app.R

# app.R

# Load necessary libraries
library(shiny)
library(readr)   # for reading CSVs
library(dplyr)   # for data manipulation
library(ggplot2) # for plotting
library(DT)      # for rendering data tables interactively

# Define UI
ui <- fluidPage(
  
  # Title of the application
  titlePanel("Country Representation Explorer"),
  
  # Sidebar layout
  sidebarLayout(
    sidebarPanel(
      # Input: Select which representation metric to visualize
      selectInput(
        inputId = "metric",
        label = "Choose representation metric:",
        choices = c("Overall", "Representation Gap", "Ethnicity", 
                    "Gender", "Religion", "Language"),
        selected = "Overall"
      ),
      
      # Input: Numeric slider to filter based on the chosen metric
      sliderInput(
        inputId = "metricRange",
        label = "Filter countries by metric range:",
        min = -10,  # set a plausible minimum range
        max = 10,   # set a plausible maximum range
        value = c(-10, 10),
        step = 0.1
      ),
      
      # Checkbox to toggle data table
      checkboxInput(
        inputId = "showDataTable",
        label = "Show data table of filtered results",
        value = TRUE
      )
    ),
    
    # Main panel to display outputs
    mainPanel(
      # Output: Bar plot
      plotOutput("repPlot", height = "600px"),
      
      # Output: Data table (shown only when checkbox is TRUE)
      conditionalPanel(
        condition = "input.showDataTable == true",
        DTOutput("tableOut")
      )
    )
  )
)

# Define server logic
server <- function(input, output, session) {
  
  # Read in the CSV
  # Make sure your 'CountryRepresentationRankings.csv' file is in the same folder as this script
  full_data <- reactive({
    read_csv("CountryRepresentationRankings.csv", show_col_types = FALSE)
  })
  
  # Reactive subset of the data based on the metric range selected
  filtered_data <- reactive({
    req(input$metricRange)
    req(input$metric)
    
    # Ensure data is loaded
    data <- full_data()
    
    # Filter based on user-selected metric range
    data %>%
      filter(
        # get() dynamically selects the column name based on input$metric
        get(input$metric) >= input$metricRange[1] &
          get(input$metric) <= input$metricRange[2]
      )
  })
  
  # Render the bar plot
  output$repPlot <- renderPlot({
    data_for_plot <- filtered_data()
    
    # Proceed only if there's data to plot
    validate(
      need(nrow(data_for_plot) > 0, "No data available within this range.")
    )
    
    # Order countries by the chosen metric
    ggplot(data_for_plot, aes(
      x = reorder(Country, get(input$metric)), 
      y = get(input$metric)
    )) +
      geom_col(fill = "steelblue") +
      coord_flip() + # Flip coordinates for easier reading of country names
      labs(
        x = "Country",
        y = paste(input$metric, "Index"),
        title = paste("Countries by", input$metric, "Representation")
      ) +
      theme_minimal(base_size = 14)
  })
  
  # Render the data table (only if checkbox is selected)
  output$tableOut <- renderDT({
    data_for_table <- filtered_data()
    datatable(
      data_for_table,
      options = list(pageLength = 10, autoWidth = TRUE),
      rownames = FALSE
    )
  })
}

# Create Shiny app
shinyApp(ui, server)