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+"female",14,0,0,0,0,0,0,0,0,1,0,"Y","175" +"female",30,0,0,0,0,0,1,1,0,0,0,"Y","175" +"male",59,0,0,0,0,0,1,0,0,1,0,"Y","175" +"male",5,0,0,0,0,0,0,0,1,0,0,"Y","175" +"male",18,0,0,0,0,0,0,1,0,1,1,"Y","175" +"female",5,0,0,0,0,0,0,0,0,0,1,"Y","175" +"female",14,0,0,0,0,0,1,0,0,0,0,"Y","175" +"male",7,0,0,0,0,0,0,0,0,0,0,"Y","175" +"female",4,0,0,0,0,0,0,1,0,0,0,"Y","175" diff --git a/analysis.R b/analysis.R new file mode 100644 index 0000000..dc8bfd6 --- /dev/null +++ b/analysis.R @@ -0,0 +1,222 @@ +####R project +####Analysis +####Shannon Culbert and Camden Arnold +####All code assumes working directory is set to RProject directory + +################################################################################ + +source("supportingFunctions.R") + +##convert all .txt files to .csv files +convert_to_csv("countryY") + +##combine all .csv files from both countries into one large .csv file +combine_data("countryX","countryY") + +##summarize important data +summary_data("allData.csv") + +################################################################################ + +###Question 1: In which country did the disease outbreak likely begin? +###Answer: The outbreak likely began in country X because infected +###individuals for Country X were detected on day 120, whereas the +###first infected individuals for Country Y were detected on day 139 + +##This reasoning can best be understood by viewing a line graph +##that compares the day of infection to the number of infected individuals + +Day.v.Infections <- function(file){ + allData <- read.csv("allData.csv") + #create separate Country X and Y dataframes to hold information + XDayInfect.df <- data.frame(matrix(NA,0,3)) + YDayInfect.df <- data.frame(matrix(NA,0,3)) + colnames(XDayInfect.df) <- c("country","dayofYear", "numberofInfections") + colnames(YDayInfect.df) <- c("country","dayofYear", "numberofInfections") + + XDayofYear <- unique(allData$dayofYear[allData$country=="X"]) + YDayofYear <- unique(allData$dayofYear[allData$country=="Y"]) + + #use a ForLoop to determine Country X's infected individuals at each dayofYear + for(i in 1:length(XDayofYear)){ + XDayInfect.df[i,1] <- "X" + XDayInfect.df[i,2] <- XDayofYear[i] + XDay <- allData[allData$dayofYear==XDayofYear[i],] + XPatients <- XDay[XDay$country=="X",] + XPat.num <- XPatients[(rowSums(XPatients[,3:12])>0),] + XDayInfect.df[i,3] <- nrow(XPat.num) + } + + #use a ForLoop to determine Country Y's infected individuals at each dayofYear + for(i in 1:length(YDayofYear)){ + YDayInfect.df[i,1] <- "Y" + YDayInfect.df[i,2] <- YDayofYear[i] + YDay <- allData[allData$dayofYear==YDayofYear[i],] + YPatients <- YDay[YDay$country=="Y",] + YPat.num <- YPatients[(rowSums(YPatients[,3:12])>0),] + YDayInfect.df[i,3] <- nrow(YPat.num) + } + + #combine Country X and Country Y dataframes together + allInfections <- rbind(XDayInfect.df, YDayInfect.df) + + #create the line graph to compare the number of infections on each day for each country + library(ggplot2) + ggplot(data = allInfections, aes(x = dayofYear, y = numberofInfections, group = country, color = country))+ + geom_line() + + geom_point() + + theme_bw() + + xlab("Day of the Year") + + ylab("Number of Infections") + #CountryX infections are labeled in red + #CountryY infections are labeled in blue +} + +Day.v.Infections("allData.csv") +#Since CountryX had an earlier exposure to disease and a greater number of infections, +#we can infer that the disease outbreak most likely began in CountryX + +################################################################################ + +###Question 2: If Country Y develops a vaccine for the disease, +###is it likely to work for citizens in Country X? +###Answer: No, because the disease that affects individuals in Country Y +###have different markers. If a vaccine is developed in Country Y, then the vaccine will +###target the markers that are representative of Country Y (such as markers #05-10). +###Since Country Y's vaccine targets markers 05-10, which are not readily present +###in the disease that affects individuals in Country X, +###the vaccine will not work for the citizens of Country X. + +##This reasoning can best be understood by viewing a barplot +##that compares the markers that are most abundant in each country + +Marker.Bar <- function(file){ + allData<-read.csv("allData.csv") + XallData <- allData[allData$country=="X",] + YallData <- allData[allData$country=="Y",] + + #create a count for each marker + Xmarker01_count<-0 + Xmarker02_count<-0 + Xmarker03_count<-0 + Xmarker04_count<-0 + Xmarker05_count<-0 + Xmarker06_count<-0 + Xmarker07_count<-0 + Xmarker08_count<-0 + Xmarker09_count<-0 + Xmarker10_count<-0 + + Ymarker01_count<-0 + Ymarker02_count<-0 + Ymarker03_count<-0 + Ymarker04_count<-0 + Ymarker05_count<-0 + Ymarker06_count<-0 + Ymarker07_count<-0 + Ymarker08_count<-0 + Ymarker09_count<-0 + Ymarker10_count<-0 + + #Use a ForLoop with If/Else statements to count each marker + for (row in 1:nrow(XallData)){ + if (XallData$marker01[row] == 1){ + Xmarker01_count<-Xmarker01_count + 1 + } + if (XallData$marker02[row] == 1){ + Xmarker02_count<-Xmarker02_count + 1 + } + if (XallData$marker03[row] == 1){ + Xmarker03_count<-Xmarker03_count + 1 + } + if (XallData$marker04[row] == 1){ + Xmarker04_count<-Xmarker04_count + 1 + } + if (XallData$marker05[row] == 1){ + Xmarker05_count<-Xmarker05_count + 1 + } + if (XallData$marker06[row] == 1){ + Xmarker06_count<-Xmarker06_count + 1 + } + if (XallData$marker07[row] == 1){ + Xmarker07_count<-Xmarker07_count + 1 + } + if (XallData$marker08[row] == 1){ + Xmarker08_count<-Xmarker08_count + 1 + } + if (XallData$marker09[row] == 1){ + Xmarker09_count<-Xmarker09_count + 1 + } + if (XallData$marker10[row] == 1){ + Xmarker10_count<-Xmarker10_count + 1 + }} + + for (row in 1:nrow(YallData)){ + if (YallData$marker01[row] == 1){ + Ymarker01_count<-Ymarker01_count + 1 + } + if (YallData$marker02[row] == 1){ + Ymarker02_count<-Ymarker02_count + 1 + } + if (YallData$marker03[row] == 1){ + Ymarker03_count<-Ymarker03_count + 1 + } + if (YallData$marker04[row] == 1){ + Ymarker04_count<-Ymarker04_count + 1 + } + if (YallData$marker05[row] == 1){ + Ymarker05_count<-Ymarker05_count + 1 + } + if (YallData$marker06[row] == 1){ + Ymarker06_count<-Ymarker06_count + 1 + } + if (YallData$marker07[row] == 1){ + Ymarker07_count<-Ymarker07_count + 1 + } + if (YallData$marker08[row] == 1){ + Ymarker08_count<-Ymarker08_count + 1 + } + if (YallData$marker09[row] == 1){ + Ymarker09_count<-Ymarker09_count + 1 + } + if (YallData$marker10[row] == 1){ + Ymarker10_count<-Ymarker10_count + 1 + }} + + #create dataframes for marker information + Xmarker.df <- data.frame(matrix(NA,10,3)) + colnames(Xmarker.df) <- c("country", "marker","occurrence") + Xmarker.df[,1] <- "X" + Xmarker.df[,2] <- c("marker01","marker02","marker03","marker04","marker05","marker06","marker07","marker08","marker09","marker10") + Xmarker.df[,3] <- c(Xmarker01_count,Xmarker02_count,Xmarker03_count,Xmarker04_count,Xmarker05_count,Xmarker06_count,Xmarker07_count,Xmarker08_count,Xmarker09_count,Xmarker10_count) + + Ymarker.df <- data.frame(matrix(NA,10,3)) + colnames(Ymarker.df) <- c("country", "marker","occurrence") + Ymarker.df[,1] <- "Y" + Ymarker.df[,2] <- c("marker01","marker02","marker03","marker04","marker05","marker06","marker07","marker08","marker09","marker10") + Ymarker.df[,3] <- c(Ymarker01_count,Ymarker02_count,Ymarker03_count,Ymarker04_count,Ymarker05_count,Ymarker06_count,Ymarker07_count,Ymarker08_count,Ymarker09_count,Ymarker10_count) + + #combine X Y marker dataframes + allMarkers <- rbind(Xmarker.df, Ymarker.df) + + #create the bargraph to compare marker occurrence for each country + library(ggplot2) + + ggplot(allMarkers, aes(x = marker, y = occurrence, fill = country)) + + geom_bar(stat="identity", position = position_dodge(), alpha = 0.75) + + theme_classic() + + xlab("Marker Number") + + ylab("Occurrence") + + theme(axis.text.x = element_text(angle=65, vjust=0.6)) + #CountryX markers are labeled in red + #CountryY markers are labeled in blue +} + +Marker.Bar("allData.csv") +#Since each country has different representative markers +#(CountryX is predominantly markers 01-05, and +#CountryY is predominantly markers 06-10), a vaccine developed +#in CountryY would not likely work for CountryX because each country +#will have different immunological responses. +#If a vaccine is developed in CountryY, it will most likely target markers 06-10, +#and very little people in CountryX have markers 06-10. \ No newline at end of file diff --git a/supportingFunctions.R b/supportingFunctions.R new file mode 100644 index 0000000..64810b3 --- /dev/null +++ b/supportingFunctions.R @@ -0,0 +1,130 @@ +####R Project +####Supporting Functions +####Shannon Culbert and Camden Arnold +####All code assumes working directory is set to RProject directory + +################################################################################ + +###Part 1: Converting all .txt files into .csv files +#Convert all .txt files to .csv files using for loop + +convert_to_csv <- function(dir) { + txt_files <- list.files(dir,pattern=".txt") #lists .txt files that need converted + for (i in txt_files) { + file_path <- file.path(dir,i) + newfile_name <- sub(".txt",".csv",i) + file_path2 <- file.path(dir,newfile_name) + data <- read.table(file=file_path, header=TRUE, sep="") #reads .txt file + write.csv(data,file=file_path2,row.names=FALSE,col.names=FALSE) #converts to .csv + } +} +#Country X has .csv files already +#Country Y needs .txt files to be converted to .csv files +#run function to convert Country Y's .txt files to .csv files +#usage: convert_to_csv("countryY") + +################################################################################ + +###Part 2: Converting all .csv files into a single .csv file + +#create path to add all of the information into "allData.csv" +#includes adding columns for Country and DayOfYear +combine_data <- function(dir1,dir2) { #creates allData.csv to which data will be added + path_init <- file.path("countryX","screen_120.csv") + data_init <- read.csv(path_init,header=TRUE) + data_init$country = "X" + data_init$dayofYear = "120" + write.csv(head(data_init,n=0), file = "allData.csv",col.names=FALSE, row.names=FALSE) + + #First country will be added into the "allData.csv" + files_1 <- list.files(dir1,pattern=".csv") + for (i in files_1) { + file_path <- file.path(dir1,i) + day_number <- substr(i,8,10) + country_letter <- substr(dir1,8,8) + data <- read.csv(file=file_path, header=TRUE) + data$country = country_letter + data$dayofYear = day_number + write.table(data, file = "allData.csv", append=TRUE, sep=",", col.names=FALSE, row.names=FALSE) + } + + #Second country will be added into the "allData.csv" + files_2 <- list.files(dir2,pattern=".csv") + for (i in files_2) { + file_path <- file.path(dir2,i) + day_number <- substr(i,8,10) + country_letter <- substr(dir2,8,8) + data <- read.csv(file=file_path, header=TRUE) + data$country = country_letter + data$dayofYear = day_number + write.table(data, file = "allData.csv", append=TRUE, sep=",", col.names=FALSE, row.names=FALSE) + } +} + +#run function for countryX and countryY +#usage: combine_data("countryX","countryY") + +################################################################################ + +###Part 3: Summarizing data sets + +summary_data <- function(file){ + allData <- read.table(file("allData.csv"), header = TRUE, sep = ",") + ##Part 3A: Number of Screens Run + #Number of screens run will be equal to the number of rows in the allData file + print("The total number of screens run is:") + print(nrow(allData)) + + ##Part 3B: Percent of Patients Screened that were Infected + infected_count = 0 + for(i in 1:nrow(allData)){ + if (sum(allData[i,3:12]) > 0) { + infected_count = infected_count + 1} + } + percent.patients.infected <- (infected_count/nrow(allData))*100 + print("The percentage of screened patient's who are infected is:") + print(percent.patients.infected) + + ##Part 3C: Male vs. Female Patients + males <- allData[allData$gender=="male",] + print("The number of all male patients is:") + print(nrow(males)) + + females <- allData[allData$gender=="female",] + print("The number of all female patients is:") + print(nrow(females)) + + #if only looking at infected Male vs. Female Patients + Males <- allData[allData$gender=="male",] + Females <- allData[allData$gender=="female",] + + Males.infected_count = 0 + for(i in 1:nrow(Males)){ + if (sum(Males[i,3:12]) > 0) { + Males.infected_count = Males.infected_count + 1} + } + print("The number of male patients who are infected is:") + print(Males.infected_count) + + Females.infected_count = 0 + for(i in 1:nrow(Females)){ + if (sum(Females[i,3:12]) > 0) { + Females.infected_count = Females.infected_count + 1} + } + print("The number of female patients who are infected is:") + print(Females.infected_count) + + ##Part 3D: Age Distribution of Patients + print("The statistical summary of age distribution of all patients is:") + print(summary(allData$age)) + + #if only looking at age distribution of infected individuals + allData$markerSum=allData$marker01+allData$marker02+allData$marker03+allData$marker04+allData$marker05+allData$marker06+allData$marker07+allData$marker08+allData$marker09+allData$marker10 + allData.infected <- allData[allData$markerSum>0,] + print("The statistical summary of age distribution of all infected patients is:") + print(summary(allData.infected$age)) + #recording error by study: maximum age is 423??? +} + +#run function to view the answers for each part: +#usage: summary_data("allData.csv") \ No newline at end of file