Update app.R

app.R

@@ -8,7 +8,7 @@ library(cartogram)
 library(ggplot2)
 library(rnaturalearth)
 library(rnaturalearthdata)
-library(countrycode)   
+library(countrycode)
 
 # =============================
 #         UI
@@ -19,7 +19,6 @@ ui <- dashboardPage(
     sidebarMenu(
       menuItem("Cartogram", tabName = "cartogramTab", icon = icon("globe"))
     ),
-    # User input: which representation index to display
     selectInput(
       inputId = "indexChoice",
       label = "Select Representation Index:",
@@ -35,7 +34,7 @@ ui <- dashboardPage(
         fluidRow(
           box(
             width = 12,
-            plotOutput("cartogramPlot", height = "600px")
+            div(style = "height: 80vh;", plotOutput("cartogramPlot", height = "100%"))
           )
         )
       )
@@ -51,32 +50,22 @@ 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
+      select(name, iso_a3, pop_est, geometry) %>%
+      st_transform(crs = "ESRI:54009")  # Mollweide projection
   })
   
-  # ---- Create cartogram ----
+  # ---- Create cartogram (currently a regular map) ----
   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 )
+    return(merged_sf)  # Regular map; cartogram functions are commented out
   })
   
   # ---- Plot output ----
@@ -88,8 +77,9 @@ server <- function(input, output, session) {
     
     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) +
+      scale_fill_viridis_c(option = "D", na.value = "white") +
+      coord_sf(expand = FALSE) +
+      theme_void(base_size = 14) +
       labs(
         fill = paste(index_col, "Index"),
         title = "Country Representation Rankings",
@@ -97,9 +87,12 @@ server <- function(input, output, session) {
         caption = "Source: Global Leadership Project (GLP) & Natural Earth"
       ) +
       theme(
-        plot.title = element_text(face = "bold"),
-        axis.text = element_blank(),
-        axis.ticks = element_blank()
+        plot.title = element_text(face = "bold", hjust = 0.5),
+        plot.subtitle = element_text(hjust = 0.5),
+        plot.caption = element_text(hjust = 1),
+        plot.margin = unit(c(0, 0, 0, 0), "cm"),
+        legend.position = "bottom",
+        legend.direction = "horizontal"
       )
   })
 }
@@ -107,4 +100,4 @@ server <- function(input, output, session) {
 # =============================
 #   Launch the Shiny App
 # =============================
-shinyApp(ui = ui, server = server)
+shinyApp(ui = ui, server = server)