Update app.R

app.R

@@ -1,145 +1,122 @@
-# install.packages(c("shiny", "dplyr", "ggplot2", "DT"))
+# app.R
 
+# Load necessary libraries
 library(shiny)
-library(dplyr)
-library(ggplot2)
-library(data.table)
+library(readr)   # for reading CSVs
+library(dplyr)   # for data manipulation
+library(ggplot2) # for plotting
+library(DT)      # for rendering data tables interactively
 
+# Define UI
 ui <- fluidPage(
-  titlePanel("Country Representation Rankings Explorer"),
-
+  
+  # Title of the application
+  titlePanel("Country Representation Explorer"),
+  
+  # Sidebar layout
   sidebarLayout(
     sidebarPanel(
-      h4("Controls"),
+      # Input: Select which representation metric to visualize
       selectInput(
         inputId = "metric",
-        label = "Select a Representation Metric:",
-        choices = c(
-          "Overall", 
-          "Representation Gap", 
-          "Ethnicity", 
-          "Gender", 
-          "Religion", 
-          "Language"
-        ),
+        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 = "sort_desc",
-        label = "Sort in descending order",
+        inputId = "showDataTable",
+        label = "Show data table of filtered results",
         value = TRUE
-      ),
-      hr(),
-      sliderInput(
-        inputId = "top_n",
-        label = "Number of Countries to Display in Plot:",
-        min = 5,
-        max = 50,
-        value = 10,
-        step = 1
       )
     ),
-
+    
+    # Main panel to display outputs
     mainPanel(
-      tabsetPanel(
-        tabPanel(
-          title = "Plot",
-          plotOutput("bar_plot", height = "600px")
-        ),
-        tabPanel(
-          title = "Data Table",
-          DTOutput("data_table")
-        ),
-        tabPanel(
-          title = "Summary",
-          verbatimTextOutput("summary_text")
-        )
+      # Output: Bar plot
+      plotOutput("repPlot", height = "600px"),
+      
+      # Output: Data table (shown only when checkbox is TRUE)
+      conditionalPanel(
+        condition = "input.showDataTable == true",
+        DTOutput("tableOut")
       )
     )
   )
 )
 
+# Define server logic
 server <- function(input, output, session) {
-
-  # Load the data (adjust the path as necessary)
-  data_raw <- reactive({
-    read.csv("CountryRepresentationRankings.csv", stringsAsFactors = FALSE)
+  
+  # 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)
   })
   
-  # 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$metric]])
-    }
+  # Reactive subset of the data based on the metric range selected
+  filtered_data <- reactive({
+    req(input$metricRange)
+    req(input$metric)
     
-    # Keep only the top N rows for plotting
-    df <- df %>% slice_head(n = input$top_n)
+    # Ensure data is loaded
+    data <- full_data()
     
-    df
+    # 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$metricRange[2]
+      )
   })
-
-  # Render bar plot of the selected metric for top N countries
-  output$bar_plot <- renderPlot({
-    df <- data_prepared()
+  
+  # Render the bar plot
+  output$repPlot <- renderPlot({
+    data_for_plot <- filtered_data()
     
-    # Convert Country to factor for a nice ordered plot
-    df <- df %>%
-      mutate(Country = factor(Country, levels = Country))
+    # Proceed only if there's data to plot
+    validate(
+      need(nrow(data_for_plot) > 0, "No data available within this range.")
+    )
     
-    ggplot(df, aes(x = Country, y = .data[[input$metric]])) +
+    # Order countries by the chosen metric
+    ggplot(data_for_plot, aes(
+      x = reorder(Country, get(input$metric)), 
+      y = get(input$metric)
+    )) +
       geom_col(fill = "steelblue") +
-      coord_flip() +
+      coord_flip() + # Flip coordinates for easier reading of country names
       labs(
         x = "Country",
-        y = paste0(input$metric, " Index"),
-        title = paste("Top", input$top_n, "Countries by", input$metric)
+        y = paste(input$metric, "Index"),
+        title = paste("Countries by", input$metric, "Representation")
       ) +
       theme_minimal(base_size = 14)
   })
   
-  # Render data table of the entire dataset
-  output$data_table <- renderDT({
-    req(data_raw())
+  # Render the data table (only if checkbox is selected)
+  output$tableOut <- renderDT({
+    data_for_table <- filtered_data()
     datatable(
-      data_raw(),
+      data_for_table,
       options = list(pageLength = 10, autoWidth = TRUE),
-      filter = "top",
       rownames = FALSE
     )
   })
-
-  # 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 = ui, server = server)
+# Create Shiny app
+shinyApp(ui, server)