Update app.R

app.R

@@ -1,58 +1,281 @@
-library(shiny)
-library(bslib)
-library(dplyr)
-library(ggplot2)
-
-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)
+# setwd("~/Downloads")
+{
+  # app.R
+  options(error = NULL)
+  
+  # ------------------------------
+  # 1. Load Packages
+  # ------------------------------
+  library(shiny)
+  library(shinydashboard)
+  library(leaflet)
+  library(raster)
+  library(DT)
+  library(readr)
+  library(dplyr)    # For data manipulation
+  library(ggplot2) # For histogram
+  library(RColorBrewer)
+  
+  # ------------------------------
+  # 2. Data & Config
+  # ------------------------------
+  
+  # Define time periods corresponding to each band in the GeoTIFF
+  time_periods <- c("1990–1992", "1993–1995", "1996–1998", "1999–2001", "2002–2004",
+                    "2005–2007", "2008–2010", "2011–2013", "2014–2016", "2017–2019")
+  
+  # Load GeoTIFF data
+  wealth_stack <- stack("wealth_map.tif")
+  
+  # Clean up out-of-range values
+  wealth_stack[wealth_stack <= 0 | wealth_stack > 1] <- NA
+  
+  # Load improvement data
+  improvement_data <- read_csv("poverty_improvement_by_state.csv")
+  
+  # ------------------------------
+  # 3. UI
+  # ------------------------------
+  
+  ui <- dashboardPage(
+    
+    # -- Header
+    dashboardHeader(title = "Africa Living Conditions Explorer"),
+    
+    # -- Sidebar
+    dashboardSidebar(
+      sidebarMenu(id = "tabs",
+                  menuItem("Wealth Map", tabName = "mapTab", icon = icon("map")),
+                  menuItem("Improvement Data", tabName = "improvementTab", icon = icon("table"))
+      ),
+      # Show inputs only for the map tab
+      conditionalPanel(
+        condition = "input.tabs == 'mapTab'",
+        br(),
+        selectInput("time_period", "Select Time Period:", 
+                    choices = time_periods,
+                    selected = time_periods[1]  # Load the first time period by default
+        ),
+        selectInput("color_palette", "Select Color Palette:",
+                    choices = c("Viridis" = "viridis", 
+                                "Plasma" = "plasma", 
+                                "Magma"  = "magma",
+                                "Inferno"= "inferno",
+                                "Spectral (Brewer)" = "Spectral"),
+                    selected = "viridis"),
+        sliderInput("opacity", "Map Opacity:", min = 0.2, max = 1, value = 0.8, step = 0.1)
+        # Removed the 'Update Map' button
+      )
     ),
-    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)
-  })
-
-  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$smooth) geom_smooth()
+    
+    # -- Body
+    dashboardBody(
+      tabItems(
+        
+        # ---------- MAP TAB ----------
+        tabItem(
+          tabName = "mapTab",
+          fluidRow(
+            # Value Boxes across the top for key stats
+            valueBoxOutput("highest_iwi_vb", width = 4),
+            valueBoxOutput("lowest_iwi_vb", width = 4),
+            valueBoxOutput("avg_iwi_vb", width = 4)
+          ),
+          fluidRow(
+            # Map
+            box(
+              title = "Wealth Map of Africa", width = 8, solidHeader = TRUE, status = "primary",
+              leafletOutput("map", height = "550px")
+            ),
+            # Histogram
+            box(
+              title = "IWI Distribution (Selected Period)", width = 4, solidHeader = TRUE, status = "info",
+              plotOutput("iwi_histogram", height = "250px"),
+              p("This histogram shows the distribution of the International Wealth Index (IWI) values for the selected time period across Africa.")
+            )
+          )
+        ),
+        
+        # ---------- IMPROVEMENT DATA TAB ----------
+        tabItem(
+          tabName = "improvementTab",
+          fluidRow(
+            box(
+              width = 12, title = "Poverty Improvement by State", status = "primary", solidHeader = TRUE,
+              p("This table shows the estimated improvement in mean IWI between 1990–1992 and 2017–2019 for each province in Africa. 
+               The 'Improvement' column indicates the change in IWI over this period. You can sort or filter the table, 
+               and use the download button to export the data."),
+              downloadButton("download_data", "Download CSV", icon = icon("download")),
+              br(), br(),
+              DTOutput("improvement_table")
+            )
+          )
         )
-
-      if (input$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$by_species
+      )
+    )
+  )
+  
+  # ------------------------------
+  # 4. Server
+  # ------------------------------
+  
+  server <- function(input, output, session) {
+    
+    # ----------------------------------
+    # Reactive expression for selected raster layer
+    # ----------------------------------
+    selected_raster <- reactive({
+      req(input$time_period)
+      band_index <- which(time_periods == input$time_period)
+      wealth_stack[[band_index]]
+    })
+    
+    # ----------------------------------
+    # Custom color palette function
+    # (reactive to user-selected palette)
+    # ----------------------------------
+    color_pal <- reactive({
+      palette_choice <- switch(
+        input$color_palette,
+        "viridis" = "viridis",
+        "plasma"  = "plasma",
+        "magma"   = "magma",
+        "inferno" = "inferno",
+        # Fallback to a Brewer palette for "Spectral"
+        "Spectral" = "Spectral"
+      )
+      colorNumeric(
+        palette = palette_choice,
+        domain  = c(0, 1),
+        na.color = "transparent"
+      )
+    })
+    
+    # ----------------------------------
+    # 1. MAP OUTPUT
+    # ----------------------------------
+    output$map <- renderLeaflet({
+      leaflet() %>%
+        addProviderTiles(providers$OpenStreetMap) %>%
+        setView(lng = 20, lat = 0, zoom = 3) %>%  # Center on Africa
+        addLegend(
+          pal      = color_pal(),
+          values   = c(1, 0),
+          bins     = 2,
+          title    = "IWI",
+          position = "bottomright",
+          labFormat = labelFormat(
+            prefix   = "",
+            suffix   = "",
+            between  = " &ndash; ",
+            digits   = 3,
+            big.mark = ","
+          )
+        )
+    })
+    
+    # Any time one of the controls changes, redraw the raster
+    observe({
+      req(input$time_period, input$color_palette, input$opacity)
+      
+      leafletProxy("map") %>%
+        clearImages() %>%
+        addRasterImage(
+          selected_raster(),
+          colors  = color_pal(),
+          opacity = input$opacity,
+          project = TRUE
         )
+    })
+    
+    # ----------------------------------
+    # 2. HISTOGRAM OUTPUT
+    # ----------------------------------
+    output$iwi_histogram <- renderPlot({
+      # Extract raster values for histogram
+      r_vals <- values(selected_raster())
+      r_vals <- r_vals[!is.na(r_vals)]
+      
+      ggplot(data.frame(iwi = r_vals), aes(x = iwi)) +
+        geom_histogram(binwidth = 0.02, fill = "#2c7bb6", color = "white", alpha = 0.7) +
+        labs(x = "IWI (0 to 1)", y = "Frequency") +
+        theme_minimal(base_size = 14)
+    })
+    
+    # ----------------------------------
+    # 3. VALUE BOXES FOR KEY STATS
+    # ----------------------------------
+    # Compute stats for current raster
+    raster_stats <- reactive({
+      r_vals <- values(selected_raster())
+      r_vals <- r_vals[!is.na(r_vals)]
+      list(
+        highest = max(r_vals, na.rm = TRUE),
+        lowest  = min(r_vals, na.rm = TRUE),
+        average = mean(r_vals, na.rm = TRUE)
+      )
+    })
+    
+    # Highest IWI
+    output$highest_iwi_vb <- renderValueBox({
+      valueBox(
+        value = round(raster_stats()$highest, 3),
+        subtitle = "Highest IWI",
+        icon = icon("arrow-up"),
+        color = "green"
+      )
+    })
+    
+    # Lowest IWI
+    output$lowest_iwi_vb <- renderValueBox({
+      valueBox(
+        value = round(raster_stats()$lowest, 3),
+        subtitle = "Lowest IWI",
+        icon = icon("arrow-down"),
+        color = "red"
+      )
+    })
+    
+    # Average IWI
+    output$avg_iwi_vb <- renderValueBox({
+      valueBox(
+        value = round(raster_stats()$average, 3),
+        subtitle = "Average IWI",
+        icon = icon("balance-scale"),
+        color = "blue"
+      )
+    })
+    
+    # ----------------------------------
+    # 4. IMPROVEMENT DATA TABLE
+    # ----------------------------------
+    output$improvement_table <- renderDT({
+      datatable(
+        improvement_data,
+        filter = "top",
+        options = list(
+          scrollX = TRUE,
+          pageLength = 20,
+          autoWidth = TRUE
+        )
+      )
+    })
+    
+    # Download CSV
+    output$download_data <- downloadHandler(
+      filename = function() {
+        paste0("poverty_improvement_", Sys.Date(), ".csv")
+      },
+      content = function(file) {
+        write.csv(improvement_data, file, row.names = FALSE)
       }
-
-      p
-    },
-    res = 100
-  )
-}
-
-shinyApp(ui, server)
+    )
+    
+  }
+  
+  # ------------------------------
+  # 5. Run the App
+  # ------------------------------
+  shinyApp(ui = ui, server = server)
+  
+}