Update app.R

app.R

@@ -8,7 +8,7 @@ library(rnaturalearth)
 library(rnaturalearthdata)
 library(countrycode)
 library(ggplot2)
-library(ggiraph)  # For interactive hover tooltips
+library(ggiraph)       # <-- for interactive tooltips
 
 # =============================
 #            UI
@@ -55,7 +55,7 @@ ui <- dashboardPage(
         .sidebar-menu, .sidebar-menu li a, .sidebar-menu .fa {
           font-family: 'OCR A Extended', monospace !important;
         }
-        
+
         /* Header gradient background */
         .main-header .navbar {
           background: linear-gradient(to right, #3b6978, #204051) !important;
@@ -82,19 +82,19 @@ ui <- dashboardPage(
           border-left-color: #78cdd7 !important;
           color: #ffffff !important;
         }
-        
+
         /* Sidebar menu item icons */
         .sidebar-menu .fa {
           color: #78cdd7 !important;
         }
-        
+
         /* Sidebar menu item text */
         .sidebar-menu > li > a {
           color: #b8c7ce !important;
           font-size: 15px;
           font-weight: 500;
         }
-        
+
         /* Customize the boxes */
         .box {
           border-top: none !important;
@@ -127,7 +127,7 @@ ui <- dashboardPage(
             width = 12,
             title = strong("Global Leadership Project (GLP)"),
             solidHeader = TRUE,
-            # Use girafeOutput instead of plotOutput
+            # Instead of plotOutput, we use girafeOutput for interactive tooltips
             div(
               style = "height: 80vh; padding: 10px;",
               girafeOutput("cartogramPlot", width = "100%", height = "100%")
@@ -144,45 +144,37 @@ ui <- dashboardPage(
 # =============================
 server <- function(input, output, session) {
   
-  # 1. Custom matches for countries not recognized by default in 'countrycode'
-  custom_iso_matches <- c("Kosovo" = "XKX",
-                          "Somaliland" = "SOM")  # or any valid code you prefer
-  
-  # 2. Read CSV data and create ISO3 codes with custom matches
+  # ---- Read CSV data and create ISO3 codes ----
   rankings_data <- reactive({
-    read_csv("CountryRepresentationRankings.csv", show_col_types = FALSE) %>%
-      mutate(iso_a3 = countrycode(
-        sourcevar     = Country,
-        origin        = "country.name",
-        destination   = "iso3c",
-        custom_match  = custom_iso_matches
-      ))
+    read_csv("CountryRepresentationRankings.csv") %>%
+      mutate(iso_a3 = countrycode(Country, origin = "country.name", destination = "iso3c"))
   })
   
-  # 3. Read/prepare world map shapefile
+  # ---- Read/prepare world map shapefile ----
   world_sf <- reactive({
     ne_countries(scale = "medium", returnclass = "sf") %>%
       dplyr::select(name, iso_a3, pop_est, geometry) %>%
       st_transform(crs = "ESRI:54009")  # Mollweide projection
   })
   
-  # 4. Create the joined sf object (currently just a regular map)
+  # ---- Create cartogram (currently a regular map) ----
   cartogram_sf <- reactive({
     merged_sf <- world_sf() %>%
       left_join(rankings_data(), by = "iso_a3")
-    # Filter out rows with missing 'Overall', if you want to exclude them
+    # Filter out NA values in 'Overall' (or whichever main column) to avoid missing countries
     merged_sf <- merged_sf[!is.na(merged_sf$Overall),]
-    merged_sf
+    return(merged_sf)
   })
   
-  # 5. Render the interactive map with ggiraph
+  # ---- Plot output (using ggiraph for tooltips) ----
   output$cartogramPlot <- renderGirafe({
     req(input$indexChoice)
     
     plot_data <- cartogram_sf()
     index_col <- input$indexChoice
     
-    # Build a tooltip string: customize to your preference
+    # Build a tooltip string showing some key columns for each country.
+    # Modify this as desired to include more (or fewer) details.
     plot_data$tooltip_text <- paste0(
       "<b>Country:</b> ", plot_data$Country, "<br/>",
       "<b>Overall:</b> ", ifelse(!is.na(plot_data$Overall), plot_data$Overall, "N/A"), "<br/>",
@@ -193,21 +185,20 @@ server <- function(input, output, session) {
       "<b>Language:</b> ", ifelse(!is.na(plot_data$Language), plot_data$Language, "N/A")
     )
     
-    # Use geom_sf_interactive with aes() + get() instead of aes_string()
+    # Create a ggplot with geom_sf_interactive so we can assign tooltips
     p <- ggplot(plot_data) +
       geom_sf_interactive(
-        aes(
-          fill = get(index_col),
-          tooltip = tooltip_text,
-          data_id = iso_a3
+        aes_string(
+          fill = index_col,
+          tooltip = "tooltip_text",
+          data_id = "iso_a3"    # required for hover/click tracking
         ),
         color = "grey20",
         size = 0.1
       ) +
       scale_fill_viridis_c(option = "D", na.value = "white") +
       coord_sf(expand = FALSE) +
-      # Use a generic base_family to avoid font errors
-      theme_void(base_size = 14, base_family = "sans") +
+      theme_void(base_size = 14, base_family = "Courier New") +
       labs(
         fill = paste(index_col, "Index"),
         title = "Country Representation Rankings",
@@ -223,17 +214,14 @@ server <- function(input, output, session) {
         legend.key.width = unit(2, "cm")
       )
     
-    # Wrap the ggplot in a girafe object, including tooltip styling
+    # Wrap the ggplot in a girafe object
     girafe(
       ggobj = p,
       width_svg  = 10,
       height_svg = 6,
       options = list(
-        opts_tooltip(
-          css = "background-color: white; 
-                 font-family: 'OCR A Extended', monospace; 
-                 color: black;"
-        )
+        # You can style the tooltip background here
+        opts_tooltip(css = "background-color: white; font-family: 'OCR A Extended', monospace;")
       )
     )
   })