app.R

# =============================
#  Global options & libraries
# =============================
library(shiny)
library(shinydashboard)   # or bslib / thematic, if desired
library(dplyr)
library(readr)
library(sf)
library(cartogram)
library(ggplot2)
library(rnaturalearth)
library(rnaturalearthdata)

# =============================
#         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", "Representation Gap", "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 ----
  # Modify the path to your CSV as needed
  rankings_data <- reactive({
    read_csv("CountryRepresentationRankings.csv") %>%
      # Make sure "Country" and "Population" columns are present:
      # e.g., rename columns if your CSV differs
      rename(name = Country)  # rename to match natural earth "name" field
  })
  
  # ---- Read/prepare world map shapefile ----
  world_sf <- reactive({
    # Download a simple polygons set
    ne_countries(scale = "medium", returnclass = "sf") %>%
      select(name, iso_a3, geometry) # keep only relevant columns
  })
  
  # ---- Join data and create cartogram ----
  cartogram_sf <- reactive({
    # Join your data on "name"
    merged_sf <- world_sf() %>%
      left_join(rankings_data(), by = "name") 
    
    # cartogram_cont scales polygon area proportionally to your "Population" column
    # If you do NOT have a "Population" column, you could use "pop_est" from
    # the natural earth data or from your CSV if provided.
    cartogram_cont(merged_sf, weight = "Population", prepare = TRUE)
  })
  
  # ---- Plot output ----
  output$cartogramPlot <- renderPlot({
    req(input$indexChoice)
    
    # The user’s chosen index to display
    index_col <- input$indexChoice
    
    # Prepare the cartogram for plotting
    plot_data <- cartogram_sf()
    
    # Basic ggplot: color fill by the chosen index
    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 = "Cartogram sized by Population, colored by selected Index",
        caption = "Source: Global Leadership Project (GLP)"
      ) +
      theme(
        plot.title = element_text(face = "bold"),
        axis.text = element_blank(),
        axis.ticks = element_blank()
      )
  })
}

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