diversity-app

server.R

library("XML")
library("ggplot2")
library("downloader")
library('scales')
library('grid')
library('RColorBrewer')


function(input, output) {


        team_url <- 'https://docs.google.com/spreadsheets/u/1/d/1E9WwcIEYuGxR8GUrmxL1iaozOk_0FKSPPWbnCDn_C0A/pubhtml'
        applicants_url <- "https://docs.google.com/spreadsheets/d/11GXSEkgDnLIBWmqYWJA1VbG9xmsPPl2MFRWxvFiWmwQ/pubhtml"

        urls <- list(team = team_url, applicants = applicants_url)

        applicants_data <- readGoogleSheet(applicants_url, 'applicants')
        applicants_data <- cleanUpData(applicants_data)

        team_data <- readGoogleSheet(team_url, 'team')
        team_data <- cleanUpData(team_data)

        ## RETURN REQUESTED DATASET
        datasetInput <- reactive({
          switch(input$dataset,
                 "The Buffer Team" = team_data,
                 "Applicants" = applicants_data)
        })


        #plots
        output$genderPlot <- renderPlot({
            data <- datasetInput()

            department_and_gender <- data %>%
            group_by(department,gender) %>%
            summarise(n=n()) %>%
            mutate(percent=n/sum(n),department_size=sum(n))
            
            ggplot(department_and_gender, aes(x=reorder(department,-department_size), y=n, fill=gender)) +
                geom_bar(stat="identity") + scale_fill_brewer(palette="Pastel1") +
                labs(x="Area",y="Number of People", title="Gender Breakdown Across Areas")
        })
        
        output$genderTimeSeries <- renderPlot({
            data <- datasetInput()

            time_and_gender <- data %>%
                group_by(posixDate,gender) %>%
                summarise(n=n())
            
            ggplot(time_and_gender, aes(x=posixDate, y=n, fill=gender)) +
                geom_bar(stat="identity") +
                scale_color_brewer(palette="Pastel1") +
                labs(x="Date",y="People", title="Gender of Applicants") +
                theme_minimal()
        })
        
        output$ethnicityTimeSeries <- renderPlot({
            data <- datasetInput()
            data <- data[-1,]
            data$posixDateTime <- as.POSIXct(data$date,format="%m/%d/%Y %H:%M:%S")
            data$posixDate <- as.Date(data$posixDateTime)
            
            time_and_ethnicity <- data %>%
                group_by(posixDate,ethnicity) %>%
                summarise(n=n())
            
            ggplot(time_and_ethnicity, aes(x=posixDate, y=n, fill=ethnicity)) +
                geom_bar(stat="identity") +
                scale_fill_brewer(palette="Pastel1") +
                labs(x="Date",y="People", title="Ethnicity of Applicants") +
                theme_minimal()
        })
        
        output$ethnicityPlot <- renderPlot({
            data <- datasetInput()
            department_and_ethnicity <- data %>%
                group_by(department,ethnicity) %>%
                summarise(n=n()) %>%
                mutate(percent=n/sum(n),department_size=sum(n))

            ggplot(department_and_ethnicity, aes(x=reorder(department,department_size), y=n, fill=ethnicity)) +
                geom_bar(stat="identity") + coord_flip() +
                labs(x="Area",y="People", title="Ethnicity Breakdown Across Areas") +
                scale_fill_brewer(palette="Pastel1")
        })
        
        output$ethnicityPies <- renderPlot({
        data <- datasetInput()
        by_ethnicity <- data %>%
                group_by(department,ethnicity) %>%
                summarise(n=n()) %>%
                mutate(percent=n/sum(n))

        total_ethnicity_row <- data %>%
                group_by(ethnicity) %>%
                summarise(n=n()) %>%
                mutate(percent=n/sum(n),department="Total")


        total_ethnicity_breakdown <- rbind(by_ethnicity,total_ethnicity_row)


        ggplot(total_ethnicity_breakdown,aes(x=factor(1),y=percent,fill=ethnicity)) +
                geom_bar(stat="identity",width=1) + 
                facet_wrap(~department) +
                coord_polar(theta="y") + 
                scale_fill_brewer(palette="Pastel1") +
                theme_minimal() +
                theme(axis.ticks = element_blank(), axis.text.y = element_blank(), axis.text.x = element_blank()) +
                labs(x="",y="",title="Ethnicity Breakdown Across Areas")
        })
        
        output$genderPies <- renderPlot({
        data <- datasetInput()
        
        department_and_gender <- data %>%
                group_by(department,gender) %>%
                summarise(n=n()) %>%
                mutate(percent=n/sum(n))

        total_row <- data %>%
                group_by(gender) %>%
                summarise(n=n()) %>%
                mutate(percent=n/sum(n),department="Total")

        total_gender_breakdown <- rbind(department_and_gender,total_row)
        total_gender_breakdown$gender <- factor(total_gender_breakdown$gender,levels=rev(levels(total_gender_breakdown$gender)))


        ggplot(total_gender_breakdown,aes(x=factor(1),y=percent,fill=gender)) +
                geom_bar(stat="identity",width=1) + 
                facet_wrap(~department) +
                coord_polar(theta="y") + 
                scale_fill_brewer(palette="Pastel1") +
                theme_minimal() +
                theme(axis.ticks = element_blank(), axis.text.y = element_blank(), axis.text.x = element_blank()) +
                labs(x="",y="",title="Gender Breakdown Across Areas :)")
        })
        #raw data
        output$teamTable <- renderTable(team_data)
        output$applicantsTable <- renderTable(applicants_data)
}