Update app.R

app.R

@@ -1,58 +1,227 @@
-library(shiny)
-library(bslib)
-library(dplyr)
-library(ggplot2)
+{
+  # ====================================================================
+  # app.R – interactive demo for the DescriptiveRepresentation calculator
+  # Upload this single file to a Shiny‑typed Hugging Face Space.
+  # ====================================================================
+  
+  # install.packages( "~/Documents/DescriptiveRepresentationCalculator-software/DescriptiveRepresentationCalculator", repos = NULL, type = "source",force = F)
+  options(error = NULL)
 
-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)
-    ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
-  ),
-  plotOutput("scatter")
-)
-
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
+  # ====================================================================
+  # app.R – interactive demo for the Descriptive Representation Viewer
+  # Upload this single file to a Shiny‑typed Hugging Face Space.
+  # ====================================================================
+  
+  options(error = NULL)
+  suppressPackageStartupMessages({
+    library(shiny)
+    library(ggplot2)
+    library(tidyr)
+    library(scales)
   })
-
-  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$smooth) geom_smooth()
+  
+  # --------------------------------------------------------------------
+  # 1.  Calculator functions (verbatim from Gerring, Jerzak & Öncel 2024)
+  # --------------------------------------------------------------------
+  library( DescriptiveRepresentationCalculator )
+  
+  # --------------------------------------------------------------------
+  # 2.  Shiny user interface
+  # --------------------------------------------------------------------
+  ui <- fluidPage(
+    # ---- Custom title block ---------------------------------------------------
+    tags$div(
+      style = "margin-top: 20px;",
+      tags$h2("Descriptive Representation Viewer"),
+      tags$p(
+        tags$a(href = "https://globalleadershipproject.net/",
+               tags$span("🔗 GlobalLeadershipProject.net"))
+      )
+    ),
+    
+    sidebarLayout(
+      sidebarPanel(
+        numericInput("bodyN", "Size of the political body (N):",
+                     value = 50, min = 1, step = 1),
+        
+        sliderInput("ngroups", "Number of population groups (K):",
+                    min = 2, max = 5, value = 3, step = 1),
+        
+        uiOutput("popShareInputs"),   # K – 1 numeric inputs + note
+        
+        checkboxInput("hasBody", "I have the body’s member counts", FALSE),
+        
+        conditionalPanel(
+          "input.hasBody == true",
+          uiOutput("bodyCountInputs")
+        ),
+        
+        actionButton("go", "Compute", class = "btn-primary")
+      ),
+      
+      mainPanel(
+        fluidRow(
+          column(4, verbatimTextOutput("expBox")),
+          column(4, verbatimTextOutput("obsBox")),
+          column(4, verbatimTextOutput("relBox"))
+        ),
+        hr(),
+        plotOutput("sharePlot", height = "400px"),
+        hr(),
+        helpText(
+          "Indices are based on the Rose Index (a = –0.5, b = 1). ",
+          "Expected values follow Gerring, Jerzak & Öncel (2024) ",
+          tags$a("[PDF]",
+                 href = "https://www.cambridge.org/core/services/aop-cambridge-core/"
+                 |> paste0("content/view/7EAEA1CA4C553AB9D76054D1FA9C0840/",
+                           "S0003055423000680a.pdf/the-composition-of-",
+                           "descriptive-representation.pdf"),
+                 target = "_blank")
         )
-
-      if (input$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$by_species
+      )
+    )
+  )
+  
+  # --------------------------------------------------------------------
+  # 3.  Server logic
+  # --------------------------------------------------------------------
+  server <- function(input, output, session) {
+    
+    # ----- Dynamic numericInputs for the first K‑1 shares ----------------------
+    output$popShareInputs <- renderUI({
+      n <- input$ngroups
+      if (n < 2) return()
+      tagList(
+        lapply(seq_len(n - 1), function(i) {
+          numericInput(
+            inputId  = paste0("share", i),
+            label    = paste("Share of group", LETTERS[i]),
+            value    = round(1 / n, 3),
+            min      = 0, max = 1, step = 0.001
+          )
+        }),
+        tags$p(
+          sprintf("The share of group %s is automatically set to 1 − (sum of the others).",
+                  LETTERS[n]),
+          style = "font-style: italic;"
+        )
+      )
+    })
+    
+    # ----- Dynamic numericInputs for group‑wise body counts --------------------
+    output$bodyCountInputs <- renderUI({
+      n <- input$ngroups
+      lapply(seq_len(n), function(i){
+        numericInput(
+          inputId = paste0("count", i),
+          label   = paste("Members of group", LETTERS[i], "in body"),
+          value   = 0, min = 0, step = 1
         )
+      })
+    })
+    
+    # ----- Helper producing the full K‑vector of population shares -------------
+    popShares <- eventReactive(input$go, {
+      n <- input$ngroups
+      shares_first <- sapply(seq_len(n - 1),
+                             function(i) input[[paste0("share", i)]])
+      if (anyNA(shares_first) || any(shares_first < 0)) {
+        showNotification("All shares must be non‑negative numbers.", type = "error")
+        return(NULL)
       }
-
-      p
-    },
-    res = 100
-  )
-}
-
-shinyApp(ui, server)
+      remainder <- 1 - sum(shares_first)
+      if (remainder < -1e-6) {
+        showNotification("The entered shares exceed 1. Reduce them so that K‑1 shares "
+                         |> paste("sum to at most 1."), type = "error")
+        return(NULL)
+      }
+      shares <- c(shares_first, max(remainder, 0))
+      setNames(shares, LETTERS[seq_len(n)])
+    })
+    
+    # ----- Helper reading body counts into a named vector ----------------------
+    bodyCounts <- reactive({
+      req(input$hasBody)
+      n <- input$ngroups
+      counts <- sapply(seq_len(n), function(i) input[[paste0("count", i)]])
+      if (anyNA(counts) || any(counts < 0)) {
+        showNotification("Body counts must be non‑negative integers.", type = "error")
+        return(NULL)
+      }
+      setNames(counts, LETTERS[seq_len(n)])
+    })
+    
+    # ----- Main computation on “Compute” --------------------------------------
+    observeEvent(input$go, {
+      validate(need(!is.null(popShares()), "Fix population shares first."))
+      
+      # Expected representation
+      expVal <- ExpectedRepresentation(popShares(), input$bodyN)
+      output$expBox <- renderText(sprintf("Expected representation:\n%.3f", expVal))
+      
+      # Observed / relative representation (if counts supplied)
+      if (input$hasBody) {
+        validate(need(!is.null(bodyCounts()), "Enter valid body counts."))
+        counts <- bodyCounts()
+        bodyTotal <- sum(counts)
+        if (bodyTotal == 0) {
+          showNotification("Total body member count cannot be zero.", type = "error")
+          output$obsBox <- renderText("Observed representation:\n—")
+          output$relBox <- renderText("Relative representation:\n—")
+          return()
+        }
+        if (bodyTotal != input$bodyN) {
+          showNotification(
+            sprintf("Sum of counts (%d) differs from N (%d). We use the counts.",
+                    bodyTotal, input$bodyN),
+            type = "warning", duration = 7
+          )
+        }
+        bodyShares <- counts / bodyTotal
+        obsVal <- ObservedRepresentation(NULL, popShares(),
+                                         BodyShares = bodyShares)
+        relVal <- RelativeRepresentation(obsVal, expVal)
+        output$obsBox <- renderText(sprintf("Observed representation:\n%.3f", obsVal))
+        output$relBox <- renderText(sprintf("Relative representation:\n%.3f", relVal))
+      } else {
+        output$obsBox <- renderText("Observed representation:\n—")
+        output$relBox <- renderText("Relative representation:\n—")
+      }
+    }, ignoreNULL = TRUE)
+    
+    # ----- Bar plot ------------------------------------------------------------
+    output$sharePlot <- renderPlot({
+      req(popShares())
+      
+      # Body shares (only if counts provided)
+      if (input$hasBody && !is.null(bodyCounts())) {
+        counts <- bodyCounts()
+        bodyShares <- counts / sum(counts)
+      } else {
+        bodyShares <- rep(NA_real_, length(popShares()))
+      }
+      
+      df <- data.frame(
+        Group      = factor(names(popShares()), levels = names(popShares())),
+        Population = as.numeric(popShares()),
+        Body       = as.numeric(bodyShares)
+      )
+      
+      df_long <- pivot_longer(df, -Group, names_to = "Type", values_to = "Share")
+      
+      ggplot(df_long, aes(Group, Share, fill = Type)) +
+        geom_col(position = position_dodge(width = 0.6),
+                 width = 0.55, na.rm = TRUE) +
+        scale_y_continuous(labels = percent_format(accuracy = 1)) +
+        scale_fill_manual(values = c(Population = "grey60", Body = "steelblue")) +
+        labs(x = NULL, y = "Share", fill = NULL) +
+        theme_minimal(base_size = 14) +
+        theme(legend.position = "bottom")
+    })
+  }
+  
+  # --------------------------------------------------------------------
+  # 4.  Launch the app
+  # --------------------------------------------------------------------
+  shinyApp(ui, server)
+}