server.R

server <- function(input, output) {
  
  # Observe changes to region selection
  observeEvent(input$regions, {
    if (input$regions == "Global") {
      # Reset the selected country and display global trend when "Global" is chosen
      selected_country(NULL)
    } else {
      # Reset the selected country when any specific region is chosen
      selected_country(NULL)
    }
  })
  
  ## Map ----
  
  # Reactive expression to filter the map data
  map_data <- reactive({
    filtered_map_data <- vdem_data[vdem_data$year == input$year, ]
    selected_map_data <- filtered_map_data[, c("country_name", input$indicator, "geometry"), drop = FALSE]
    return(selected_map_data)
    })
  
  # Create labels for map
  labels <- reactive({
    sprintf(
      "<strong>%s</strong><br/>%s: %g",
      map_data()$country_name,
      names(vars)[which(vars == input$indicator)],
      map_data()[[input$indicator]]
    ) |> lapply(htmltools::HTML)
  })
  
  # Reactive value for selected country
  selected_country <- reactiveVal(NULL)
  
  # Update the selected country when the map is clicked
  onClick <- function(click_data) {
    clicked_country <- click_data$id
    selected_country(clicked_country)
    }
  
  # Call map module
  callModule(countryMapModule, "map1", 
             map_data = map_data, 
             labels = labels, 
             indicator = reactive(input$indicator),
             onClick = onClick)

  ## Line Chart ----
  
  # Reactive function to filter data for line chart
  line_chart_data <- reactive({
    
    if (is.null(selected_country()) && input$regions == "Global") {
      # Global selected and no country selected, show global average
      line_chart_data <- non_spatial_vdem |>
        group_by(year) |>
        summarize(yvar = round(mean(!!sym(input$indicator), na.rm = TRUE), 3))
      
      title = "Global Trend" 
      
    } else if (!is.null(selected_country())) {
      # Country selected
      line_chart_data <- non_spatial_vdem |>
        filter(country_name == selected_country()) |>
        select(year, yvar = !!sym(input$indicator))
      
      title = selected_country() 
      
    } else {
      # Region selected, show trends for countries in region
      line_chart_data <- non_spatial_vdem |>
        filter(region == input$regions) |>
        group_by(year) |>
        summarize(yvar = round(mean(!!sym(input$indicator), na.rm = TRUE), 3))
      
      title = input$regions
    }
    
    list(data = line_chart_data, title = title)

  })  

  # Determine y-axis range based on checkbox input
  y_axis_range <- reactive({
    if (input$fixed_axis) {
      return(list(range = c(0, 1)))
    } else {
      return(list(autorange = TRUE))
    }
  })
  
  # Call plotly line chart module
  callModule(plotlyLineChartModule, "linechart1",
             data = reactive({ line_chart_data()$data }),
             selected_year = reactive( input$year ),
             title = reactive({ line_chart_data()$title }),
             x_title = "Year",
             y_title = reactive(names(vars[which(vars == input$indicator)])), 
             y_range = reactive({ y_axis_range() })
  )
  
  ## Bar Chart ----
  
  # Reactive function to filter data for bar chart
  # Note: is some of this redundant to wrangling for line chart?
  # Think about redoing this so that all scenarios include a highlight column
  
  bar_chart_data <- reactive({

    if (is.null(selected_country()) && input$regions == "Global") {
      # Global selected and no country selected, show global average
      bar_chart_data <- non_spatial_vdem |>
        filter(year == input$year, !is.na(region)) |>  # Exclude rows where region is NA
        group_by(region) |>
        summarize(xvar = round(mean(!!sym(input$indicator), na.rm = TRUE), 3)) |>
        mutate(yvar = region) |>
        arrange(xvar) 
      
    }  else if (!is.null(selected_country())) {
      # Find the region of the selected country
      ctry_from_region <- non_spatial_vdem |>
        filter(country_name == selected_country()) |>
        pull(region) |>
        unique()
      
      # Filter data to include only countries from the selected region
      bar_chart_data <- non_spatial_vdem |>
        filter(region == ctry_from_region, 
               year == input$year) |>
        mutate(xvar = !!sym(input$indicator),
               yvar = country_name,
               # Add a column to highlight the selected country
               highlight = country_name == selected_country()) |>
        arrange(xvar)
      
    } else {
      # Region selected, show values for countries in region
      bar_chart_data <- non_spatial_vdem |>
        filter(region == input$regions, 
               year == input$year) |>
        mutate(xvar = !!sym(input$indicator),
               yvar = country_name) |>
        arrange(xvar)
    }

    bar_chart_data

  })
  
  # Call plotly bar chart module
  
  callModule(plotlyBarChartModule, "barchart1", 
             data = reactive({ bar_chart_data() }), 
             x_title = reactive(names(vars[which(vars == input$indicator)])),
             highlight_col = reactive("highlight"),
             color1 = magma(256)[128], #128
             color2 = magma(256)[192]) #192

  ## Scatter Plot ----
  
  # Think about redoing this so that all scenarios include a highlight column
    
  # Reactive expression for scatter plot data
  scatter_plot_data <- reactive({
    
    # Filter by selected year
    filtered_plot_data <- non_spatial_vdem |>
      filter(year == input$year)
    
    # Scenario 1: Global view or no country selected
    if (input$regions == "Global" || !is.null(selected_country())) {
      plot_data <- filtered_plot_data 
    # Scenario 2: Region selected
    } else {
      plot_data <- filtered_plot_data |>
        filter(region == input$regions)
    }
    
    plot_data |>
      select(country_name, 
             yvar = .data[[input$indicator]], 
             xvar = e_gdppc, 
             gdp_pc) |>
      drop_na()
  })
  
  # Call plotly scatter plot module
  callModule(plotlyScatterPlotModule, 
             "scatterplot1", 
             data = scatter_plot_data, 
             x_title = "GDP per capita",
             y_title = reactive(names(vars[which(vars == input$indicator)])),
             color1 = magma(256)[192], #192
             color2 = magma(256)[128],
             selected_country = selected_country)
  }