Now it works!

Analysis.R

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+#Graphing------------------------------
+#Reading in data
+allData<-read.csv(file = "allData.csv", header = TRUE)
+library(ggplot2)
+ggplot()
+
+#The data had strangely high ages that were likely errors; this corrects them
+removed_strange_ages<-allData[allData$age <110,]
+
+#finding entries with a marker present; removes any entries that weren't infected
+sick<-subset(removed_strange_ages, marker01!=0 | marker02!=0 |marker03!=0 |marker04!=0 |marker05!=0 |marker06!=0 |marker07!=0 |marker08!=0 | marker09!=0 |marker10!=0)
+
+#ggplotting cases as a function of time
+#sub-setting and adjusting data
+Xdata<- sick[sick$country=="X",]
+Ydata<-sick[sick$country=="Y",]
+
+Yno<-seq_along(Ydata$dayofYear)
+Xno<-seq_along(Xdata$country)
+sick$Casenum<-append(Xno,Yno, after = length(Xno))
+
+#Plot Cumulative cases in each country against time
+ggplot(data = sick,
+       aes(x=sick$dayofYear, y=sick$Casenum, colour = country))+
+  geom_smooth()+
+  xlab("Day of the Year") +
+  ylab("Running Case Total")+
+  theme_classic()
+
+#Plot new cases against time
+ggplot(data = sick,
+       aes(x=dayofYear))+
+  geom_bar(position="dodge", aes( fill = country), show.legend = FALSE)+
+  xlab("Day of the Year") +
+  ylab("New Cases Per Day")+
+  facet_grid(.~country)+
+  theme_classic()
+
+#Marker comparison by country
+#subset data
+XmarkerData<-data.frame( MarkerNum = colnames(Xdata)[3:12],
+                         MarkerSum = colSums(Xdata[,3:12]),
+                         Country = Xdata[1:10,13])
+rownames(XmarkerData)<-1:nrow(XmarkerData)
+YmarkerData<-data.frame( MarkerNum = colnames(Ydata)[3:12],
+                         MarkerSum = colSums(Ydata[,3:12]),
+                         Country = Ydata[1:10,13])
+rownames(YmarkerData)<-1:nrow(YmarkerData)
+MarkerforPlot<- rbind(XmarkerData,YmarkerData)
+#plot
+ggplot(data = MarkerforPlot,
+       aes(x=Country, y=MarkerSum, fill=MarkerNum))+
+  geom_bar(stat="identity", position="dodge")+
+  xlab("Country") +
+  ylab("Marker Abundance")+
+  theme_minimal()
+
+
+
+
+
+
+
+
+
+
+
+

analysis.R

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+#Age distribution histogram of patients (this goes is supportingFunctions.R script)
+#Reading in data
+allData<-read.csv(file = "allData.csv", header = TRUE)
+
+#The data had strangely high ages that were likely errors; this corrects them
+removed_strange_ages<-allData[allData$age <120,]
+
+#finding entries with a marker present
+sick<-subset(removed_strange_ages, marker01!=0 | marker02!=0 |marker03!=0 |marker04!=0 |marker05!=0 |marker06!=0 |marker07!=0 |marker08!=0 | marker09!=0 |marker10!=0)
+
+#visualize age distribution 
+
+# Load the ggplot2 package
+library(ggplot2)
+ggplot()
+
+# Create the histogram using the ggplot function and the geom_histogram function
+ggplot(sick, aes(x = sick$age)) +
+  geom_histogram(binwidth = 10, center = 5) +
+  xlab("Age Groups") +
+  ylab("Number of People Infected") +
+  xlim(0,120) +
+  scale_x_continuous(breaks =seq(0,120,10)) +
+  scale_y_continuous(breaks =seq(0,15000,1250)) 
+
+#analysis.R script
+
+#create source path to supportingFunctions.R script
+source("C:/Users/natal/Desktop/shell-lesson-data/shell-lesson-data/RProject/RProject2022_Submission.R")
+
+#Load functions in supportingFunctions.R file
+source("RProject2022_Submission.R")
+
+#Compile all data into single CSV by calling function
+compiledData()
+
+
+#Process data 

analysis_for_grading.R

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+#github was very uncooperative, we all did pretty equal work though 
+#1)our figures provide good evidence based on the cases per day the the disease originated in country X. 
+#2)We also determined that a vaccine developed in country Y would have limited uses for country X. 
+#The disparity in markers present between the two countries means that the little overlap that doesoccur would not confer good immunity to country X people. 
+
+#Analysis R script---------------------
+
+#create source path to RProject2022_Submission.R script
+setwd(C:/Users/natal/Desktop/shell-lesson-data/shell-lesson-data/Rproject)
+source("C:/Users/natal/Desktop/shell-lesson-data/shell-lesson-data/Rproject/RProject2022_Submission.R")
+
+#Load functions in RProject2022_Submission.R file
+source(RProject2022_Submission.R)
+#-------------------------
+#Compile all data into single CSV by calling function
+# compile data from all .csv files in a directory into a single .csv file
+#same columns, also add "country" and "dayofYear" columns.
+#user should be able to remove NA rows, include NA rows but be warned, 
+#or include NAs without warning
+
+#Path directory
+setwd("/Users/avivalund/Desktop/Biocomputing/FinalProject/RProject/")
+
+#Compile; couldnt get source to work so here is the code that would compile the csvs
+compileData <- function(directory,country,naOption){
+  #setting directory variable while testing , delete when functions works
+  setwd(directory)
+  csvlist <- list.files(path=directory, pattern = ".csv")
+  input<-read.csv(csvlist[1])
+  input$country <-  country
+  input$dayofYear <- as.numeric(substr(csvlist[1], 8, 10))
+
+  for (i in 2:length(csvlist)){
+    inputloop<-read.csv(csvlist[i])
+    inputloop$country <- country 
+    inputloop$dayofYear <- as.numeric(substr(csvlist[i], 8, 10)) 
+    input=rbind(input,inputloop)
+  } 
+
+  if (naOption == "remove") {
+    # Remove rows with NA's in any columns
+    input <- na.omit(input)
+  } else if (naOption == "warn") {
+    # Check for NA's in the data and warn the user if they are present
+    if (any(is.na(input))) {
+      warning("Data contains NA values")
+    }
+  } else if (naOption == "include") {
+    # Do nothing - include NA's in the data without warning the user
+  } else {
+    # Invalid option - raise an error
+    stop("Invalid value for naOption parameter")
+  }
+  write.csv(input, paste("country",country,"_alldata.csv"), row.names = F)
+
+}
+#------------------------------------------------------------------------------------------------------------------
+#Script 2: analyzing script, source("supportingFunctions.R")
+# Write a function to summarize the compiled data set in terms of
+# number of screens run, percent of patients screened that were infected,
+# male vs. female patients, and the age distribution of patients.
+
+#percentage male, percentage female, percentage positive, percentage negative, age distribution
+summarydata<-read.csv("./alldata.csv")
+removed_strange_ages<-data[data$age <110,]
+summarizedCompileData <- function(data){
+  cat("Summarized Data")
+
+  #number of screens run
+  cat("\nNumber of Screens Run:",nrow(data))
+
+  #number of infected or healthy patients; removed ages that were likely errors
+  removed_strange_ages<-data[data$age <110,]
+  sick<-subset(removed_strange_ages, marker01!=0 | marker02!=0 |marker03!=0 |marker04!=0 |marker05!=0 |marker06!=0 |marker07!=0 |marker08!=0 | marker09!=0 |marker10!=0)
+  healthy<-subset(removed_strange_ages, marker01!=1 & marker02!=1 & marker03!=1 & marker04!=1 & marker05!=1 & marker06!=1 & marker07!=1 & marker08!=1 & marker09!=1 & marker10!=1)
+  cat("\nNumber of Positive Screenings:",nrow(sick))
+
+  #percentages
+  cat("\n\nPercentage of screenings that were positive:",(nrow(sick)/nrow(removed_strange_ages))*100,"%")
+  cat("\nPercentage of positive screens that were male:",((nrow(subset(sick, gender=="male")))/nrow(sick)*100),"%")
+  cat("\n\nPercentage of positive screens that were female:",((nrow(subset(sick, gender=="female")))/nrow(sick)*100),"%")
+  cat("\nPercentage of screenings that were negative:",(nrow(healthy)/nrow(removed_strange_ages))*100,"%")
+
+  #visualize age distribution
+  # Load the ggplot2 package
+  library(ggplot2)
+  # Create the histogram using the ggplot function and the geom_histogram function
+  ggplot(sick, aes(x =age)) +
+    geom_histogram(binwidth = 10, center = 5, color="white") +
+    xlab("Age Groups") +
+    ylab("Number of People Infected") +
+    xlim(0,120) +
+    ggtitle("Age Distribution by Sex of Patients")+
+    facet_grid(.~gender)+
+    scale_x_continuous(breaks =seq(0,120,10)) +
+    theme_bw()+
+    scale_y_continuous(breaks =seq(0,15000,1250))
+}
+
+
+
+#Graphing------------------------------
+#Reading in data
+allData<-read.csv(file = "allData.csv", header = TRUE)
+library(ggplot2)
+ggplot()
+
+#The data had strangely high ages that were likely errors; this corrects them
+removed_strange_ages<-allData[allData$age <110,]
+
+#finding entries with a marker present; removes any entries that weren't infected
+sick<-subset(removed_strange_ages, marker01!=0 | marker02!=0 |marker03!=0 |marker04!=0 |marker05!=0 |marker06!=0 |marker07!=0 |marker08!=0 | marker09!=0 |marker10!=0)
+
+#ggplotting cases as a function of time
+#sub-setting and adjusting data
+Xdata<- sick[sick$country=="X",]
+Ydata<-sick[sick$country=="Y",]
+
+Yno<-seq_along(Ydata$dayofYear)
+Xno<-seq_along(Xdata$country)
+sick$Casenum<-append(Xno,Yno, after = length(Xno))
+
+#Plot Cumulative cases in each country against time
+ggplot(data = sick,
+       aes(x=sick$dayofYear, y=sick$Casenum, colour = country))+
+  geom_smooth()+
+  xlab("Day of the Year") +
+  ylab("Running Case Total")+
+  theme_classic()
+
+#Plot new cases against time
+ggplot(data = sick,
+       aes(x=dayofYear))+
+  geom_bar(position="dodge", aes( fill = country), show.legend = FALSE)+
+  xlab("Day of the Year") +
+  ylab("New Cases Per Day")+
+  facet_grid(.~country)+
+  theme_classic()
+
+#Marker comparison by country
+#subset data
+XmarkerData<-data.frame( MarkerNum = colnames(Xdata)[3:12],
+                         MarkerSum = colSums(Xdata[,3:12]),
+                         Country = Xdata[1:10,13])
+rownames(XmarkerData)<-1:nrow(XmarkerData)
+YmarkerData<-data.frame( MarkerNum = colnames(Ydata)[3:12],
+                         MarkerSum = colSums(Ydata[,3:12]),
+                         Country = Ydata[1:10,13])
+rownames(YmarkerData)<-1:nrow(YmarkerData)
+MarkerforPlot<- rbind(XmarkerData,YmarkerData)
+#plot
+ggplot(data = MarkerforPlot,
+       aes(x=Country, y=MarkerSum, fill=MarkerNum))+
+  geom_bar(stat="identity", position="dodge")+
+  xlab("Country") +
+  ylab("Marker Abundance")+
+  theme_minimal()