diff --git a/.Rhistory b/.Rhistory
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index 0000000..14f8cf4
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+++ b/.Rhistory
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+combineCSVs <- function(directoryname,outputFile,country){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",files.list)]
+#initiating matrix for final results
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+colnames(bigDF) = colsList
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,ep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+expanded.tempDF <- cbind(tempDF,rep(country,nrow(tempDF)),rep(day,nrow(tempDF)))
+rbind(bigMatrix,expanded.tempDF)
+}
+return(bigMatrix)
+write.csv(bigMatrix,file=outputFile)
+}
+combineCSVs("countryX/",outputFile = "test.csv",country="x" )
+combineCSVs <- function(directoryname,outputFile,country){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+colnames(bigDF) = colsList
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,ep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+expanded.tempDF <- cbind(tempDF,rep(country,nrow(tempDF)),rep(day,nrow(tempDF)))
+rbind(bigMatrix,expanded.tempDF)
+}
+return(bigMatrix)
+write.csv(bigMatrix,file=outputFile)
+}
+combineCSVs("countryX/",outputFile = "test.csv",country="x" )
+combineCSVs <- function(directoryname,outputFile,country){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+colnames(bigDF) = colsList
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+expanded.tempDF <- cbind(tempDF,rep(country,nrow(tempDF)),rep(day,nrow(tempDF)))
+rbind(bigMatrix,expanded.tempDF)
+}
+return(bigMatrix)
+write.csv(bigMatrix,file=outputFile)
+}
+combineCSVs("countryX/",outputFile = "test.csv",country="x" )
+csv.files.list <- dir("countryX/)[grep(".csv",dir("directoryname"countryX/))]
+csv.files.list <- dir("countryX/")[grep(".csv",dir("directoryname"countryX/))]
+csv.files.list <- dir("countryX/")[grep(".csv",dir("countryX/"))]
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+colnames(bigDF) = colsList
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+colnames(bigMatrix) = colsList
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste("countryX/",csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+expanded.tempDF <- cbind(tempDF,rep("x",nrow(tempDF)),rep(day,nrow(tempDF)))
+rbind(bigMatrix,expanded.tempDF)
+}
+View(tempDF)
+View(expanded.tempDF)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste("countryX/",csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep("x",n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+View(bigMatrix)
+colnames(bigMatrix) = colsList
+return(bigMatrix)
+bigMatrix
+write.csv(bigMatrix,file="testBig.csv")
+combineCSVs <- function(directoryname,outputFile,country){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+return(bigMatrix)
+write.csv(bigMatrix,file=outputFile,row.names = F)
+}
+combineCSVs(directoryname = "countryx/",outputFile = "testBig.csv",country = "X")
+combineCSVs <- function(directoryname,outputFile,country){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+return(bigMatrix)
+write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+combineCSVs <- function(directoryname,outputFile,country){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+return(bigMatrix)
+#write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+?na.omit.data.frame
+testOmit <- na.omit(bigMatrix)
+View(testOmit)
+testBase <- read.csv(file="countryX/screen_123.csv",header=T)
+testBase <- read.csv(file="countryX/screen_123.csv",header=T,na.rm=T)
+testOmit <- na.omit(testBase)
+View(testBase)
+testDirty <- df(col1=c(1,2,3,5,6,NA,7,8,9,10),col2=c(1,NA,3,5,6,27,7,8,9,10))
+testDirty <- data.frame(col1=c(1,2,3,5,6,NA,7,8,9,10),col2=c(1,NA,3,5,6,27,7,8,9,10))
+View(testDirty)
+testOmit <- na.omit(testDirty)
+grep("NA",testDirty)
+grep("NA",testOmit)
+TRUE AND FALSE
+TRUE | FALSE
+TRUE || FALSE
+T & F
+T & T
+F & T
+F & F
+combineCSVs <- function(directoryname,outputFile,country,na_handling){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+if(na_handling == "remove"){bigMatrix=na.omit(bigMatrix)}
+if(na_handling == "warn" ){
+if(grep("NA",bigMatrix) != 0){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+#write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+combineCSVs(directoryname = "countryx/",outputFile = "testBig.csv",country = "X")
+combineCSVs <- function(directoryname,outputFile,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+if(na_handling == "remove"){bigMatrix=na.omit(bigMatrix)}
+if(na_handling == "warn" ){
+if(grep("NA",bigMatrix) != 0){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+#write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+combineCSVs(directoryname = "countryx/",outputFile = "testBig.csv",country = "X",na_handling = "warn")
+combineCSVs <- function(directoryname,outputFile,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+if(na_handling == "remove"){bigMatrix=na.omit(bigMatrix)}
+if(na_handling == "warn" ){
+if(grep("NA", bigMatrix) != 0){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+#write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+combineCSVs(directoryname = "countryx/",outputFile = "testBig.csv",country = "X",na_handling = "warn")
+combineCSVs <- function(directoryname,outputFile,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+if(na_handling == "remove"){bigMatrix=na.omit(bigMatrix)}
+if(na_handling == "warn" ){
+if(grep(NA, bigMatrix) != 0){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+#write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+combineCSVs(directoryname = "countryx/",outputFile = "testBig.csv",country = "X",na_handling = "warn")
+combineCSVs <- function(directoryname,outputFile,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day)
+day.vect <- rep(day,n=nrow(tempDF))
+country.vect <- rep(country,n=nrow(tempDF))
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect)
+bigMatrix <- rbind(bigMatrix,expanded.tempDF)
+}
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+if(na_handling == "remove"){bigMatrix=na.omit(bigMatrix)}
+if(na_handling == "warn" ){
+if(any(is.na.data.frame(bigMatrix))){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+#write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+combineCSVs(directoryname = "countryx/",outputFile = "testBig.csv",country = "X",na_handling = "warn")
+#This file will contain supporting files
+makeCSV <- function(filename,delimeter,fileOutname){
+tempTable <- read.table(file=filename,sep=delimeter)
+write.csv(tempTable,file=paste(fileOutname,".csv",sep=""))
+}
+combineCSVs <- function(directoryname,outputFile,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+#initiating temp data obj for raw data from csv
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day) # using char replace to extract the day from file name
+day.vect <- rep(day,n=nrow(tempDF)) # making vector of day name to bind to tempDF
+country.vect <- rep(country,n=nrow(tempDF)) # making vector of country to bind to tempDF
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect) # binding day and country col to tempDF
+bigMatrix <- rbind(bigMatrix,expanded.tempDF) #binding the new completed DF to existing large one
+}
+#renaming columns of the final df
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+#code allowing user to request option removal of or warning about NA's present in data
+if(na_handling == "remove"){bigMatrix=na.omit(bigMatrix)}
+if(na_handling == "warn" ){
+if(any(is.na.data.frame(bigMatrix))){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+#write.csv(bigMatrix,file=outputFile,row.names = FALSE)
+}
+source("SupportingFunctions.R")
+#This file will contain supporting files
+makeCSV <- function(filename,delimeter,fileOutname){
+tempTable <- read.table(file=filename,sep=delimeter)
+write.csv(tempTable,file=paste(fileOutname,".csv",sep=""))
+}
+combineCSVs <- function(directoryname,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+#initiating temp data obj for raw data from csv
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day) # using char replace to extract the day from file name
+day.vect <- rep(day,n=nrow(tempDF)) # making vector of day name to bind to tempDF
+country.vect <- rep(country,n=nrow(tempDF)) # making vector of country to bind to tempDF
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect) # binding day and country col to tempDF
+bigMatrix <- rbind(bigMatrix,expanded.tempDF) #binding the new completed DF to existing large one
+}
+#renaming columns of the final df
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+#code allowing user to request option removal of or warning about NA's present in data
+if(na_handling == "remove"){bigMatrix=na.omit(bigMatrix)}
+if(na_handling == "warn" ){
+if(any(is.na.data.frame(bigMatrix))){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+}
+#this R file will be used to analyze the patient data
+# with the help of the functions in SupportingFunctions.R
+#setting current working directory; Change for your system
+setwd("/Users/kevinbuck/Desktop/Biocomputing/Exercises/Rproject")
+source("SupportingFunctions.R")
+#turning all the txt files for Country Y into CSV files
+for (i in 120:175){
+makeCSV(filename = paste("countryY/screen_",i,".txt",sep=""),delimeter = " ",
+fileOutname = paste("countryY/screen_",i,sep=""))
+}
+countryX.df <- combineCSVs(directoryname = "countryX/", country="X",na_handling = "warn")
+conutryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "warn")
+#This file will contain supporting files
+makeCSV <- function(filename,delimeter,fileOutname){
+tempTable <- read.table(file=filename,sep=delimeter)
+write.csv(tempTable,file=paste(fileOutname,".csv",sep=""))
+}
+combineCSVs <- function(directoryname,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+#initiating temp data obj for raw data from csv
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day) # using char replace to extract the day from file name
+day.vect <- rep(day,n=nrow(tempDF)) # making vector of day name to bind to tempDF
+country.vect <- rep(country,n=nrow(tempDF)) # making vector of country to bind to tempDF
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect) # binding day and country col to tempDF
+bigMatrix <- rbind(bigMatrix,expanded.tempDF) #binding the new completed DF to existing large one
+}
+#renaming columns of the final df
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+#code allowing user to request option removal of or warning about NA's present in data
+if(na_handling == "remove"){
+bigMatrix=na.omit(bigMatrix)
+print("Any NA Values in data removed")
+}
+if(na_handling == "warn" ){
+if(any(is.na.data.frame(bigMatrix))){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+}
+setwd("/Users/kevinbuck/Desktop/Biocomputing/Exercises/Rproject")
+source("SupportingFunctions.R")
+#turning all the txt files for Country Y into CSV files
+for (i in 120:175){
+makeCSV(filename = paste("countryY/screen_",i,".txt",sep=""),delimeter = " ",
+fileOutname = paste("countryY/screen_",i,sep=""))
+}
+#making giant data frame of screening data from both countries
+countryX.df <- combineCSVs(directoryname = "countryX/", country="X",na_handling = "warn")
+conutryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "warn")
+countryX.df <- combineCSVs(directoryname = "countryX/", country="X",na_handling = "remove")
+conutryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "remoce")
+countryX.df <- combineCSVs(directoryname = "countryX/", country="X",na_handling = "remove")
+conutryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "remove")
+countryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "remove")
+allData.df <- rbind(countryX.df,countryY.df)
+setwd("/Users/kevinbuck/Desktop/Biocomputing/Exercises/Rproject")
+source("SupportingFunctions.R")
+#turning all the txt files for Country Y into CSV files
+for (i in 120:175){
+makeCSV(filename = paste("countryY/screen_",i,".txt",sep=""),delimeter = " ",
+fileOutname = paste("countryY/screen_",i,sep=""))
+}
+#making giant data frame of screening data from both countries
+countryX.df <- combineCSVs(directoryname = "countryX/", country="X",na_handling = "remove")
+countryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "remove")
+allData.df <- rbind(countryX.df,countryY.df)
+View(countryY.df)
+makeCSV <- function(filename,delimeter,fileOutname){
+tempTable <- read.table(file=filename,sep=delimeter)
+write.csv(tempTable,file=paste(fileOutname,".csv",sep=""),row.names = F)
+}
+combineCSVs <- function(directoryname,country,na_handling="none"){
+#making list of all .csv files in directory using grep to search for .csv
+csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))]
+#initiating matrix for final results
+bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+for(csv.file in csv.files.list){
+#initiating temp data obj for raw data from csv
+tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+day <- gsub("[^0-9]*","",csv.file)
+day = gsub("[_]","",day) # using char replace to extract the day from file name
+day.vect <- rep(day,n=nrow(tempDF)) # making vector of day name to bind to tempDF
+country.vect <- rep(country,n=nrow(tempDF)) # making vector of country to bind to tempDF
+expanded.tempDF <- cbind(tempDF,country.vect,day.vect) # binding day and country col to tempDF
+bigMatrix <- rbind(bigMatrix,expanded.tempDF) #binding the new completed DF to existing large one
+}
+#renaming columns of the final df
+colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+"marker05","marker06","marker07","marker08","marker09","marker10",
+"country","dayofYear")
+colnames(bigMatrix) = colsList
+#code allowing user to request option removal of or warning about NA's present in data
+if(na_handling == "remove"){
+bigMatrix=na.omit(bigMatrix)
+print("Any NA Values in data removed")
+}
+if(na_handling == "warn" ){
+if(any(is.na.data.frame(bigMatrix))){print("Warning : Your Data Contains NA Values")}else{
+print("No NA Values Present")
+}
+}
+return(bigMatrix)
+}
+setwd("/Users/kevinbuck/Desktop/Biocomputing/Exercises/Rproject")
+source("SupportingFunctions.R")
+#turning all the txt files for Country Y into CSV files
+for (i in 120:175){
+makeCSV(filename = paste("countryY/screen_",i,".txt",sep=""),delimeter = " ",
+fileOutname = paste("countryY/screen_",i,sep=""))
+}
+#making giant data frame of screening data from both countries
+countryX.df <- combineCSVs(directoryname = "countryX/", country="X",na_handling = "remove")
+countryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "remove")
+allData.df <- rbind(countryX.df,countryY.df)
+View(allData.df)
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..f92e106
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+countryX/
+countryY/
+
diff --git a/CountryX.csv b/CountryX.csv
new file mode 100644
index 0000000..d71e88e
--- /dev/null
+++ b/CountryX.csv
@@ -0,0 +1,20063 @@
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diff --git a/InfectionsPlot.png b/InfectionsPlot.png
new file mode 100644
index 0000000..7dc19f1
Binary files /dev/null and b/InfectionsPlot.png differ
diff --git a/MarkerPlot.png b/MarkerPlot.png
new file mode 100644
index 0000000..25a2df0
Binary files /dev/null and b/MarkerPlot.png differ
diff --git a/SupportingFunctions.R b/SupportingFunctions.R
new file mode 100644
index 0000000..ed4a65e
--- /dev/null
+++ b/SupportingFunctions.R
@@ -0,0 +1,74 @@
+#This file will contain supporting files
+makeCSV <- function(filename,delimeter,fileOutname){
+  tempTable <- read.table(file=filename,sep=delimeter)
+  write.csv(tempTable,file=paste(fileOutname,".csv",sep=""),row.names = F)
+}
+
+#This function combines all the data from a directory into a big csv file and also
+# returns this aggregated data set as a data frame
+combineCSVs <- function(directoryname,country,na_handling="none",FileOutName){
+  #making list of all .csv files in directory using grep to search for .csv
+  csv.files.list <- dir(directoryname)[grep(".csv",dir(directoryname))] 
+  #initiating matrix for final results
+  bigMatrix <- matrix(data=NA,nrow=0,ncol=14)
+  for(csv.file in csv.files.list){
+    #initiating temp data obj for raw data from csv
+    tempDF <- read.csv(file=paste(directoryname,csv.file,sep=""),header=T)
+    day <- gsub("[^0-9]*","",csv.file)
+    day = gsub("[_]","",day) # using char replace to extract the day from file name
+    day.vect <- rep(day,n=nrow(tempDF)) # making vector of day name to bind to tempDF
+    country.vect <- rep(country,n=nrow(tempDF)) # making vector of country to bind to tempDF
+    expanded.tempDF <- cbind(tempDF,country.vect,day.vect) # binding day and country col to tempDF
+    bigMatrix <- rbind(bigMatrix,expanded.tempDF) #binding the new completed DF to existing large one
+  }
+  #renaming columns of the final df
+  colsList <- list("gender","age","marker01","marker02","marker03","marker04",
+                   "marker05","marker06","marker07","marker08","marker09","marker10",
+                   "country","dayofYear")
+  colnames(bigMatrix) = colsList
+  #code allowing user to request option removal of or warning about NA's present in data
+  if(na_handling == "remove"){
+    bigMatrix=na.omit(bigMatrix)
+    print("Any NA Values in data removed")
+    }
+  if(na_handling == "warn" ){
+    if(any(is.na.data.frame(bigMatrix))){print("Warning : Your Data Contains NA Values")}else{
+      print("No NA Values Present")
+    }
+    }
+  write.csv(bigMatrix,file = paste(as.character(FileOutName),".csv",sep=""))
+  return(bigMatrix)
+
+}
+# This function summarizes the Data Set for a variety of aspects
+SummarizeCompiledTests <- function(dfName){
+  screen_number <- nrow(dfName)
+  running_infected_total <- 0
+  #markers.list <- c("marker01","marker02","marker03","marker04","marker05",
+  #                  "marker06","marker07","marker08","marker09","marker10")
+  for (i in 1:screen_number){
+    if(sum(as.numeric(dfName[i,3:12] == "1"))){
+      running_infected_total <- running_infected_total +1
+      }
+  }
+  percentage.male <- sum(dfName[,"gender"] == "male") / screen_number * 100
+  percentage.female <- 100 - percentage.male
+  percentage.juvenile <- sum(dfName[,"age"] < 18) / screen_number * 100
+  percentage.adult <- (sum(dfName[,"age"] < 65) / screen_number *100 ) - percentage.juvenile
+  percentage.senior <- sum( dfName[,"age"] >= 65) / screen_number *100
+  infected <- running_infected_total / screen_number *100
+  print(c("Data Summary:",
+          paste("There were ",screen_number," screening tests run",sep=""),
+          #percentage of males out of all screened
+          paste(round(percentage.male,1),"% of patients were male",sep=""), 
+          #percentage of females out of all screened
+          paste(round(percentage.female,1),"% of patients were female",sep=""),
+          #percentage of juveniles out of all screened
+          paste(round(percentage.juvenile,1),"% of patients were juveniles (under 18)",sep=""),  
+          #percentage of adults out of all screened
+          paste(round(percentage.adult,1),"% of patients were adults (18-65)",sep=""),
+          #percentage of senior citizens out of all screened
+          paste(round(percentage.senior,1),"% of patients were seniors (over 65)",sep=""))
+          )
+}
+  
\ No newline at end of file
diff --git a/analysis.R b/analysis.R
new file mode 100644
index 0000000..17e1515
--- /dev/null
+++ b/analysis.R
@@ -0,0 +1,122 @@
+#this R file will be used to analyze the patient data
+# with the help of the functions in SupportingFunctions.R
+
+#setting current working directory; Change for your system
+setwd("/Users/kevinbuck/Desktop/Biocomputing/Exercises/Rproject")
+source("SupportingFunctions.R")
+####Initial Data Setup ####
+#turning all the txt files for Country Y into CSV files
+for (i in 120:175){
+  makeCSV(filename = paste("countryY/screen_",i,".txt",sep=""),delimeter = " ",
+          fileOutname = paste("countryY/screen_",i,sep=""))
+}
+
+#making giant data frame of screening data from both countries
+countryX.df <- combineCSVs(directoryname = "countryX/", country="X",na_handling = "remove",
+                           FileOutName = "CountryX")
+countryY.df <- combineCSVs(directoryname = "countryY/", country="Y",na_handling = "remove",
+                           FileOutName = "CountryY")
+
+allData.df <- rbind(countryX.df,countryY.df)
+
+#summarize data 
+SummarizeCompiledTests(allData.df)
+
+
+#making function to check if the screened patient is infected
+check_infected <- function(row_index,df){
+  return(sum(as.numeric(df[row_index,3:12] == "1")) != 0)
+}
+
+####Tracking Cases Over Time####
+
+day.list <- as.character(seq(120,175))
+
+#initiating data frame to store info
+Daily_Total_Cases <- data.frame(Day=as.numeric(day.list),CountryX.Total=numeric(length(day.list)),
+                                  CountryY.Total=numeric(length(day.list)))
+row.names(Daily_Total_Cases) = day.list
+#this big loop checks if a screened row has any markers present (is patient infected?)
+# and then it will add 1 to the total for cases in the appropriate day as stored in the dataframe
+for(day_num in day.list){
+  tempDF <- allData.df[allData.df[,"dayofYear"] == day_num,]
+  for(i in 1:nrow(tempDF)){
+    if( (check_infected(i,tempDF) == TRUE) & (tempDF[i,'country'] == "X")){
+      Daily_Total_Cases[day_num,"CountryX.Total"] = 1 + Daily_Total_Cases[day_num,"CountryX.Total"]
+    }
+    if((check_infected(i,tempDF) == TRUE) & (tempDF[i,'country'] == "Y")){
+      Daily_Total_Cases[day_num,"CountryY.Total"] = 1 + Daily_Total_Cases[day_num,"CountryY.Total"]
+    }
+  }
+}
+
+#####Plotting Daily Cases by Day#####
+
+#Notice how Country X has cases starting at day 120, and the country Y
+# cases line is at 0 cases until day 138ish. This means the outbreak began in country X
+
+library(ggplot2)
+ggplot()+geom_line(data=Daily_Total_Cases,mapping=aes(x=Day,y=CountryX.Total,color="CountryX")) +
+  geom_line(data=Daily_Total_Cases,mapping=aes(x=Day,y=CountryY.Total,color="CountryY")) +
+  ylab("Daily Cases") + xlab("Day Number") + ggtitle("Infections Over Time")+ 
+  scale_color_manual(name = "Country", values = c("CountryX" = "blue", "CountryY" = "red")) 
+
+
+#Figuring out which markers cause the disease
+total.cases.X <- 0
+for (i in 1:nrow(countryX.df)){
+  if(check_infected(i,countryX.df) == T){ 
+    total.cases.X <- total.cases.X +1
+    }
+}
+total.cases.Y <- 0
+for (i in 1:nrow(countryY.df)){
+  if(check_infected(i,countryY.df) == T){ 
+    total.cases.Y <- total.cases.Y +1
+  }}
+
+x.markers.pct <- numeric(10)
+y.markers.pct <- numeric(10)
+x.markers.pct[1] <- sum(countryX.df[,"marker01"] == "1") / total.cases.X
+y.markers.pct[1] <- sum(countryY.df[,"marker01"] == "1") / total.cases.Y
+x.markers.pct[2] <- sum(countryX.df[,"marker02"] == "1") / total.cases.X
+y.markers.pct[2] <- sum(countryY.df[,"marker02"] == "1") / total.cases.Y
+x.markers.pct[3] <- sum(countryX.df[,"marker03"] == "1") / total.cases.X
+y.markers.pct[3] <- sum(countryY.df[,"marker03"] == "1") / total.cases.Y
+x.markers.pct[4] <- sum(countryX.df[,"marker04"] == "1") / total.cases.X
+y.markers.pct[4] <- sum(countryY.df[,"marker04"] == "1") / total.cases.Y
+x.markers.pct[5] <- sum(countryX.df[,"marker05"] == "1") / total.cases.X
+y.markers.pct[5] <- sum(countryY.df[,"marker05"] == "1") / total.cases.Y
+x.markers.pct[6] <- sum(countryX.df[,"marker06"] == "1") / total.cases.X
+y.markers.pct[6] <- sum(countryY.df[,"marker06"] == "1") / total.cases.Y
+x.markers.pct[7] <- sum(countryX.df[,"marker07"] == "1") / total.cases.X
+y.markers.pct[7] <- sum(countryY.df[,"marker07"] == "1") / total.cases.Y
+x.markers.pct[8] <- sum(countryX.df[,"marker08"] == "1") / total.cases.X
+y.markers.pct[8] <- sum(countryY.df[,"marker08"] == "1") / total.cases.Y
+x.markers.pct[9] <- sum(countryX.df[,"marker09"] == "1") / total.cases.X
+y.markers.pct[9] <- sum(countryY.df[,"marker09"] == "1") / total.cases.Y
+x.markers.pct[10] <- sum(countryX.df[,"marker10"] == "1") / total.cases.X
+y.markers.pct[10] <- sum(countryY.df[,"marker10"] == "1") / total.cases.Y
+markers.list <- as.character(seq(1,10))
+markers.sum.table <- matrix(ncol = 3,nrow=20)
+colnames(markers.sum.table) <- c("country","marker","freq")
+markers.sum.table <- as.data.frame(markers.sum.table)
+for (i in 1:10){
+  tempvect01 <- c("X",as.character(i),x.markers.pct[i])
+  markers.sum.table[i,] <- tempvect01
+  tempvect02 <- c("Y",as.character(i),y.markers.pct[i])
+  markers.sum.table[10+i,] <- tempvect02
+}
+#plotting the markers that cause disease
+# Note how the distribution of markers for country X is skewed towards Markers 1-5,
+# Whereas Country Y has a relatively even distribution. This means that a vaccine targeted
+# At patients in country Y might be based upon solving a problem caused by a micro satellite the patient in 
+# Country X might not have present. Thus, vaccines developed by country Y MIGHT work for 
+# Country X, but it is not certain.
+ggplot(data=markers.sum.table,aes(x=marker,y=freq,fill=country)) + 
+  geom_bar(stat="identity",position = "dodge",alpha=.75) + 
+  ggtitle("Relative Frequency of MicroSatellites")
+  
+
+
+
diff --git a/countryY/screen_120.csv b/countryY/screen_120.csv
deleted file mode 100644
index 1d6083f..0000000
--- a/countryY/screen_120.csv
+++ /dev/null
@@ -1,498 +0,0 @@
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diff --git a/countryY/screen_121.csv b/countryY/screen_121.csv
deleted file mode 100644
index 7b3f944..0000000
--- a/countryY/screen_121.csv
+++ /dev/null
@@ -1,485 +0,0 @@
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deleted file mode 100644
index 1aa0849..0000000
--- a/countryY/screen_123.csv
+++ /dev/null
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deleted file mode 100644
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--- a/countryY/screen_124.csv
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deleted file mode 100644
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deleted file mode 100644
index 77ce93f..0000000
--- a/countryY/screen_127.csv
+++ /dev/null
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deleted file mode 100644
index 3616248..0000000
--- a/countryY/screen_128.csv
+++ /dev/null
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diff --git a/countryY/screen_129.csv b/countryY/screen_129.csv
deleted file mode 100644
index bac88c7..0000000
--- a/countryY/screen_129.csv
+++ /dev/null
@@ -1,252 +0,0 @@
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diff --git a/countryY/screen_130.csv b/countryY/screen_130.csv
deleted file mode 100644
index b40e16e..0000000
--- a/countryY/screen_130.csv
+++ /dev/null
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diff --git a/countryY/screen_133.csv b/countryY/screen_133.csv
deleted file mode 100644
index b23d6a1..0000000
--- a/countryY/screen_133.csv
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diff --git a/countryY/screen_134.csv b/countryY/screen_134.csv
deleted file mode 100644
index 1160ab4..0000000
--- a/countryY/screen_134.csv
+++ /dev/null
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diff --git a/countryY/screen_136.csv b/countryY/screen_136.csv
deleted file mode 100644
index 113e940..0000000
--- a/countryY/screen_136.csv
+++ /dev/null
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deleted file mode 100644
index 710dbdc..0000000
--- a/countryY/screen_137.csv
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diff --git a/countryY/screen_139.csv b/countryY/screen_139.csv
deleted file mode 100644
index fcd21df..0000000
--- a/countryY/screen_139.csv
+++ /dev/null
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deleted file mode 100644
index 9fc8c65..0000000
--- a/countryY/screen_141.csv
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diff --git a/countryY/screen_142.csv b/countryY/screen_142.csv
deleted file mode 100644
index 3aa1f73..0000000
--- a/countryY/screen_142.csv
+++ /dev/null
@@ -1,434 +0,0 @@
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deleted file mode 100644
index af9623a..0000000
--- a/countryY/screen_144.csv
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deleted file mode 100644
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--- a/countryY/screen_145.csv
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deleted file mode 100644
index 04e80fe..0000000
--- a/countryY/screen_146.csv
+++ /dev/null
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deleted file mode 100644
index 502c993..0000000
--- a/countryY/screen_147.csv
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deleted file mode 100644
index 8175ed6..0000000
--- a/countryY/screen_148.csv
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deleted file mode 100644
index 0197e79..0000000
--- a/countryY/screen_149.csv
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diff --git a/countryY/screen_150.csv b/countryY/screen_150.csv
deleted file mode 100644
index bd01a9c..0000000
--- a/countryY/screen_150.csv
+++ /dev/null
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deleted file mode 100644
index e2e4b6c..0000000
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deleted file mode 100644
index 0b4833a..0000000
--- a/countryY/screen_152.csv
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diff --git a/countryY/screen_153.csv b/countryY/screen_153.csv
deleted file mode 100644
index 136acf1..0000000
--- a/countryY/screen_153.csv
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index 83a23fe..0000000
--- a/countryY/screen_155.csv
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diff --git a/countryY/screen_156.csv b/countryY/screen_156.csv
deleted file mode 100644
index a7bbad2..0000000
--- a/countryY/screen_156.csv
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deleted file mode 100644
index ab9c91f..0000000
--- a/countryY/screen_157.csv
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deleted file mode 100644
index a722190..0000000
--- a/countryY/screen_158.csv
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diff --git a/countryY/screen_159.csv b/countryY/screen_159.csv
deleted file mode 100644
index a94d5ad..0000000
--- a/countryY/screen_159.csv
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diff --git a/countryY/screen_161.csv b/countryY/screen_161.csv
deleted file mode 100644
index 3436247..0000000
--- a/countryY/screen_161.csv
+++ /dev/null
@@ -1,309 +0,0 @@
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diff --git a/countryY/screen_162.csv b/countryY/screen_162.csv
deleted file mode 100644
index 64c52db..0000000
--- a/countryY/screen_162.csv
+++ /dev/null
@@ -1,435 +0,0 @@
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diff --git a/countryY/screen_163.csv b/countryY/screen_163.csv
deleted file mode 100644
index edfe02d..0000000
--- a/countryY/screen_163.csv
+++ /dev/null
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diff --git a/countryY/screen_164.csv b/countryY/screen_164.csv
deleted file mode 100644
index 8b3e70d..0000000
--- a/countryY/screen_164.csv
+++ /dev/null
@@ -1,302 +0,0 @@
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diff --git a/countryY/screen_165.csv b/countryY/screen_165.csv
deleted file mode 100644
index 92112ac..0000000
--- a/countryY/screen_165.csv
+++ /dev/null
@@ -1,309 +0,0 @@
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diff --git a/countryY/screen_166.csv b/countryY/screen_166.csv
deleted file mode 100644
index 10b6ffe..0000000
--- a/countryY/screen_166.csv
+++ /dev/null
@@ -1,349 +0,0 @@
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index 07772dc..0000000
--- a/countryY/screen_167.csv
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deleted file mode 100644
index 4c33754..0000000
--- a/countryY/screen_168.csv
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diff --git a/countryY/screen_170.csv b/countryY/screen_170.csv
deleted file mode 100644
index 5b057d0..0000000
--- a/countryY/screen_170.csv
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diff --git a/countryY/screen_171.csv b/countryY/screen_171.csv
deleted file mode 100644
index 04dbd5d..0000000
--- a/countryY/screen_171.csv
+++ /dev/null
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diff --git a/countryY/screen_172.csv b/countryY/screen_172.csv
deleted file mode 100644
index cdb4453..0000000
--- a/countryY/screen_172.csv
+++ /dev/null
@@ -1,331 +0,0 @@
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diff --git a/countryY/screen_173.csv b/countryY/screen_173.csv
deleted file mode 100644
index 704eaa1..0000000
--- a/countryY/screen_173.csv
+++ /dev/null
@@ -1,298 +0,0 @@
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diff --git a/countryY/screen_174.csv b/countryY/screen_174.csv
deleted file mode 100644
index c1cd2ef..0000000
--- a/countryY/screen_174.csv
+++ /dev/null
@@ -1,390 +0,0 @@
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deleted file mode 100644
index 9d00a50..0000000
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new file mode 100644
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