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app.R
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#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(plotly)
library(ggplot2)
library(scales)
library(doBy)
library(DT)
set.seed(1)
populationdata<-data.frame(ID=1:1000,Training=sample(c("Yes","No"),size = 1000,replace=TRUE,prob = c(0.95,0.18)),Gender=sample(c("Male","Female"),size = 1000,replace=TRUE,prob = c(0.64,0.88)),
Age=round(rnorm(1000,45,10)))
populationdata$Income<-ifelse(populationdata$Gender=="Male",50+10*round(rexp(407,1/10000)/10,0),50+10*round(rexp(593,1/9000)/10,0))
pdat<-populationdata
pdat$Gender<-100*as.numeric(pdat$Gender=="Female")
pdat$Training<-100*as.numeric(pdat$Training=="Yes")
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
sidebarLayout(
sidebarPanel(style = "position:fixed;width:inherit;",
selectInput(inputId = "Variable",
"Select A Variable",
c("Gender of Farmer" = "Gender",
"Age of Farmer"= "Age" ,
"Sales Income from Oranges"= "Income",
"Training Attendance"="Training")),
sliderInput(inputId = "Add10",
label="Sample Size",min = 0,max=1000,value = 1000,step=10),
actionButton(inputId = "refresh",
"New Survey"),
actionButton(inputId = "reset",
"Reset"),width = 2),
mainPanel(
titlePanel("Estimation"),
tabsetPanel(
tabPanel("Data",DT::dataTableOutput("data1")),
tabPanel("Summary",tableOutput("stats1"),plotlyOutput("plot1"),plotlyOutput("plot1a")),
tabPanel("Repeatability",actionButton(inputId = "nrep",
label="Add 100 New Surveys"),plotlyOutput("plot3"),
checkboxInput("meanline","Add Mean Line",value=FALSE), checkboxInput("ciline","Add 95% CI Lines",value=FALSE),
checkboxInput("fix","Fix Axis",value=FALSE)),
tabPanel("Precision",tableOutput("stats2"),plotlyOutput("plot5"),plotlyOutput("plot4")),
tabPanel("Disaggregation", selectInput(inputId = "group",
"Select A Disaggregation Variable",
c("Gender of Farmer" = "Gender","Training Attendance"="Training")),tableOutput("stats3"),plotlyOutput("plot_d"),
checkboxInput("fix1","Fix Axis",value=TRUE))
)
,width = 8)
)
)
yaxis<-list(axis.automargin=TRUE)
# Define server logic required to draw a histogram
server <- function(input, output) {
counter <- reactiveValues(n = 0)
sampledata<-eventReactive(input$refresh|counter$n==0,{
if(input$Add10=="1000"|counter$n==0){
data<-populationdata
}
else{
data<-populationdata[sample(1:1000,as.numeric(as.character(input$Add10))),]
data<-data[order(data$ID),]
}
data})
observeEvent(input$refresh, {counter$n <- counter$n + 1})
observeEvent(input$reset, { set.seed(1)
counter$n <- 0})
observeEvent(input$nrep, {counter$n <- counter$n + 100})
output$data1<-DT::renderDataTable({
sampledata()
}, options = list(
pageLength = 25) )
output$plot1<-renderPlotly({
if(input$Variable=="Gender"){
p1<-ggplot(data=sampledata(),aes(y = (..count..)/sum(..count..),x=Gender,fill=Gender))+geom_bar(position="dodge",col="black")+
xlab("Gender of Farmer")+ylab("Number of Farmers")+ggtitle("Farmers by Gender",subtitle = paste("N=",nrow(sampledata())))+
scale_y_continuous(labels=percent,limits=c(0,1))
}
if(input$Variable=="Training"){
p1<- ggplot(data=sampledata(),aes(y = (..count..)/sum(..count..),x=Training,fill=Training))+geom_bar(position="dodge",col="black")+
xlab("Training Attended in Past 12 Months")+ylab("Number of Farmers")+ggtitle("Farmers by Training Attendance",subtitle = paste("N=",nrow(sampledata())))+
scale_y_continuous(labels=percent,limits=c(0,1))
}
if(input$Variable=="Income"){
p1<- ggplot(data=sampledata(),aes(x =Income))+geom_histogram(fill="red",col="black",alpha=0.6)+
xlab("Sales Income From Oranges")+ggtitle("Sales Income From Oranges",subtitle = paste("N=",nrow(sampledata())))+xlim(0,100000)
}
if(input$Variable=="Age"){
p1<-ggplot(data=sampledata(),aes(x =Age))+geom_histogram(fill="red",col="black",alpha=0.6)+
xlab("Age of Farmer")+ggtitle("Age of Farmer",subtitle = paste("N=",nrow(sampledata())))+xlim(0,100)
}
p2<- p1+theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plot
})
output$plot1a<-renderPlotly({
if(input$Variable=="Income"|input$Variable=="Age"){
if(input$Variable=="Income"){
p1<- ggplot(data=sampledata(),aes(y =Income,x=" "))+geom_boxplot(position="dodge",col="black")+
xlab(" ")+ylab("Sales Income From Oranges")+ggtitle("Sales Income From Oranges",subtitle = paste("N=",nrow(sampledata())))+ylim(0,100000)+coord_flip()
}
if(input$Variable=="Age"){
p1<-ggplot(data=sampledata(),aes(y =Age,x=" "))+geom_boxplot(position="dodge",col="black")+
xlab(" ")+ylab("Age of Farmer")+ggtitle("Age of Farmer",subtitle = paste("N=",nrow(sampledata())))+ylim(0,100)+coord_flip()
}
p2<-p1+theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plot
}
if(input$Variable=="Gender"){
p1<-ggplot(data=sampledata(),aes(y = (..count..)/sum(..count..),x=Gender,fill=Gender))+geom_bar(position="dodge",col="black")+
xlab("Gender of Farmer")+ylab("Number of Farmers")+ggtitle("Farmers by Gender",subtitle = paste("N=",nrow(sampledata())))+
scale_y_continuous(labels=percent,limits=c(0,1))
}
if(input$Variable=="Training"){
p1<- ggplot(data=sampledata(),aes(y = (..count..)/sum(..count..),x=Training,fill=Training))+geom_bar(position="dodge",col="black")+
xlab("Training Attended in Past 12 Months")+ylab("Number of Farmers")+ggtitle("Farmers by Training Attendance",subtitle = paste("N=",nrow(sampledata())))+
scale_y_continuous(labels=percent,limits=c(0,1))
}
p2<-p1+theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plot
})
output$stats1<-renderTable({
if(input$Variable=="Gender"){
df1<- data.frame(data.frame(table(sampledata()$Gender)), Percent=data.frame(prop.table(table(sampledata()$Gender)))[,2])
colnames(df1)<-c("Gender","Frequency","Percentage")
df1$Percentage<-paste(round(100*df1$Percentage),"%",sep="")
}
if(input$Variable=="Training"){
df1<- data.frame(data.frame(table(sampledata()$Training)), Percent=data.frame(prop.table(table(sampledata()$Training)))[,2])
colnames(df1)<-c("Training","Frequency","Percentage")
df1$Percentage<-paste(round(100*df1$Percentage),"%",sep="")
}
if(input$Variable=="Income"){
df1<- data.frame(Statistic=c("Mean","Standard Deviation","Median","Minimum","Maximum","5th Percentile","25th Percentile","75th Percentile","95th Percentile"),
Estimate=round(c(mean(sampledata()$Income),sd(sampledata()$Income),
median(sampledata()$Income),
min(sampledata()$Income),
max(sampledata()$Income),
quantile(sampledata()$Income,0.05),
quantile(sampledata()$Income,0.25),
quantile(sampledata()$Income,0.75),
quantile(sampledata()$Income,0.95))))
}
if(input$Variable=="Age"){
df1<-data.frame(Statistic=c("Mean","Standard Deviation","Median","Minimum","Maximum","5th Percentile","25th Percentile","75th Percentile","95th Percentile"),
Estimate=round(c(mean(sampledata()$Age),sd(sampledata()$Age),
median(sampledata()$Age),
min(sampledata()$Age),
max(sampledata()$Age),
quantile(sampledata()$Age,0.05),
quantile(sampledata()$Age,0.25),
quantile(sampledata()$Age,0.75),
quantile(sampledata()$Age,0.95)),1))
}
df1
})
output$stats2<-renderTable({
tmp<-sampledata()
tmp$Gender<-100*as.numeric(tmp$Gender=="Female")
tmp$Training<-100*as.numeric(tmp$Training=="Yes")
out1<-data.frame(Variable=c("Age","Gender","Income","Training"),Type=c("Mean","% Female","Mean","% Received Training"),Sample=input$Add10,
Estimate=c(mean(tmp$Age),mean(tmp$Gender),mean(tmp$Income),mean(tmp$Training)),
SD=c(sd(tmp$Age),
100*sqrt(mean(tmp$Gender/100)*(1-mean(tmp$Gender/100))),
sd(tmp$Income),
100*sqrt(mean(tmp$Training/100)*(1-mean(tmp$Training/100)))))
out1$SE=sqrt((1000-as.numeric(as.character(input$Add10)))/999)*out1$SD/sqrt(as.numeric(as.character(input$Add10)))
out1$LCI=out1$Estimate-qt(0.975,as.numeric(as.character(input$Add10))-1)*out1$SE
out1$UCI=out1$Estimate+qt(0.975,as.numeric(as.character(input$Add10))-1)*out1$SE
out1$UCI[out1$UCI>100&input$Variable=="Gender"|input$Variable=="Training"]<-100
out1$LCI[out1$LCI<0&input$Variable=="Gender"|input$Variable=="Training"]<-0
colnames(out1)[5:8]<-c("Standard Deviation","Standard Error","95% Confidence Interval (Lower)","95% Confidence Interval (Upper)")
out1[out1$Variable==input$Variable,]
})
output$stats3<-renderTable({
if(input$group=="Gender"){
tmp<-sampledata()
tmp$Gender<-100*as.numeric(tmp$Gender=="Female")
tmp$Training<-100*as.numeric(tmp$Training=="Yes")
Men=subset(tmp,Gender==0)
Women=subset(tmp,Gender==100)
out1<-data.frame(Variable=c("Age","Income","Training"),Type=c("Mean","Mean","% Received Training"),
Group="Male Farmers",Sample=nrow(Men),
Estimate=c(mean(Men$Age),mean(Men$Income),mean(Men$Training)),
SD=c(sd(Men$Age),
sd(Men$Income),
100*sqrt(mean(Men$Training/100)*(1-mean(Men$Training/100)))))
out2<-data.frame(Variable=c("Age","Income","Training"),
Type=c("Mean","Mean","% Received Training"),Group="Female Farmers",Sample=nrow(Women),
Estimate=c(mean(Women$Age),mean(Women$Income),mean(Women$Training)),
SD=c(sd(Women$Age),
sd(Women$Income),
100*sqrt(mean(Women$Training/100)*(1-mean(Women$Training/100)))))
out1$SE=sqrt((407-out1$Sample)/406)*out1$SD/sqrt(out1$Sample)
out2$SE=sqrt((593-out2$Sample)/592)*out2$SD/sqrt(out2$Sample)
out3<-rbind(out1,out2)
out3<- out3[out3$Variable==input$Variable,]
out3$LCI=out3$Estimate-qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI=out3$Estimate+qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI[out3$UCI>100&input$Variable=="Gender"|input$Variable=="Training"]<-100
out3$LCI[out3$LCI<0&input$Variable=="Gender"|input$Variable=="Training"]<-0
colnames(out3)[6:9]<-c("Standard Deviation","Standard Error",
"95% Confidence Interval (Lower)","95% Confidence Interval (Upper)")
}
if(input$group=="Training"){
tmp<-sampledata()
tmp$Gender<-100*as.numeric(tmp$Gender=="Female")
tmp$Training<-100*as.numeric(tmp$Training=="Yes")
Men=subset(tmp,Training==0)
Women=subset(tmp,Training==100)
out1<-data.frame(Variable=c("Age","Income","Gender"),Type=c("Mean","Mean","% Female Farmers"),Group="No Training",Sample=nrow(Men),
Estimate=c(mean(Men$Age),mean(Men$Income),mean(Men$Gender)),
SD=c(sd(Men$Age),
sd(Men$Income),
100*sqrt(mean(Men$Gender/100)*(1-mean(Men$Gender/100)))))
out2<-data.frame(Variable=c("Age","Income","Gender"),Type=c("Mean","Mean","% Female Farmers"),Group="Training",Sample=nrow(Women),
Estimate=c(mean(Women$Age),mean(Women$Income),mean(Women$Gender)),
SD=c(sd(Women$Age),
sd(Women$Income),
100*sqrt(mean(Women$Gender/100)*(1-mean(Women$Gender/100)))))
out1$SE=sqrt((161-out1$Sample)/160)*out1$SD/sqrt(out1$Sample)
out2$SE=sqrt((839-out2$Sample)/838)*out2$SD/sqrt(out2$Sample)
out3<-rbind(out1,out2)
out3<- out3[out3$Variable==input$Variable,]
out3$LCI=out3$Estimate-qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI=out3$Estimate+qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI[out3$UCI>100&input$Variable=="Gender"|input$Variable=="Training"]<-100
out3$LCI[out3$LCI<0&input$Variable=="Gender"|input$Variable=="Training"]<-0
colnames(out3)[6:9]<-c("Standard Deviation","Standard Error",
"95% Confidence Interval (Lower)","95% Confidence Interval (Upper)")
}
out3
})
output$plot_d<-renderPlotly({
if(input$group=="Gender"){
tmp<-sampledata()
tmp$Gender<-100*as.numeric(tmp$Gender=="Female")
tmp$Training<-100*as.numeric(tmp$Training=="Yes")
Men=subset(tmp,Gender==0)
Women=subset(tmp,Gender==100)
out1<-data.frame(Variable=c("Age","Income","Training"),Type=c("Mean","Mean","% Received Training"),Group="Male Farmers",Sample=nrow(Men),
Estimate=c(mean(Men$Age),mean(Men$Income),mean(Men$Training)),
SD=c(sd(Men$Age),
sd(Men$Income),
100*sqrt(mean(Men$Training/100)*(1-mean(Men$Training/100)))))
out2<-data.frame(Variable=c("Age","Income","Training"),
Type=c("Mean","Mean","% Received Training"),Group="Female Farmers",Sample=nrow(Women),
Estimate=c(mean(Women$Age),mean(Women$Income),mean(Women$Training)),
SD=c(sd(Women$Age),
sd(Women$Income),
100*sqrt(mean(Women$Training/100)*(1-mean(Women$Training/100)))))
out1$SE=sqrt((407-out1$Sample)/406)*out1$SD/sqrt(out1$Sample)
out2$SE=sqrt((593-out2$Sample)/592)*out2$SD/sqrt(out2$Sample)
out3<-rbind(out1,out2)
print(out3$Variable)
print(input$Variable)
out3<- out3[out3$Variable==input$Variable,]
out3$LCI=out3$Estimate-qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI=out3$Estimate+qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI[out3$UCI>100&input$Variable=="Gender"|input$Variable=="Training"]<-100
out3$LCI[out3$LCI<0&input$Variable=="Gender"|input$Variable=="Training"]<-0
p1<-ggplot(data=out3,aes(y=Estimate,x=Group,ymax=UCI,ymin=LCI))+geom_errorbar(width=0.1)+geom_point(aes(col=Group),size=5)+
xlab("Gender of Farmer")+ylab(paste("Estimate of",input$Variable))+
theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
}
if(input$group=="Training"){
tmp<-sampledata()
tmp$Gender<-100*as.numeric(tmp$Gender=="Female")
tmp$Training<-100*as.numeric(tmp$Training=="Yes")
Men=subset(tmp,Training==0)
Women=subset(tmp,Training==100)
out1<-data.frame(Variable=c("Age","Income","Gender"),Type=c("Mean","Mean","% Female Farmers"),Group="No Training",Sample=nrow(Men),
Estimate=c(mean(Men$Age),mean(Men$Income),mean(Men$Gender)),
SD=c(sd(Men$Age),
sd(Men$Income),
100*sqrt(mean(Men$Gender/100)*(1-mean(Men$Gender/100)))))
out2<-data.frame(Variable=c("Age","Income","Gender"),Type=c("Mean","Mean","% Female Farmers"),Group="Training",Sample=nrow(Women),
Estimate=c(mean(Women$Age),mean(Women$Income),mean(Women$Gender)),
SD=c(sd(Women$Age),
sd(Women$Income),
100*sqrt(mean(Women$Gender/100)*(1-mean(Women$Gender/100)))))
out1$SE=sqrt((161-out1$Sample)/160)*out1$SD/sqrt(out1$Sample)
out2$SE=sqrt((839-out2$Sample)/838)*out2$SD/sqrt(out2$Sample)
out3<-rbind(out1,out2)
out3<- out3[out3$Variable==input$Variable,]
out3$LCI=out3$Estimate-qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI=out3$Estimate+qt(0.975,as.numeric(as.character(input$Add10))-1)*out3$SE
out3$UCI[out3$UCI>100&input$Variable=="Gender"|input$Variable=="Training"]<-100
out3$LCI[out3$LCI<0&input$Variable=="Gender"|input$Variable=="Training"]<-0
p1<-ggplot(data=out3,aes(y=Estimate,x=Group,ymax=UCI,ymin=LCI))+geom_errorbar(width=0.1)+geom_point(aes(col=Group),size=5)+
xlab("Training Received?")+ylab(paste("Estimate of",input$Variable))+
theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
}
if(input$fix1==TRUE){
p1<-p1+ylim(min(pdat[,input$Variable]),max(pdat[,input$Variable]))
}
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p1),yaxis=yaxis1)
new_plot
})
output$plot3<-renderPlotly({
reps<-data.frame(Survey=1:counter$n,Estimate=NA,sd=NA)
set.seed(1)
for(i in 1:counter$n){
tmp<-pdat[sample(1:1000,as.numeric(as.character(input$Add10))),input$Variable]
reps$Estimate[i]<-mean(tmp)
reps$sd[i]<-ifelse(input$Variable=="Income"|input$Variable=="Age",sd(tmp),
100*sqrt(mean(tmp/100)*(1-mean(tmp/100))))
}
reps$upper<-reps$Estimate+qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-as.numeric(as.character(input$Add10)))/999)*reps$sd/sqrt(as.numeric(as.character(input$Add10)))
reps$lower<-reps$Estimate-qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-as.numeric(as.character(input$Add10)))/999)*reps$sd/sqrt(as.numeric(as.character(input$Add10)))
p1<-ggplot(data=reps,aes(y=Estimate,x=Survey))
if(input$meanline==TRUE){
p1<-p1+geom_hline(aes(yintercept=mean(Estimate)),col="blue")
}
if(input$ciline==TRUE){
p1<-p1+
geom_errorbar(aes(ymax=upper,
ymin=lower),col="red",linetype=2,width=0.1)
}
if(input$fix==TRUE){
p1<-p1+ylim(min(pdat[,input$Variable]),max(pdat[,input$Variable]))
}
p2<-p1+geom_point(size=2,col="forestgreen")+
theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )+
ylab(input$Variable)+xlab("Survey Number")+ggtitle(paste("Estimates of",input$Variable,"\nfrom",counter$n,"surveys each of sample size",input$Add10))
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plot
})
output$plot5<-renderPlotly({
pdat<-populationdata
pdat$Gender<-100*as.numeric(pdat$Gender=="Female")
pdat$Training<-100*as.numeric(pdat$Training=="Yes")
ns<-data.frame(N=10:1000,Training=0.95/(0.95+0.18),Gender=0.88/(0.88+0.64),Income=10050,Age=45)
ns$Training_SE<-sqrt((ns$Training*(1-ns$Training))/ns$N)*sqrt((1000-ns$N)/999)
ns$Gender_SE<-sqrt((ns$Gender*(1-ns$Gender))/ns$N)*sqrt((1000-ns$N)/999)
ns$Age_SE<-10/sqrt(ns$N)*sqrt((1000-ns$N)/999)
ns$Income_SE<-10000/sqrt(ns$N)*sqrt((1000-ns$N)/999)
if(input$Variable=="Gender"){
ns$Upper<-ns$Gender+qt(0.975,ns$N-1)*ns$Gender_SE
ns$Lower<-ns$Gender-qt(0.975,ns$N-1)*ns$Gender_SE
p1<-ggplot(data=ns,aes(y=Gender,x=N))+
geom_line()+geom_line(aes(y=Upper),col="red")+
geom_line(aes(y=Lower),col="red")+
xlab("Sample Size")+ylab("Confidence Interval")+ggtitle("% Female Farmers + Confidence Interval")
}
if(input$Variable=="Training"){
ns$Upper<-ns$Training+qt(0.975,ns$N-1)*ns$Training_SE
ns$Lower<-ns$Training-qt(0.975,ns$N-1)*ns$Training_SE
p1<- ggplot(data=ns,aes(y=Training,x=N))+geom_line()+
geom_line(aes(y=Upper),col="red")+
geom_line(aes(y=Lower),col="red")+
xlab("Sample Size")+ylab("Confidence Interval")+ggtitle("% Trained Farmers + Confidence Interval")
}
if(input$Variable=="Age"){
ns$Upper<-ns$Age+qt(0.975,ns$N-1)*ns$Age_SE
ns$Lower<-ns$Age-qt(0.975,ns$N-1)*ns$Age_SE
p1<- ggplot(data=ns,aes(y=Age,x=N))+geom_line()+
geom_line(aes(y=Upper),col="red")+
geom_line(aes(y=Lower),col="red")+
xlab("Sample Size")+ylab("Confidence Interval")+ggtitle("Mean Age + Confidence Interval")
}
if(input$Variable=="Income"){
ns$Upper<-ns$Income+qt(0.975,ns$N-1)*ns$Income_SE
ns$Lower<-ns$Income-qt(0.975,ns$N-1)*ns$Income_SE
p1<- ggplot(data=ns,aes(y=Income,x=N))+geom_line()+
geom_line(aes(y=Lower),col="red")+
geom_line(aes(y=Lower),col="red")+
xlab("Sample Size")+ylab("Confidence Interval")+ggtitle("Mean Income + Confidence Interval")
}
p2<-p1 +
theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plot
})
output$plot4<-renderPlotly({
pdat<-populationdata
pdat$Gender<-100*as.numeric(pdat$Gender=="Female")
pdat$Training<-100*as.numeric(pdat$Training=="Yes")
ns<-data.frame(N=10:1000,Training=0.95/(0.95+0.18),Gender=0.88/(0.88+0.64),Income=10050,Age=45)
ns$Training_SE<-sqrt((ns$Training*(1-ns$Training))/ns$N)*sqrt((1000-ns$N)/999)
ns$Gender_SE<-sqrt((ns$Gender*(1-ns$Gender))/ns$N)*sqrt((1000-ns$N)/999)
ns$Age_SE<-10/sqrt(ns$N)*sqrt((1000-ns$N)/999)
ns$Income_SE<-10000/sqrt(ns$N)*sqrt((1000-ns$N)/999)
if(input$Variable=="Gender"){
p1<-ggplot(data=ns,aes(y=Gender_SE,x=N))+geom_line()+xlab("Sample Size")+ylab("Standard Error")+ggtitle("Standard Error of % Female Farmers")
}
if(input$Variable=="Training"){
p1<- ggplot(data=ns,aes(y=Training_SE,x=N))+geom_line()+xlab("Sample Size")+ylab("Standard Error")+ggtitle("Standard Error of % Trained Farmers")
}
if(input$Variable=="Age"){
p1<- ggplot(data=ns,aes(y=Age_SE,x=N))+geom_line()+xlab("Sample Size")+ylab("Standard Error")+ggtitle("Standard Error of Mean Age")
}
if(input$Variable=="Income"){
p1<- ggplot(data=ns,aes(y=Income_SE,x=N))+geom_line()+xlab("Sample Size")+ylab("Standard Error")+ggtitle("Standard Error of Mean Income")
}
p2<- p1 +
theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plot
})
output$plot_c<-renderPlotly({
if(input$Variable=="Gender"){
p1<-ggplot(data=sampledata(),aes(y = (..count..)/sum(..count..),x=Gender,fill=Gender))+geom_bar(position="dodge",col="black")+
xlab("Gender of Farmer")+ylab("Number of Farmers")+ggtitle("Farmers by Gender",subtitle = paste("N=",nrow(sampledata())))+
scale_y_continuous(labels=percent,limits=c(0,1))
}
if(input$Variable=="Training"){
p1<- ggplot(data=sampledata(),aes(y = (..count..)/sum(..count..),x=Training,fill=Training))+geom_bar(position="dodge",col="black")+
xlab("Training Attended in Past 12 Months")+ylab("Number of Farmers")+ggtitle("Farmers by Training Attendance",subtitle = paste("N=",nrow(sampledata())))+
scale_y_continuous(labels=percent,limits=c(0,1))
}
if(input$Variable=="Income"){
p1<- ggplot(data=sampledata(),aes(x =Income))+geom_histogram(fill="red",col="black",alpha=0.6)+
xlab("Sales Income From Oranges")+ggtitle("Income From Oranges",subtitle = paste("N=",nrow(sampledata())))+xlim(0,100000)
}
if(input$Variable=="Age"){
p1<-ggplot(data=sampledata(),aes(x =Age))+geom_histogram(fill="red",col="black",alpha=0.6)+
xlab("Age of Farmer")+ggtitle("Age of Farmer",subtitle = paste("N=",nrow(sampledata())))+xlim(0,100)
}
p2<-p1+theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plot
})
output$plotx<-renderPlotly({
if(input$Variable1=="Gender"){
plt1<-data.frame(percents=c(mean(sampledata()[,"Training"]=="Yes"),mean(sampledata()[,"Training"]=="No")),Training=c("Yes","No"))
plt1$upper< plt1$percents+qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)*(plt1$percents*(1-plt1$percents))/sqrt(nrow(sampledata()))
plt1$lower<-plt1$percents-qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)*(plt1$percents*(1-plt1$percents))/sqrt(nrow(sampledata()))
plt1$lower[plt1$lower<0]<-0
p1<-ggplot(data=plt1,aes(y =percents,x=Training))+
geom_point()+
geom_errorbar(aes(ymax=upper,
ymin=lower),width=0.1)+
xlab("Gender of Farmer")+ylab("Number of Farmers")+ggtitle(paste("Farmers by GenderN=",nrow(sampledata())))+
scale_y_continuous(labels=percent)+ylim(0,1)
}
if(input$Variable1=="Training"){
plt1<-data.frame(percents=c(mean(sampledata()[,"Training"]=="Yes"),mean(sampledata()[,"Training"]=="No")),Training=c("Yes","No"))
plt1$upper< plt1$percents+qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)*(plt1$percents*(1-plt1$percents))/sqrt(nrow(sampledata()))
plt1$lower<-plt1$percents-qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)*(plt1$percents*(1-plt1$percents))/sqrt(nrow(sampledata()))
plt1$upper[plt1$upper>1]<-1
plt1$lower[plt1$lower<0]<-0
p1<-ggplot(data=plt1,aes(y =percents,x=Training))+
geom_point()+
geom_errorbar(aes(ymax=upper,
ymin=lower),width=0.1)+
xlab("Training Attended in Past 12 Months")+ylab("Number of Farmers")+ggtitle(paste("Farmers by Training AttendanceN=",nrow(sampledata())))+
scale_y_continuous(labels=percent)+ylim(0,1)
}
if(input$Variable1=="Income"){
plt1<-data.frame(mean=mean(sampledata()[,"Income"]),sd=sd(sampledata()[,"Income"]))
plt1$upper< plt1$mean+qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)* plt1$sd/sqrt(nrow(sampledata()))
plt1$lower<-plt1$mean-qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)* plt1$sd/sqrt(nrow(sampledata()))
p1<-ggplot(data=plt1,aes(y =mean,x=1))+
geom_point()+
geom_errorbar(aes(ymax=upper,
ymin=lower),width=0.1)+
xlab(" ")+ylab("Sales Income From Oranges")+ggtitle(paste("Sales Income From OrangesN=",nrow(sampledata())))+ylim(0,25000)
}
if(input$Variable1=="Age"){
plt1<-data.frame(mean=mean(sampledata()[,"Age"]),sd=sd(sampledata()[,"Age"]))
plt1$upper< plt1$mean+qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)* plt1$sd/sqrt(nrow(sampledata()))
plt1$lower<-plt1$mean-qt(0.975,as.numeric(as.character(input$Add10))-1)*sqrt((1000-nrow(sampledata()))/999)* plt1$sd/sqrt(nrow(sampledata()))
p1<-ggplot(data=plt1,aes(y =mean,x=1))+
geom_point()+
geom_errorbar(aes(ymax=upper,
ymin=lower),width=0.1)+
xlab(" ")+ylab("Age of Farmer")+ggtitle(paste("Age of FarmerN=",nrow(sampledata())))+ylim(0,25000)
}
p2<- p1+
theme(axis.title = element_text(size=14),title = element_text(size=18,face = "bold"),
legend.text = element_text(size=14),legend.title = element_text(size=16,face="bold"),axis.text =element_text(size=14) )
yaxis1<-list(axis.automargin=TRUE,tickprefix=" ")
new_plot<-layout(ggplotly(p2),yaxis=yaxis1)
new_plots
})
}
# Run the application
shinyApp(ui = ui, server = server)