Update app.R

app.R

@@ -1,122 +1,112 @@
-# app.R
-
-# Load necessary libraries
+# =============================
+#  Global options & libraries
+# =============================
 library(shiny)
-library(readr)   # for reading CSVs
-library(dplyr)   # for data manipulation
-library(ggplot2) # for plotting
-library(DT)      # for rendering data tables interactively
+library(shinydashboard)   # or bslib / thematic, if desired
+library(dplyr)
+library(readr)
+library(sf)
+library(cartogram)
+library(ggplot2)
+library(rnaturalearth)
+library(rnaturalearthdata)
 
-# 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
-      )
+# =============================
+#         UI
+# =============================
+ui <- dashboardPage(
+  dashboardHeader(title = "Country Representation Rankings"),
+  dashboardSidebar(
+    sidebarMenu(
+      menuItem("Cartogram", tabName = "cartogramTab", icon = icon("globe"))
     ),
-    
-    # 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")
+    # 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")
+          )
+        )
       )
     )
   )
 )
 
-# Define server logic
+# =============================
+#        SERVER
+# =============================
 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)
+  # ---- 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
   })
   
-  # 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()
+  # ---- 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") 
     
-    # 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]
-      )
+    # 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)
   })
   
-  # Render the bar plot
-  output$repPlot <- renderPlot({
-    data_for_plot <- filtered_data()
+  # ---- Plot output ----
+  output$cartogramPlot <- renderPlot({
+    req(input$indexChoice)
     
-    # Proceed only if there's data to plot
-    validate(
-      need(nrow(data_for_plot) > 0, "No data available within this range.")
-    )
+    # The user’s chosen index to display
+    index_col <- input$indexChoice
+    
+    # Prepare the cartogram for plotting
+    plot_data <- cartogram_sf()
     
-    # 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
+    # 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(
-        x = "Country",
-        y = paste(input$metric, "Index"),
-        title = paste("Countries by", input$metric, "Representation")
+        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_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
-    )
+      theme(
+        plot.title = element_text(face = "bold"),
+        axis.text = element_blank(),
+        axis.ticks = element_blank()
+      )
   })
 }
 
-# Create Shiny app
-shinyApp(ui, server)
+# =============================
+#   Launch the Shiny App
+# =============================
+shinyApp(ui = ui, server = server)