Update app.R

app.R

@@ -1,8 +1,6 @@
-# =============================
-#  Global options & libraries
-# =============================
+options(error = NULL)
 library(shiny)
-library(shinydashboard)   # or bslib / thematic, if desired
+library(shinydashboard)
 library(dplyr)
 library(readr)
 library(sf)
@@ -10,6 +8,7 @@ library(cartogram)
 library(ggplot2)
 library(rnaturalearth)
 library(rnaturalearthdata)
+library(countrycode)   
 
 # =============================
 #         UI
@@ -24,7 +23,7 @@ ui <- dashboardPage(
     selectInput(
       inputId = "indexChoice",
       label = "Select Representation Index:",
-      choices = c("Overall", "Representation Gap", "Ethnicity",
+      choices = c("Overall", "RepresentationGap", "Ethnicity",
                   "Gender", "Religion", "Language"),
       selected = "Overall"
     )
@@ -49,45 +48,44 @@ ui <- dashboardPage(
 # =============================
 server <- function(input, output, session) {
   
-  # ---- Read CSV data ----
-  # Modify the path to your CSV as needed
+  # ---- Read CSV data and create ISO3 codes ----
   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
+      # 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({
-    # Download a simple polygons set
     ne_countries(scale = "medium", returnclass = "sf") %>%
-      select(name, iso_a3, geometry) # keep only relevant columns
+      select(name, iso_a3, pop_est, geometry) %>%  # includes iso_a3 for merging
+      st_transform(crs = "ESRI:54009")             # projected coordinate system
   })
   
-  # ---- Join data and create cartogram ----
+  # ---- Create cartogram ----
   cartogram_sf <- reactive({
-    # Join your data on "name"
+    # Merge your CSV data (for coloring) with Natural Earth polygons via ISO3
     merged_sf <- world_sf() %>%
-      left_join(rankings_data(), by = "name") 
+      left_join(rankings_data(), by = "iso_a3")
+    merged_sf <- merged_sf[!is.na(merged_sf$Overall),]
     
-    # 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)
+    # 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)
     
-    # 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") + 
@@ -95,8 +93,8 @@ server <- function(input, output, session) {
       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)"
+        subtitle = "Cartogram sized by pop_est, colored by selected Index",
+        caption = "Source: Global Leadership Project (GLP) & Natural Earth"
       ) +
       theme(
         plot.title = element_text(face = "bold"),