Update app.R

app.R

@@ -1,58 +1,145 @@
+# install.packages(c("shiny", "dplyr", "ggplot2", "DT"))
+
 library(shiny)
-library(bslib)
 library(dplyr)
 library(ggplot2)
+library(DT)
+
+ui <- fluidPage(
+  titlePanel("Country Representation Rankings Explorer"),
 
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
-
-ui <- page_sidebar(
-  theme = bs_theme(bootswatch = "minty"),
-  title = "Penguins explorer",
-  sidebar = sidebar(
-    varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-    varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-    checkboxGroupInput("species", "Filter by species",
-      choices = unique(df$Species), selected = unique(df$Species)
+  sidebarLayout(
+    sidebarPanel(
+      h4("Controls"),
+      selectInput(
+        inputId = "metric",
+        label = "Select a Representation Metric:",
+        choices = c(
+          "Overall", 
+          "Representation Gap", 
+          "Ethnicity", 
+          "Gender", 
+          "Religion", 
+          "Language"
+        ),
+        selected = "Overall"
+      ),
+      checkboxInput(
+        inputId = "sort_desc",
+        label = "Sort in descending order",
+        value = TRUE
+      ),
+      hr(),
+      sliderInput(
+        inputId = "top_n",
+        label = "Number of Countries to Display in Plot:",
+        min = 5,
+        max = 50,
+        value = 10,
+        step = 1
+      )
     ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
-  ),
-  plotOutput("scatter")
+
+    mainPanel(
+      tabsetPanel(
+        tabPanel(
+          title = "Plot",
+          plotOutput("bar_plot", height = "600px")
+        ),
+        tabPanel(
+          title = "Data Table",
+          DTOutput("data_table")
+        ),
+        tabPanel(
+          title = "Summary",
+          verbatimTextOutput("summary_text")
+        )
+      )
+    )
+  )
 )
 
 server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
-  })
 
-  output$scatter <- renderPlot(
-    {
-      p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
-        theme_light() +
-        list(
-          theme(legend.position = "bottom"),
-          if (input$by_species) aes(color = Species),
-          geom_point(),
-          if (input$smooth) geom_smooth()
-        )
+  # Load the data (adjust the path as necessary)
+  data_raw <- reactive({
+    read.csv("CountryRepresentationRankings.csv", stringsAsFactors = FALSE)
+  })
+  
+  # Reactively prepare the data for plotting and table display
+  data_prepared <- reactive({
+    req(data_raw())
+    # Convert to tibble for convenience
+    df <- tibble::as_tibble(data_raw())
+    
+    # Ensure the selected metric is numeric for plotting
+    # (This is typically already numeric, but we can do a safe check.)
+    # Also handle potential issues with missing values.
+    df <- df %>%
+      mutate(
+        across(all_of(input$metric), as.numeric)
+      ) %>%
+      filter(!is.na(.data[[input$metric]]))
+    
+    # Sort ascending or descending
+    if (input$sort_desc) {
+      df <- df %>% arrange(desc(.data[[input$metric]]))
+    } else {
+      df <- df %>% arrange(.data[[input$metric]])
+    }
+    
+    # Keep only the top N rows for plotting
+    df <- df %>% slice_head(n = input$top_n)
+    
+    df
+  })
 
-      if (input$show_margins) {
-        margin_type <- if (input$by_species) "density" else "histogram"
-        p <- p |> ggExtra::ggMarginal(
-          type = margin_type, margins = "both",
-          size = 8, groupColour = input$by_species, groupFill = input$by_species
-        )
-      }
+  # Render bar plot of the selected metric for top N countries
+  output$bar_plot <- renderPlot({
+    df <- data_prepared()
+    
+    # Convert Country to factor for a nice ordered plot
+    df <- df %>%
+      mutate(Country = factor(Country, levels = Country))
+    
+    ggplot(df, aes(x = Country, y = .data[[input$metric]])) +
+      geom_col(fill = "steelblue") +
+      coord_flip() +
+      labs(
+        x = "Country",
+        y = paste0(input$metric, " Index"),
+        title = paste("Top", input$top_n, "Countries by", input$metric)
+      ) +
+      theme_minimal(base_size = 14)
+  })
+  
+  # Render data table of the entire dataset
+  output$data_table <- renderDT({
+    req(data_raw())
+    datatable(
+      data_raw(),
+      options = list(pageLength = 10, autoWidth = TRUE),
+      filter = "top",
+      rownames = FALSE
+    )
+  })
 
-      p
-    },
-    res = 100
-  )
+  # Render summary statistics text for the selected metric
+  output$summary_text <- renderPrint({
+    df <- data_raw()
+    req(df)
+    # Convert the chosen metric to numeric if needed
+    df[[input$metric]] <- suppressWarnings(as.numeric(df[[input$metric]]))
+    
+    valid_metric <- df[[input$metric]][!is.na(df[[input$metric]])]
+    if (length(valid_metric) == 0) {
+      cat("No valid numeric data for the selected metric.")
+    } else {
+      summary_stats <- summary(valid_metric)
+      cat("Summary of", input$metric, "across all countries:\n")
+      print(summary_stats)
+    }
+  })
 }
 
-shinyApp(ui, server)
+shinyApp(ui = ui, server = server)