app.R

# 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
      )
    ),
    
    # -- 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")
            )
          )
        )
      )
    )
  )
  
  # ------------------------------
  # 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)
      }
    )
    
  }
  
  # ------------------------------
  # 5. Run the App
  # ------------------------------
  shinyApp(ui = ui, server = server)
  
}