeric price The order price in base currency (Only for limit and stop_limit orders) When adding order with advanced=usd, the field price should be the option price value in USD. When adding order with advanced=implv, the field price should be a value of implied volatility in percentages. For example, price=100, means implied volatility of 100% (optional) # @param character time.in.force Specifies how long the order remains in effect. Default `\"good_til_cancelled\"` <ul> <li>`\"good_til_cancelled\"` - unfilled order remains in order book until cancelled</li> <li>`\"fill_or_kill\"` - execute a transaction immediately and completely or not at all</li> <li>`\"immediate_or_cancel\"` - execute a transaction immediately, and any portion of the order that cannot be immediately filled is cancelled</li> </ul> (optional) # @param numeric max.show Maximum amount within an order to be shown to other customers, `0` for invisible order (optional) # @param character post.only If true, the order is considered post-only. If the new price would cause the order to be filled immediately (as taker), the price will be changed to be just below the bid. Only valid in combination with time_in_force=`\"good_til_cancelled\"` (optional) # @param character reduce.only If `true`, the order is considered reduce-only which is intended to only reduce a current position (optional) # @param numeric stop.price Stop price, required for stop limit orders (Only for stop orders) (optional) # @param character trigger Defines trigger type, required for `\"stop_limit\"` order type (optional) # @param character advanced Advanced option order type. (Only for options) (optional) # @return [object] # uncomment below to test the operation #expect_equal(result, "EXPECTED_RESULT") })

7eb174d9f8f5ddd70fe2506da2b89f7ff030f10b

c104b6569f1bc152b2e05c8cbbd91a5d88962be2

/man-roxygen/file-plural.R

90c69e609614bb579febe2dc133c42c20fb8c12f

[ "MIT" ]

permissive

tidyverse/googledrive

46057f3ea96ae0fc47da30fd5d38d35e01a67d2a

74a69a3a1fd66e930802ce6e461538c9e15f7c86

refs/heads/main

2023-09-01T18:13:56.084465

2023-06-27T15:59:01

89,535,184

317

60

NOASSERTION

2023-06-27T06:13:13

2017-04-26T23:22:32

R

UTF-8

R

false

219

r

file-plural.R

#' @param file Something that identifies the file(s) of interest on your Google #' Drive. Can be a character vector of names/paths, a character vector of file #' ids or URLs marked with [as_id()], or a [`dribble`].