inst/webtool/server.R
In jcheng5/pqantimalarials: web tool for estimating under-five deaths caused by poor-quality antimalarials in sub-Saharan Africa
### server.R
#library(RColorBrewer)
#library(reshape2)
library(shiny)
source("miscFunctions.R")
names <- privateSales$Country
### Read in WHO death data
WHOdeaths <- u5deathsWHO2012
WHOmalaria <- u5malariadeathsWHO2010
# get colors
palette <- RColorBrewer::brewer.pal(9, "Blues")
#print("I Am HERE FIRST")
# drop row with totals
privateSales <- privateSales[-length(privateSales$Country), ]
# rename columns
colnames(privateSales) <- c("Country", "pfPositiveSales", "StdDev")
numCountries <- length(privateSales$Country)
# create cohen data table to be printed on website
displaySales <- privateSales
colnames(displaySales) <- c("Country", "Mean", "Standard_Deviation")
displaySales$Mean <- as.integer(displaySales$Mean)
displaySales$Standard_Deviation <- as.integer(displaySales$Standard_Deviation)
## global random 6 digit number that is generated for each set of input parameters
inputID <- 100000
### Define server logic for slider examples ###
shinyServer(function(input, output) {
#print("I am here Second")
generateLHS <- reactive(
{
inputID <- runif(1, 0, 999999)
inputID <- as.integer(inputID)
#print(paste(inputID, "sim1"))
#print(inputID)
sameFlag <- FALSE
countrySpec <- input$countrySpecific
numReps <- input$N
minCFR <- input$CFR[1]
maxCFR <- input$CFR[2]
if(minCFR == maxCFR) sameFlag <- TRUE
if(countrySpec){
# make dependent on Update button
input$goButton
minPrev <- isolate(c(input$Prev1[1],input$Prev2[1],input$Prev3[1],input$Prev4[1],input$Prev5[1],input$Prev6[1],input$Prev7[1],input$Prev8[1],input$Prev9[1],input$Prev10[1],input$Prev11[1],input$Prev12[1],input$Prev13[1],input$Prev14[1],input$Prev15[1],input$Prev16[1],input$Prev17[1],input$Prev18[1],input$Prev19[1],input$Prev20[1],input$Prev21[1],input$Prev22[1],input$Prev23[1],input$Prev24[1],input$Prev25[1],input$Prev26[1],input$Prev27[1],input$Prev28[1],input$Prev29[1],input$Prev30[1],input$Prev31[1],input$Prev32[1],input$Prev33[1],input$Prev34[1],input$Prev35[1],input$Prev36[1],input$Prev37[1],input$Prev38[1],input$Prev39[1]))
maxPrev <- isolate(c(input$Prev1[2],input$Prev2[2],input$Prev3[2],input$Prev4[2],input$Prev5[2],input$Prev6[2],input$Prev7[2],input$Prev8[2],input$Prev9[2],input$Prev10[2],input$Prev11[2],input$Prev12[2],input$Prev13[2],input$Prev14[2],input$Prev15[2],input$Prev16[2],input$Prev17[2],input$Prev18[2],input$Prev19[2],input$Prev20[2],input$Prev21[2],input$Prev22[2],input$Prev23[2],input$Prev24[2],input$Prev25[2],input$Prev26[2],input$Prev27[2],input$Prev28[2],input$Prev29[2],input$Prev30[2],input$Prev31[2],input$Prev32[2],input$Prev33[2],input$Prev34[2],input$Prev35[2],input$Prev36[2],input$Prev37[2],input$Prev38[2],input$Prev39[2]))
for(i in 1:39){
if(minPrev[i] == maxPrev[i]) sameFlag <- TRUE
}
}
else{
minPrev <- input$Prev[1]
maxPrev <- input$Prev[2]
if(minPrev == maxPrev) sameFlag <- TRUE
}
## Get sample vector for each variable
deathRate = getUniformLHS(numReps, minCFR, maxCFR) # 1 in 1000 - 5 in 1000
sales = matrix(NA, nrow = numReps, ncol = numCountries)
fakePercent = matrix(NA, nrow = numReps, ncol = numCountries)
if(countrySpec){
for (i in 1:numCountries) {
sales[, i] = getNormalLHS(numReps, privateSales$pfPositiveSales[i], privateSales$StdDev[i])
fakePercent[, i] = getUniformLHS(numReps, minPrev[i], maxPrev[i])
}
} else {
for (i in 1:numCountries) {
sales[, i] = getNormalLHS(numReps, privateSales$pfPositiveSales[i], privateSales$StdDev[i])
fakePercent[, i] = getUniformLHS(numReps, minPrev, maxPrev)
}
}
calculations <- matrix(NA, nrow = numReps, ncol = (numCountries + 1))
## Run simulations
for (i in 1:numReps) {
calculations[i, ] = model(sales[i, ], fakePercent[i, ], deathRate[i])
for (j in 1:40) {
if (calculations[i, j] < 0.5) {
calculations[i, j] = 0
}
}
}
#calculations
calculations <- data.frame(calculations)
colnames(calculations) <- names
#print(colnames(calculations))
calculations <- list(calculations, deathRate, sales, fakePercent,inputID, sameFlag)
calculations
}
)
generateSummary <- reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
#results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <- reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
meltedSummary <- meltedSummary[order(meltedSummary$value, decreasing = TRUE),]
#print(colnames(meltedSummary))
#print(meltedSummary)
meltedSummary <- meltedSummary[,c(1,3,4,5,6,7,8,9)]
meltedSummary <- meltedSummary[,c(1,5,6,8,2,7,4,3)]
meltedSummary[,2] <- as.integer(meltedSummary[,2])
meltedSummary[,3] <- as.integer(meltedSummary[,3])
meltedSummary[,4] <- as.integer(meltedSummary[,4])
meltedSummary[,5] <- as.integer(meltedSummary[,5])
meltedSummary[,6] <- as.integer(meltedSummary[,6])
meltedSummary[,7] <- as.integer(meltedSummary[,7])
meltedSummary[,8] <- as.integer(meltedSummary[,8])
colnames(meltedSummary) <- c("Country", "Min", "First Quartile", "Median","Mean", "Third Quartile", "Max", "Std Dev")
rownames(meltedSummary) <- NULL
meltedSummary$Country <- paste(meltedSummary$Country)
#print(class(meltedSummary$Country))
for (i in 1:length(meltedSummary$Country)){
if (meltedSummary$Country[i] == "Cote d'Ivoire"){
meltedSummary$Country[i] <- "Coté d'Ivoire"
}
}
#write.csv(meltedSummary$Country,"testNames.csv")
#test()
meltedSummary
}
)
# generate an HTML table view of the summary data
output$summaryTable <- renderTable(
{
data.frame(generateSummary())
}
)
# function to allow for .csv download of summary stats
output$downloadSummary <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_SummaryStats.csv', sep='')
}
,content = function(file){
out <- generateSummary()
for (i in 1:length(out[,1])){
if(out[i,1] == "Coté d'Ivoire") out[i,1] <- "Cote d'Ivoire"
}
write.csv(out, file)
}
)
plotRaw <- reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <- reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by estimated deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$median),]
if(medianGraphs2$variable[39] != 'Nigeria') {}
# get max without Nigeria
#ymax = max(medianGraphs2$q3[1:38])
# get max
ymax = max(medianGraphs2$q3[1:39])
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$median[1:39], xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Estimated Under-Five Deaths Per Year", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1[1:39], plot, medianGraphs2$q3[1:39], angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.04*ymax), medianGraphs2$variable[1:39], adj = c(0,0), cex = .95)
}
)
plotRaw2 = function(){
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by estimated deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$median),]
if(medianGraphs2$variable[39] != 'Nigeria') {}
# get max without Nigeria
#ymax = max(medianGraphs2$q3[1:38])
# get max
ymax = max(medianGraphs2$q3[1:39])
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$median[1:39], xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Estimated Under-Five Deaths Per Year", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1[1:39], plot, medianGraphs2$q3[1:39], angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.02*ymax), medianGraphs2$variable[1:39], adj = c(0,0), cex = .85)
}
output$raw <- renderPlot(
{
plotRaw()
}
)
# handler for downloading median Estimates as pdf
output$downloadMedianEstimates <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
paste(inputID,'_MedianDeathEstimates.pdf', sep='')
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotRaw2()
dev.off()
}
,contentType = 'application/pdf'
)
plotMalariaProp <- reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by malaria prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$malariaProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$malariaDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$malariaProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five Malaria Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five Malaria Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$malariaDeaths, plot, medianGraphs2$q3/medianGraphs2$malariaDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.04*ymax), medianGraphs2$variable, adj = c(0,0), cex = .95)
}
)
plotMalariaProp2 = function(){
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by malaria prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$malariaProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$malariaDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$malariaProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five Malaria Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five Malaria Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$malariaDeaths, plot, medianGraphs2$q3/medianGraphs2$malariaDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.02*ymax), medianGraphs2$variable, adj = c(0,0), cex = .85)
}
output$malariaProp <- renderPlot(
{
plotMalariaProp()
}
)
# handler for downloading malariaProp as pdf
output$downloadMalariaProp <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_ProportionOfTotalMalariaDeaths.pdf', sep='')
#name <- "HistogramTest.png"
#name
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotMalariaProp2()
#plot(1,1)
#hist(runif(1000, 1, 10))
dev.off()
}
,contentType = 'application/pdf'
)
plotDeathProp <-reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by death prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$deathProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$whoDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$deathProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five All-Cause Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five All-Cause Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$whoDeaths, plot, medianGraphs2$q3/medianGraphs2$whoDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.04*ymax), medianGraphs2$variable, adj = c(0,0), cex = .95)
}
)
plotDeathProp2 = function(){
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by death prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$deathProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$whoDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$deathProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five All-Cause Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five All-Cause Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$whoDeaths, plot, medianGraphs2$q3/medianGraphs2$whoDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.02*ymax), medianGraphs2$variable, adj = c(0,0), cex = .85)
}
output$deathProp <- renderPlot(
{
plotDeathProp()
}
)
# handler for downloading allCauseDeathProp as pdf
output$downloadDeathProp <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
paste(inputID,'_ProportionOfAllCauseDeaths.pdf', sep='')
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotDeathProp2()
#plot(1,1)
#hist(runif(1000, 1, 10))
dev.off()
}
,contentType = 'application/pdf'
)
plotRequestedHist <-reactive(
{
country <- input$histogram1
country2 <- country
#print(country)
if (country2 == "Equatorial Guinea") country2 <- "Equatorial.Guinea"
if (country2 == "Guinea-Bissau") country2 <- "Guinea.Bissau"
if (country2 == "Central African Republic") country2 <- "Central.African.Republic"
if (country2 == "Sierra Leone") country2 <- "Sierra.Leone"
if (country2 == "Burkina Faso") country2 <- "Burkina.Faso"
if (country2 == "Coté d'Ivoire") country2 <- "Cote.d.Ivoire"
if (country2 == "Democratic Republic of the Congo") country2 <- "Democratic.Republic.of.the.Congo"
if (country2 == "All Countries") country2 <- "All.Countries"
#test <- rnorm(1000, 50, 10)
#hist(test)
boxplotOutput <- generateLHS()
boxplotOutput <- boxplotOutput[1]
boxplotOutput <- data.frame(boxplotOutput)
#print(colnames(boxplotOutput))
LHSsize <- input$N
#print("LHSsize = ")
#print(LHSsize)
for(i in 1:length(colnames(boxplotOutput))){
boxplotOutput[(LHSsize + 1), i] <- median(boxplotOutput[1:LHSsize, i])
}
# order by mean
boxplotOutput <- boxplotOutput[,order(boxplotOutput[(LHSsize + 1),])]
# remove mean
boxplotOutput <- boxplotOutput[1:LHSsize,]
# print(colnames(boxplotOutput))
#print(colnames(boxplotOutput))
firstBox <- boxplotOutput[,country2]
#firstBox <- as.matrix(firstBox)
#print(str(firstBox))
#print("Median:")
#print(median(boxplotOutput[,1]))
#print(firstBox)
maxy <- max(firstBox)
plot <- hist(firstBox, main = country, xlab = "Estimated Deaths Per Year")
maxCounts <- max(plot$counts)
#plot
plot2 <- {
hist(firstBox, main = country, xlab = "Estimated Deaths Per Year")
# add red line at the median
abline(v = median(boxplotOutput[,country2]), lwd = 4, col = "red")
# add a blue line at the mean
abline(v = mean(boxplotOutput[,country2]), lwd = 4, col = "blue", lty = 2)
legend(.85*maxy, maxCounts,c("median","mean"), lty = c(1,2), lwd = c(3,3), seg.len = 3, col = c("red","blue"))
}
}
)
plotRequestedHist2 = function() {
country <- input$histogram1
country2 <- country
#print(country)
if (country2 == "Equatorial Guinea") country2 <- "Equatorial.Guinea"
if (country2 == "Guinea-Bissau") country2 <- "Guinea.Bissau"
if (country2 == "Central African Republic") country2 <- "Central.African.Republic"
if (country2 == "Sierra Leone") country2 <- "Sierra.Leone"
if (country2 == "Burkina Faso") country2 <- "Burkina.Faso"
if (country2 == "Coté d'Ivoire") country2 <- "Cote.d.Ivoire"
if (country2 == "Democratic Republic of the Congo") country2 <- "Democratic.Republic.of.the.Congo"
if (country2 == "All Countries") country2 <- "All.Countries"
#test <- rnorm(1000, 50, 10)
#hist(test)
boxplotOutput <- generateLHS()
boxplotOutput <- boxplotOutput[1]
boxplotOutput <- data.frame(boxplotOutput)
#print(colnames(boxplotOutput))
LHSsize <- input$N
#print("LHSsize = ")
#print(LHSsize)
for(i in 1:length(colnames(boxplotOutput))){
boxplotOutput[(LHSsize + 1), i] <- median(boxplotOutput[1:LHSsize, i])
}
# order by mean
boxplotOutput <- boxplotOutput[,order(boxplotOutput[(LHSsize + 1),])]
# remove mean
boxplotOutput <- boxplotOutput[1:LHSsize,]
# print(colnames(boxplotOutput))
#print(colnames(boxplotOutput))
firstBox <- boxplotOutput[,country2]
#firstBox <- as.matrix(firstBox)
#print(str(firstBox))
#print("Median:")
#print(median(boxplotOutput[,1]))
#print(firstBox)
maxy <- max(firstBox)
plot <- hist(firstBox, main = country, xlab = "Estimated Deaths Per Year")
maxCounts <- max(plot$counts)
#plot
# add red line at the median
abline(v = median(boxplotOutput[,country2]), lwd = 4, col = "red")
# add a blue line at the mean
abline(v = mean(boxplotOutput[,country2]), lwd = 4, col = "blue", lty = 2)
legend(.85*maxy, maxCounts,c("median","mean"), lty = c(1,2), lwd = c(3,3), seg.len = 3, col = c("red","blue"))
}
# handler for downloaded requested Histogram as pdf
output$downloadHist <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_Histogram_',input$histogram1,'.pdf', sep='')
#name <- "HistogramTest.png"
#name
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotRequestedHist2()
#plot(1,1)
#hist(runif(1000, 1, 10))
dev.off()
}
,contentType = 'application/pdf'
)
# test = function(){
# nameTEST <- c("HistogramTest1.png")
# #print(nameTEST)
# png(nameTEST)
# plotRequestedHist()
# dev.off()
# #print("finished")
# }
# return the requested histogram
output$requestedHist <- renderPlot(
{
plotRequestedHist()
}
)
generatePRCC <- reactive(
{
out <- generateLHS()
sameFlag <- out[6]
#print(sameFlag)
if(sameFlag == TRUE){
stop("Partial Rank Correlation Coefficients cannot be calculated when at least one of the input parameters does not have a range (i.e. the min and max slider for that input parameter are set to the same value).")
}
results <- out[1]
results <- data.frame(results)
deathRate <- out[2]
deathRate <- data.frame(deathRate)
sales <- out[3]
sales <- data.frame(sales)
fakePercent <-out[4]
fakePercent <- data.frame(fakePercent)
totalDeaths = results[,40]
names2 <- names[-40]
names2 <- paste(names2)
#print(class(names2))
# shorten long country names so that they appear on the graph
for (i in 1:length(names2)){
if (names2[i] == "Central African Republic") {
names2[i] <- "CAR"
}
if (names2[i] == "Democratic Republic of the Congo") {
names2[i] <- "DRC"
}
if (names2[i] == "Cote d'Ivoire"){
names2[i] <- "Coté d'Ivoire"
}
}
salesLabel <- replicate(39, ": Antimalarial Sales")
salesLabel <- paste(paste(names2), salesLabel, sep = "")
fakeLabel<- replicate(39, ": Prevalence of PQ Antimalarials")
fakeLabel <- paste(paste(names2), fakeLabel, sep = "")
x.base <- data.frame(cbind(sales, fakePercent, deathRate))
x = data.frame(x.base, totalDeaths)
colnames(x) <- c(salesLabel, fakeLabel, "Case Fatality Rate", "TotalDeathsOutput")
totDeathSens <- counterfeitPRCC(x, sort.results = TRUE, sort.abs = TRUE)
totDeathSens <- totDeathSens[-2]
totDeathSens <- data.frame(rownames(totDeathSens), totDeathSens[,1], totDeathSens[,2], stringsAsFactors = FALSE)
colnames(totDeathSens) <- c("Input Parameter","PRCC", "P-value")
totDeathSens <- totDeathSens[order(totDeathSens$PRCC, decreasing = TRUE),]
rownames(totDeathSens) <- NULL
#print(class(totDeathSens$PRCC))
#print(totDeathSens)
# return PRCC table
totDeathSens
}
)
# generate an HTML table view of the PRCCs
output$PRCC <- renderTable(
{
data.frame(generatePRCC())
}
)
# function to allow for .csv download of PRCCs
output$downloadPRCC <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_SensitivityAnalysis.csv', sep='')
}
,content = function(file){
out <- generatePRCC()
#print(class(out[,1]))
for(i in 1:length(out[,1])){
if(out[i,1] == "Coté d'Ivoire: Antimalarial Sales"){
out[i,1] <- "Cote d'Ivoire: Antimalarial Sales"
}
if(out[i,1] == "Coté d'Ivoire: Prevalence of PQ Antimalarials"){
out[i,1] <- "Cote d'Ivoire: Prevalence of PQ Antimalarials"
}
}
write.csv(out, file)
}
)
generateInputs <-reactive({
countrySpec <- input$countrySpecific
names2 <- names[-40]
names2 <- paste(names2)
#print(class(names2))
# shorten long country names so that they appear on the graph
for (i in 1:length(names2)){
if (names2[i] == "Central African Republic") {
names2[i] <- "CAR"
}
if (names2[i] == "Democratic Republic of the Congo") {
names2[i] <- "DRC"
}
if (names2[i] == "Cote d'Ivoire"){
names2[i] <- "Coté d'Ivoire"
}
}
displaySales <- data.frame(displaySales)
rownames(displaySales) <- NULL
#
salesLabel <- replicate(39, ": Antimalarial Sales")
salesLabel <- paste(paste(names2), salesLabel, sep = "")
salesMin <- replicate(39, "NA")
salesMax <- replicate(39, "NA")
salesDist <- replicate(39, "Normal")
#
fakeLabel<- replicate(39, ": Prevalence of PQ Antimalarials")
fakeLabel <- paste(paste(names2), fakeLabel, sep = "")
fakeDist <- replicate(39, "Uniform")
salesMean <- replicate(39, 0)
salesStd <- replicate(39,0)
fakeMean <- replicate(39,0)
fakeStd <- replicate(39,0)
if(countrySpec) {
# make dependent on Update button
input$goButton
fakeMin <- isolate(c(input$Prev1[1],input$Prev2[1],input$Prev3[1],input$Prev4[1],input$Prev5[1],input$Prev6[1],input$Prev7[1],input$Prev8[1],input$Prev9[1],input$Prev10[1],input$Prev11[1],input$Prev12[1],input$Prev13[1],input$Prev14[1],input$Prev15[1],input$Prev16[1],input$Prev17[1],input$Prev18[1],input$Prev19[1],input$Prev20[1],input$Prev21[1],input$Prev22[1],input$Prev23[1],input$Prev24[1],input$Prev25[1],input$Prev26[1],input$Prev27[1],input$Prev28[1],input$Prev29[1],input$Prev30[1],input$Prev31[1],input$Prev32[1],input$Prev33[1],input$Prev34[1],input$Prev35[1],input$Prev36[1],input$Prev37[1],input$Prev38[1],input$Prev39[1]))
fakeMax <- isolate(c(input$Prev1[2],input$Prev2[2],input$Prev3[2],input$Prev4[2],input$Prev5[2],input$Prev6[2],input$Prev7[2],input$Prev8[2],input$Prev9[2],input$Prev10[2],input$Prev11[2],input$Prev12[2],input$Prev13[2],input$Prev14[2],input$Prev15[2],input$Prev16[2],input$Prev17[2],input$Prev18[2],input$Prev19[2],input$Prev20[2],input$Prev21[2],input$Prev22[2],input$Prev23[2],input$Prev24[2],input$Prev25[2],input$Prev26[2],input$Prev27[2],input$Prev28[2],input$Prev29[2],input$Prev30[2],input$Prev31[2],input$Prev32[2],input$Prev33[2],input$Prev34[2],input$Prev35[2],input$Prev36[2],input$Prev37[2],input$Prev38[2],input$Prev39[2]))
} else{
fakeMin <- replicate(39,input$Prev[1])
fakeMax <- replicate(39,input$Prev[2])
}
for(i in 1:39){
# Sales Inputs
salesMean[i] <- displaySales$Mean[i]
salesStd[i] <- displaySales$Standard_Deviation[i]
# Prevalence Inputs
fakeMean[i] <- (fakeMax[i]+fakeMin[i])/2
fakeStd[i] <- stdUniform(fakeMin[i],fakeMax[i])
}
# create and sort sales inputs by name
cols <- c("InputParameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
salesINPUTS <- data.frame(salesLabel, salesMin, salesMean, salesMax, salesStd, salesDist, stringsAsFactors = FALSE)
colnames(salesINPUTS) <- cols
#print(salesINPUTS)
salesINPUTS <- salesINPUTS[order(salesINPUTS$InputParameter),]
cols <- c("Input Parameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
colnames(salesINPUTS) <- cols
# create and sort PQ prevalence inputs by name
cols <- c("InputParameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
fakeINPUTS <- data.frame(fakeLabel, fakeMin, fakeMean, fakeMax, fakeStd, fakeDist, stringsAsFactors = FALSE)
colnames(fakeINPUTS) <- cols
#print(fakeINPUTS)
fakeINPUTS <- fakeINPUTS[order(fakeINPUTS$InputParameter),]
cols <- c("Input Parameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
colnames(fakeINPUTS) <- cols
#case fatality rate inputs
cfrINPUTS <- list("Case Fatality Rate", input$CFR[1], (input$CFR[2]+input$CFR[1])/2,
input$CFR[2], stdUniform(input$CFR[1],input$CFR[2]), "Uniform")
masterINPUTS <- rbind(cfrINPUTS, fakeINPUTS, salesINPUTS)
masterINPUTS$Min <- as.character(masterINPUTS$Min)
masterINPUTS$Mean <- as.character(masterINPUTS$Mean)
masterINPUTS$Max <- as.character(masterINPUTS$Max)
masterINPUTS$StdDev <- as.character(masterINPUTS$StdDev)
masterINPUTS
}
)
output$InputParameters <- renderTable({
inputs <- data.frame(generateInputs())
rownames(inputs) <- NULL
inputs
})
output$downloadInputs <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_InputParamters.csv', sep='')
}
,content = function(file){
out <- generateInputs()
for(i in 1:length(out[,1])){
if(out[i,1] == "Coté d'Ivoire: Antimalarial Sales") out[i,1] <- "Cote d'Ivoire: Antimalarial Sales"
if(out[i,1] == "Coté d'Ivoire: Prevalence of PQ Antimalarials") out[i,1] <- "Cote d'Ivoire: Prevalence of PQ Antimalarials"
}
write.csv(out, file)
}
)
# generate an HTML Table view of the Cohen Antimalarial Sales
# input data
output$sales <- renderTable(
{
#print("I AM HERE Third")
data.frame(displaySales)
displaySales <- displaySales[order(displaySales$Mean, decreasing = TRUE),]
rownames(displaySales) <- NULL
displaySales
}
)
}
)
jcheng5/pqantimalarials documentation built on May 18, 2019, 10:22 p.m.
R Package Documentation
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### server.R
#library(RColorBrewer)
#library(reshape2)
library(shiny)
source("miscFunctions.R")
names <- privateSales$Country
### Read in WHO death data
WHOdeaths <- u5deathsWHO2012
WHOmalaria <- u5malariadeathsWHO2010
# get colors
palette <- RColorBrewer::brewer.pal(9, "Blues")
#print("I Am HERE FIRST")
# drop row with totals
privateSales <- privateSales[-length(privateSales$Country), ]
# rename columns
colnames(privateSales) <- c("Country", "pfPositiveSales", "StdDev")
numCountries <- length(privateSales$Country)
# create cohen data table to be printed on website
displaySales <- privateSales
colnames(displaySales) <- c("Country", "Mean", "Standard_Deviation")
displaySales$Mean <- as.integer(displaySales$Mean)
displaySales$Standard_Deviation <- as.integer(displaySales$Standard_Deviation)
## global random 6 digit number that is generated for each set of input parameters
inputID <- 100000
### Define server logic for slider examples ###
shinyServer(function(input, output) {
#print("I am here Second")
generateLHS <- reactive(
{
inputID <- runif(1, 0, 999999)
inputID <- as.integer(inputID)
#print(paste(inputID, "sim1"))
#print(inputID)
sameFlag <- FALSE
countrySpec <- input$countrySpecific
numReps <- input$N
minCFR <- input$CFR[1]
maxCFR <- input$CFR[2]
if(minCFR == maxCFR) sameFlag <- TRUE
if(countrySpec){
# make dependent on Update button
input$goButton
minPrev <- isolate(c(input$Prev1[1],input$Prev2[1],input$Prev3[1],input$Prev4[1],input$Prev5[1],input$Prev6[1],input$Prev7[1],input$Prev8[1],input$Prev9[1],input$Prev10[1],input$Prev11[1],input$Prev12[1],input$Prev13[1],input$Prev14[1],input$Prev15[1],input$Prev16[1],input$Prev17[1],input$Prev18[1],input$Prev19[1],input$Prev20[1],input$Prev21[1],input$Prev22[1],input$Prev23[1],input$Prev24[1],input$Prev25[1],input$Prev26[1],input$Prev27[1],input$Prev28[1],input$Prev29[1],input$Prev30[1],input$Prev31[1],input$Prev32[1],input$Prev33[1],input$Prev34[1],input$Prev35[1],input$Prev36[1],input$Prev37[1],input$Prev38[1],input$Prev39[1]))
maxPrev <- isolate(c(input$Prev1[2],input$Prev2[2],input$Prev3[2],input$Prev4[2],input$Prev5[2],input$Prev6[2],input$Prev7[2],input$Prev8[2],input$Prev9[2],input$Prev10[2],input$Prev11[2],input$Prev12[2],input$Prev13[2],input$Prev14[2],input$Prev15[2],input$Prev16[2],input$Prev17[2],input$Prev18[2],input$Prev19[2],input$Prev20[2],input$Prev21[2],input$Prev22[2],input$Prev23[2],input$Prev24[2],input$Prev25[2],input$Prev26[2],input$Prev27[2],input$Prev28[2],input$Prev29[2],input$Prev30[2],input$Prev31[2],input$Prev32[2],input$Prev33[2],input$Prev34[2],input$Prev35[2],input$Prev36[2],input$Prev37[2],input$Prev38[2],input$Prev39[2]))
for(i in 1:39){
if(minPrev[i] == maxPrev[i]) sameFlag <- TRUE
}
}
else{
minPrev <- input$Prev[1]
maxPrev <- input$Prev[2]
if(minPrev == maxPrev) sameFlag <- TRUE
}
## Get sample vector for each variable
deathRate = getUniformLHS(numReps, minCFR, maxCFR) # 1 in 1000 - 5 in 1000
sales = matrix(NA, nrow = numReps, ncol = numCountries)
fakePercent = matrix(NA, nrow = numReps, ncol = numCountries)
if(countrySpec){
for (i in 1:numCountries) {
sales[, i] = getNormalLHS(numReps, privateSales$pfPositiveSales[i], privateSales$StdDev[i])
fakePercent[, i] = getUniformLHS(numReps, minPrev[i], maxPrev[i])
}
} else {
for (i in 1:numCountries) {
sales[, i] = getNormalLHS(numReps, privateSales$pfPositiveSales[i], privateSales$StdDev[i])
fakePercent[, i] = getUniformLHS(numReps, minPrev, maxPrev)
}
}
calculations <- matrix(NA, nrow = numReps, ncol = (numCountries + 1))
## Run simulations
for (i in 1:numReps) {
calculations[i, ] = model(sales[i, ], fakePercent[i, ], deathRate[i])
for (j in 1:40) {
if (calculations[i, j] < 0.5) {
calculations[i, j] = 0
}
}
}
#calculations
calculations <- data.frame(calculations)
colnames(calculations) <- names
#print(colnames(calculations))
calculations <- list(calculations, deathRate, sales, fakePercent,inputID, sameFlag)
calculations
}
)
generateSummary <- reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
#results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <- reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
meltedSummary <- meltedSummary[order(meltedSummary$value, decreasing = TRUE),]
#print(colnames(meltedSummary))
#print(meltedSummary)
meltedSummary <- meltedSummary[,c(1,3,4,5,6,7,8,9)]
meltedSummary <- meltedSummary[,c(1,5,6,8,2,7,4,3)]
meltedSummary[,2] <- as.integer(meltedSummary[,2])
meltedSummary[,3] <- as.integer(meltedSummary[,3])
meltedSummary[,4] <- as.integer(meltedSummary[,4])
meltedSummary[,5] <- as.integer(meltedSummary[,5])
meltedSummary[,6] <- as.integer(meltedSummary[,6])
meltedSummary[,7] <- as.integer(meltedSummary[,7])
meltedSummary[,8] <- as.integer(meltedSummary[,8])
colnames(meltedSummary) <- c("Country", "Min", "First Quartile", "Median","Mean", "Third Quartile", "Max", "Std Dev")
rownames(meltedSummary) <- NULL
meltedSummary$Country <- paste(meltedSummary$Country)
#print(class(meltedSummary$Country))
for (i in 1:length(meltedSummary$Country)){
if (meltedSummary$Country[i] == "Cote d'Ivoire"){
meltedSummary$Country[i] <- "Coté d'Ivoire"
}
}
#write.csv(meltedSummary$Country,"testNames.csv")
#test()
meltedSummary
}
)
# generate an HTML table view of the summary data
output$summaryTable <- renderTable(
{
data.frame(generateSummary())
}
)
# function to allow for .csv download of summary stats
output$downloadSummary <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_SummaryStats.csv', sep='')
}
,content = function(file){
out <- generateSummary()
for (i in 1:length(out[,1])){
if(out[i,1] == "Coté d'Ivoire") out[i,1] <- "Cote d'Ivoire"
}
write.csv(out, file)
}
)
plotRaw <- reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <- reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by estimated deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$median),]
if(medianGraphs2$variable[39] != 'Nigeria') {}
# get max without Nigeria
#ymax = max(medianGraphs2$q3[1:38])
# get max
ymax = max(medianGraphs2$q3[1:39])
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$median[1:39], xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Estimated Under-Five Deaths Per Year", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1[1:39], plot, medianGraphs2$q3[1:39], angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.04*ymax), medianGraphs2$variable[1:39], adj = c(0,0), cex = .95)
}
)
plotRaw2 = function(){
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by estimated deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$median),]
if(medianGraphs2$variable[39] != 'Nigeria') {}
# get max without Nigeria
#ymax = max(medianGraphs2$q3[1:38])
# get max
ymax = max(medianGraphs2$q3[1:39])
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$median[1:39], xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Estimated Under-Five Deaths Per Year", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1[1:39], plot, medianGraphs2$q3[1:39], angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.02*ymax), medianGraphs2$variable[1:39], adj = c(0,0), cex = .85)
}
output$raw <- renderPlot(
{
plotRaw()
}
)
# handler for downloading median Estimates as pdf
output$downloadMedianEstimates <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
paste(inputID,'_MedianDeathEstimates.pdf', sep='')
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotRaw2()
dev.off()
}
,contentType = 'application/pdf'
)
plotMalariaProp <- reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by malaria prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$malariaProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$malariaDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$malariaProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five Malaria Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five Malaria Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$malariaDeaths, plot, medianGraphs2$q3/medianGraphs2$malariaDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.04*ymax), medianGraphs2$variable, adj = c(0,0), cex = .95)
}
)
plotMalariaProp2 = function(){
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by malaria prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$malariaProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$malariaDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$malariaProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five Malaria Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five Malaria Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$malariaDeaths, plot, medianGraphs2$q3/medianGraphs2$malariaDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.02*ymax), medianGraphs2$variable, adj = c(0,0), cex = .85)
}
output$malariaProp <- renderPlot(
{
plotMalariaProp()
}
)
# handler for downloading malariaProp as pdf
output$downloadMalariaProp <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_ProportionOfTotalMalariaDeaths.pdf', sep='')
#name <- "HistogramTest.png"
#name
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotMalariaProp2()
#plot(1,1)
#hist(runif(1000, 1, 10))
dev.off()
}
,contentType = 'application/pdf'
)
plotDeathProp <-reactive(
{
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by death prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$deathProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$whoDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$deathProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five All-Cause Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five All-Cause Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$whoDeaths, plot, medianGraphs2$q3/medianGraphs2$whoDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.04*ymax), medianGraphs2$variable, adj = c(0,0), cex = .95)
}
)
plotDeathProp2 = function(){
numReps <- input$N
results <- generateLHS()
results <- results[1]; results <- data.frame(results)
totalDeaths <- results[,40]
results[, 41] = 1:numReps
colnames(results) = c(paste(privateSales$Country), "All Countries", "SimulationNumber")
# reformat data from wide to long format
meltedOutput <-reshape2::melt(results, id.vars = c("SimulationNumber"))
# use summary function to generate summary
meltedSummary <- summarySE(meltedOutput, measurevar = "value", groupvars = c("variable"))
medianGraphs <- meltedSummary
medianGraphs2 <- medianGraphs[1:39,]
# turn names into text
medianGraphs2$variable <- paste(medianGraphs2$variable)
# order meltedSummary 2 by country name and load in <5 Deaths
medianGraphs2 <- medianGraphs2[order(medianGraphs2$variable),]
medianGraphs2["whoDeaths"] <- WHOdeaths[,3]
medianGraphs2["whoDeathRate"] <- WHOdeaths[,4]
# load in <5 malaria deaths
WHOmalaria <- WHOmalaria[WHOmalaria$Country %in% medianGraphs2$variable,]
WHOmalaria <- WHOmalaria[order(WHOmalaria$Country),]
medianGraphs2["malariaDeaths"] <- WHOmalaria$Numeric
# calculation prop of deaths
medianGraphs2$whoDeaths <- medianGraphs2$whoDeaths * 1000
medianGraphs2["malariaProp"] <- medianGraphs2$median / medianGraphs2$malariaDeaths
medianGraphs2["deathProp"] <- medianGraphs2$median / medianGraphs2$whoDeaths
#print(medianGraphs2$variable)
# shorten long country names so that they appear on the graph
for (i in 1:length(colnames(medianGraphs2))){
if (medianGraphs2$variable[i] == "Central African Republic") {
medianGraphs2$variable[i] <- "CAR"
}
if (medianGraphs2$variable[i] == "Democratic Republic of the Congo") {
medianGraphs2$variable[i] <- "DRC"
}
if (medianGraphs2$variable[i] == "Cote d'Ivoire"){
medianGraphs2$variable[i] <- "Coté d'Ivoire"
}
}
#print(medianGraphs2$variable)
# sort by death prop
medianGraphs2 <- medianGraphs2[order(medianGraphs2$deathProp),]
ymax = max(medianGraphs2$q3/medianGraphs2$whoDeaths)
#print(ymax)
par(oma = c(3,1,0,0), srt = -40, xpd = NA)
plot <-barplot(medianGraphs2$deathProp, xlab = NULL, ylab = NULL, ylim = c(0,ymax*1.05), col = palette[7], las = 1, main = "Median Death Estimates as a Proportion\nof Total Under-Five All-Cause Deaths\n(error bars as Interquartile Range)")
mtext(side = 2, text = "Proportion of Under-Five All-Cause Deaths", outer = TRUE)
suppressWarnings(arrows(plot, medianGraphs2$q1/medianGraphs2$whoDeaths, plot, medianGraphs2$q3/medianGraphs2$whoDeaths, angle = 90, code = 3, length = 0.05))
text(x = as.vector(plot)-.3, y = -(.02*ymax), medianGraphs2$variable, adj = c(0,0), cex = .85)
}
output$deathProp <- renderPlot(
{
plotDeathProp()
}
)
# handler for downloading allCauseDeathProp as pdf
output$downloadDeathProp <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
paste(inputID,'_ProportionOfAllCauseDeaths.pdf', sep='')
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotDeathProp2()
#plot(1,1)
#hist(runif(1000, 1, 10))
dev.off()
}
,contentType = 'application/pdf'
)
plotRequestedHist <-reactive(
{
country <- input$histogram1
country2 <- country
#print(country)
if (country2 == "Equatorial Guinea") country2 <- "Equatorial.Guinea"
if (country2 == "Guinea-Bissau") country2 <- "Guinea.Bissau"
if (country2 == "Central African Republic") country2 <- "Central.African.Republic"
if (country2 == "Sierra Leone") country2 <- "Sierra.Leone"
if (country2 == "Burkina Faso") country2 <- "Burkina.Faso"
if (country2 == "Coté d'Ivoire") country2 <- "Cote.d.Ivoire"
if (country2 == "Democratic Republic of the Congo") country2 <- "Democratic.Republic.of.the.Congo"
if (country2 == "All Countries") country2 <- "All.Countries"
#test <- rnorm(1000, 50, 10)
#hist(test)
boxplotOutput <- generateLHS()
boxplotOutput <- boxplotOutput[1]
boxplotOutput <- data.frame(boxplotOutput)
#print(colnames(boxplotOutput))
LHSsize <- input$N
#print("LHSsize = ")
#print(LHSsize)
for(i in 1:length(colnames(boxplotOutput))){
boxplotOutput[(LHSsize + 1), i] <- median(boxplotOutput[1:LHSsize, i])
}
# order by mean
boxplotOutput <- boxplotOutput[,order(boxplotOutput[(LHSsize + 1),])]
# remove mean
boxplotOutput <- boxplotOutput[1:LHSsize,]
# print(colnames(boxplotOutput))
#print(colnames(boxplotOutput))
firstBox <- boxplotOutput[,country2]
#firstBox <- as.matrix(firstBox)
#print(str(firstBox))
#print("Median:")
#print(median(boxplotOutput[,1]))
#print(firstBox)
maxy <- max(firstBox)
plot <- hist(firstBox, main = country, xlab = "Estimated Deaths Per Year")
maxCounts <- max(plot$counts)
#plot
plot2 <- {
hist(firstBox, main = country, xlab = "Estimated Deaths Per Year")
# add red line at the median
abline(v = median(boxplotOutput[,country2]), lwd = 4, col = "red")
# add a blue line at the mean
abline(v = mean(boxplotOutput[,country2]), lwd = 4, col = "blue", lty = 2)
legend(.85*maxy, maxCounts,c("median","mean"), lty = c(1,2), lwd = c(3,3), seg.len = 3, col = c("red","blue"))
}
}
)
plotRequestedHist2 = function() {
country <- input$histogram1
country2 <- country
#print(country)
if (country2 == "Equatorial Guinea") country2 <- "Equatorial.Guinea"
if (country2 == "Guinea-Bissau") country2 <- "Guinea.Bissau"
if (country2 == "Central African Republic") country2 <- "Central.African.Republic"
if (country2 == "Sierra Leone") country2 <- "Sierra.Leone"
if (country2 == "Burkina Faso") country2 <- "Burkina.Faso"
if (country2 == "Coté d'Ivoire") country2 <- "Cote.d.Ivoire"
if (country2 == "Democratic Republic of the Congo") country2 <- "Democratic.Republic.of.the.Congo"
if (country2 == "All Countries") country2 <- "All.Countries"
#test <- rnorm(1000, 50, 10)
#hist(test)
boxplotOutput <- generateLHS()
boxplotOutput <- boxplotOutput[1]
boxplotOutput <- data.frame(boxplotOutput)
#print(colnames(boxplotOutput))
LHSsize <- input$N
#print("LHSsize = ")
#print(LHSsize)
for(i in 1:length(colnames(boxplotOutput))){
boxplotOutput[(LHSsize + 1), i] <- median(boxplotOutput[1:LHSsize, i])
}
# order by mean
boxplotOutput <- boxplotOutput[,order(boxplotOutput[(LHSsize + 1),])]
# remove mean
boxplotOutput <- boxplotOutput[1:LHSsize,]
# print(colnames(boxplotOutput))
#print(colnames(boxplotOutput))
firstBox <- boxplotOutput[,country2]
#firstBox <- as.matrix(firstBox)
#print(str(firstBox))
#print("Median:")
#print(median(boxplotOutput[,1]))
#print(firstBox)
maxy <- max(firstBox)
plot <- hist(firstBox, main = country, xlab = "Estimated Deaths Per Year")
maxCounts <- max(plot$counts)
#plot
# add red line at the median
abline(v = median(boxplotOutput[,country2]), lwd = 4, col = "red")
# add a blue line at the mean
abline(v = mean(boxplotOutput[,country2]), lwd = 4, col = "blue", lty = 2)
legend(.85*maxy, maxCounts,c("median","mean"), lty = c(1,2), lwd = c(3,3), seg.len = 3, col = c("red","blue"))
}
# handler for downloaded requested Histogram as pdf
output$downloadHist <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_Histogram_',input$histogram1,'.pdf', sep='')
#name <- "HistogramTest.png"
#name
}
,content = function(file)
{
pdf(file, width = 11, height = 8.5)
plotRequestedHist2()
#plot(1,1)
#hist(runif(1000, 1, 10))
dev.off()
}
,contentType = 'application/pdf'
)
# test = function(){
# nameTEST <- c("HistogramTest1.png")
# #print(nameTEST)
# png(nameTEST)
# plotRequestedHist()
# dev.off()
# #print("finished")
# }
# return the requested histogram
output$requestedHist <- renderPlot(
{
plotRequestedHist()
}
)
generatePRCC <- reactive(
{
out <- generateLHS()
sameFlag <- out[6]
#print(sameFlag)
if(sameFlag == TRUE){
stop("Partial Rank Correlation Coefficients cannot be calculated when at least one of the input parameters does not have a range (i.e. the min and max slider for that input parameter are set to the same value).")
}
results <- out[1]
results <- data.frame(results)
deathRate <- out[2]
deathRate <- data.frame(deathRate)
sales <- out[3]
sales <- data.frame(sales)
fakePercent <-out[4]
fakePercent <- data.frame(fakePercent)
totalDeaths = results[,40]
names2 <- names[-40]
names2 <- paste(names2)
#print(class(names2))
# shorten long country names so that they appear on the graph
for (i in 1:length(names2)){
if (names2[i] == "Central African Republic") {
names2[i] <- "CAR"
}
if (names2[i] == "Democratic Republic of the Congo") {
names2[i] <- "DRC"
}
if (names2[i] == "Cote d'Ivoire"){
names2[i] <- "Coté d'Ivoire"
}
}
salesLabel <- replicate(39, ": Antimalarial Sales")
salesLabel <- paste(paste(names2), salesLabel, sep = "")
fakeLabel<- replicate(39, ": Prevalence of PQ Antimalarials")
fakeLabel <- paste(paste(names2), fakeLabel, sep = "")
x.base <- data.frame(cbind(sales, fakePercent, deathRate))
x = data.frame(x.base, totalDeaths)
colnames(x) <- c(salesLabel, fakeLabel, "Case Fatality Rate", "TotalDeathsOutput")
totDeathSens <- counterfeitPRCC(x, sort.results = TRUE, sort.abs = TRUE)
totDeathSens <- totDeathSens[-2]
totDeathSens <- data.frame(rownames(totDeathSens), totDeathSens[,1], totDeathSens[,2], stringsAsFactors = FALSE)
colnames(totDeathSens) <- c("Input Parameter","PRCC", "P-value")
totDeathSens <- totDeathSens[order(totDeathSens$PRCC, decreasing = TRUE),]
rownames(totDeathSens) <- NULL
#print(class(totDeathSens$PRCC))
#print(totDeathSens)
# return PRCC table
totDeathSens
}
)
# generate an HTML table view of the PRCCs
output$PRCC <- renderTable(
{
data.frame(generatePRCC())
}
)
# function to allow for .csv download of PRCCs
output$downloadPRCC <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_SensitivityAnalysis.csv', sep='')
}
,content = function(file){
out <- generatePRCC()
#print(class(out[,1]))
for(i in 1:length(out[,1])){
if(out[i,1] == "Coté d'Ivoire: Antimalarial Sales"){
out[i,1] <- "Cote d'Ivoire: Antimalarial Sales"
}
if(out[i,1] == "Coté d'Ivoire: Prevalence of PQ Antimalarials"){
out[i,1] <- "Cote d'Ivoire: Prevalence of PQ Antimalarials"
}
}
write.csv(out, file)
}
)
generateInputs <-reactive({
countrySpec <- input$countrySpecific
names2 <- names[-40]
names2 <- paste(names2)
#print(class(names2))
# shorten long country names so that they appear on the graph
for (i in 1:length(names2)){
if (names2[i] == "Central African Republic") {
names2[i] <- "CAR"
}
if (names2[i] == "Democratic Republic of the Congo") {
names2[i] <- "DRC"
}
if (names2[i] == "Cote d'Ivoire"){
names2[i] <- "Coté d'Ivoire"
}
}
displaySales <- data.frame(displaySales)
rownames(displaySales) <- NULL
#
salesLabel <- replicate(39, ": Antimalarial Sales")
salesLabel <- paste(paste(names2), salesLabel, sep = "")
salesMin <- replicate(39, "NA")
salesMax <- replicate(39, "NA")
salesDist <- replicate(39, "Normal")
#
fakeLabel<- replicate(39, ": Prevalence of PQ Antimalarials")
fakeLabel <- paste(paste(names2), fakeLabel, sep = "")
fakeDist <- replicate(39, "Uniform")
salesMean <- replicate(39, 0)
salesStd <- replicate(39,0)
fakeMean <- replicate(39,0)
fakeStd <- replicate(39,0)
if(countrySpec) {
# make dependent on Update button
input$goButton
fakeMin <- isolate(c(input$Prev1[1],input$Prev2[1],input$Prev3[1],input$Prev4[1],input$Prev5[1],input$Prev6[1],input$Prev7[1],input$Prev8[1],input$Prev9[1],input$Prev10[1],input$Prev11[1],input$Prev12[1],input$Prev13[1],input$Prev14[1],input$Prev15[1],input$Prev16[1],input$Prev17[1],input$Prev18[1],input$Prev19[1],input$Prev20[1],input$Prev21[1],input$Prev22[1],input$Prev23[1],input$Prev24[1],input$Prev25[1],input$Prev26[1],input$Prev27[1],input$Prev28[1],input$Prev29[1],input$Prev30[1],input$Prev31[1],input$Prev32[1],input$Prev33[1],input$Prev34[1],input$Prev35[1],input$Prev36[1],input$Prev37[1],input$Prev38[1],input$Prev39[1]))
fakeMax <- isolate(c(input$Prev1[2],input$Prev2[2],input$Prev3[2],input$Prev4[2],input$Prev5[2],input$Prev6[2],input$Prev7[2],input$Prev8[2],input$Prev9[2],input$Prev10[2],input$Prev11[2],input$Prev12[2],input$Prev13[2],input$Prev14[2],input$Prev15[2],input$Prev16[2],input$Prev17[2],input$Prev18[2],input$Prev19[2],input$Prev20[2],input$Prev21[2],input$Prev22[2],input$Prev23[2],input$Prev24[2],input$Prev25[2],input$Prev26[2],input$Prev27[2],input$Prev28[2],input$Prev29[2],input$Prev30[2],input$Prev31[2],input$Prev32[2],input$Prev33[2],input$Prev34[2],input$Prev35[2],input$Prev36[2],input$Prev37[2],input$Prev38[2],input$Prev39[2]))
} else{
fakeMin <- replicate(39,input$Prev[1])
fakeMax <- replicate(39,input$Prev[2])
}
for(i in 1:39){
# Sales Inputs
salesMean[i] <- displaySales$Mean[i]
salesStd[i] <- displaySales$Standard_Deviation[i]
# Prevalence Inputs
fakeMean[i] <- (fakeMax[i]+fakeMin[i])/2
fakeStd[i] <- stdUniform(fakeMin[i],fakeMax[i])
}
# create and sort sales inputs by name
cols <- c("InputParameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
salesINPUTS <- data.frame(salesLabel, salesMin, salesMean, salesMax, salesStd, salesDist, stringsAsFactors = FALSE)
colnames(salesINPUTS) <- cols
#print(salesINPUTS)
salesINPUTS <- salesINPUTS[order(salesINPUTS$InputParameter),]
cols <- c("Input Parameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
colnames(salesINPUTS) <- cols
# create and sort PQ prevalence inputs by name
cols <- c("InputParameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
fakeINPUTS <- data.frame(fakeLabel, fakeMin, fakeMean, fakeMax, fakeStd, fakeDist, stringsAsFactors = FALSE)
colnames(fakeINPUTS) <- cols
#print(fakeINPUTS)
fakeINPUTS <- fakeINPUTS[order(fakeINPUTS$InputParameter),]
cols <- c("Input Parameter", "Min", "Mean","Max", "StdDev", "Distribution Shape")
colnames(fakeINPUTS) <- cols
#case fatality rate inputs
cfrINPUTS <- list("Case Fatality Rate", input$CFR[1], (input$CFR[2]+input$CFR[1])/2,
input$CFR[2], stdUniform(input$CFR[1],input$CFR[2]), "Uniform")
masterINPUTS <- rbind(cfrINPUTS, fakeINPUTS, salesINPUTS)
masterINPUTS$Min <- as.character(masterINPUTS$Min)
masterINPUTS$Mean <- as.character(masterINPUTS$Mean)
masterINPUTS$Max <- as.character(masterINPUTS$Max)
masterINPUTS$StdDev <- as.character(masterINPUTS$StdDev)
masterINPUTS
}
)
output$InputParameters <- renderTable({
inputs <- data.frame(generateInputs())
rownames(inputs) <- NULL
inputs
})
output$downloadInputs <- downloadHandler(
filename = function() {
results <- generateLHS()
inputID <- results[5]
#print(inputID)
paste(inputID,'_InputParamters.csv', sep='')
}
,content = function(file){
out <- generateInputs()
for(i in 1:length(out[,1])){
if(out[i,1] == "Coté d'Ivoire: Antimalarial Sales") out[i,1] <- "Cote d'Ivoire: Antimalarial Sales"
if(out[i,1] == "Coté d'Ivoire: Prevalence of PQ Antimalarials") out[i,1] <- "Cote d'Ivoire: Prevalence of PQ Antimalarials"
}
write.csv(out, file)
}
)
# generate an HTML Table view of the Cohen Antimalarial Sales
# input data
output$sales <- renderTable(
{
#print("I AM HERE Third")
data.frame(displaySales)
displaySales <- displaySales[order(displaySales$Mean, decreasing = TRUE),]
rownames(displaySales) <- NULL
displaySales
}
)
}
)
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