app.R

options(error = NULL)
library(shiny)
library(shinydashboard)
library(dplyr)
library(readr)
library(sf)
library(cartogram)
library(ggplot2)
library(rnaturalearth)
library(rnaturalearthdata)
library(countrycode)   

# =============================
#         UI
# =============================
ui <- dashboardPage(
  dashboardHeader(title = "Country Representation Rankings"),
  dashboardSidebar(
    sidebarMenu(
      menuItem("Cartogram", tabName = "cartogramTab", icon = icon("globe"))
    ),
    # User input: which representation index to display
    selectInput(
      inputId = "indexChoice",
      label = "Select Representation Index:",
      choices = c("Overall", "RepresentationGap", "Ethnicity",
                  "Gender", "Religion", "Language"),
      selected = "Overall"
    )
  ),
  dashboardBody(
    tabItems(
      tabItem(
        tabName = "cartogramTab",
        fluidRow(
          box(
            width = 12,
            plotOutput("cartogramPlot", height = "600px")
          )
        )
      )
    )
  )
)

# =============================
#        SERVER
# =============================
server <- function(input, output, session) {
  
  # ---- Read CSV data and create ISO3 codes ----
  rankings_data <- reactive({
    read_csv("CountryRepresentationRankings.csv") %>%
      # Use countrycode to convert the country names to ISO3 codes
      mutate(iso_a3 = countrycode(Country, origin = "country.name", destination = "iso3c"))
  })
  
  # ---- Read/prepare world map shapefile ----
  world_sf <- reactive({
    ne_countries(scale = "medium", returnclass = "sf") %>%
      select(name, iso_a3, pop_est, geometry) %>%  # includes iso_a3 for merging
      st_transform(crs = "ESRI:54009")             # projected coordinate system
  })
  
  # ---- Create cartogram ----
  cartogram_sf <- reactive({
    # Merge your CSV data (for coloring) with Natural Earth polygons via ISO3
    merged_sf <- world_sf() %>%
      left_join(rankings_data(), by = "iso_a3")
    merged_sf <- merged_sf[!is.na(merged_sf$Overall),]
    
    # You can choose which variable to use for the cartogram distortion.
    # If you want to size by pop_est, use weight = "pop_est"
    #cartogram_cont(
      #merged_sf,
      #weight = "pop_est",
      #prepare = TRUE)
    #return( cartogram_dorling( merged_sf, weight = "pop_est" ))  
    return( merged_sf )
  })
  
  # ---- Plot output ----
  output$cartogramPlot <- renderPlot({
    req(input$indexChoice)
    
    index_col <- input$indexChoice
    plot_data <- cartogram_sf()
    
    ggplot(plot_data) +
      geom_sf(aes_string(fill = index_col), color = "grey20", size = 0.1) +
      scale_fill_viridis_c(option = "D", na.value = "white") + 
      theme_minimal(base_size = 14) +
      labs(
        fill = paste(index_col, "Index"),
        title = "Country Representation Rankings",
        subtitle = "Map Colored by Selected Representation Index",
        caption = "Source: Global Leadership Project (GLP) & Natural Earth"
      ) +
      theme(
        plot.title = element_text(face = "bold"),
        axis.text = element_blank(),
        axis.ticks = element_blank()
      )
  })
}

# =============================
#   Launch the Shiny App
# =============================
shinyApp(ui = ui, server = server)