This can take ~30s to load as it runs entirely in your browser…just leave the window open until it loads

The app is best viewed in landscape mode on a mobile device.

#| standalone: true
#| viewerHeight: 1100

# Load required libraries
library(shiny)
library(ggplot2)
library(dplyr)
library(ggrepel)
library(plotly)

#-------------------------------------------
# Data Loading and Processing Functions ----
#-------------------------------------------

# Function to load presidents data
load_presidents_data <- function() {
    presidents_data <- data.frame(
      president = c(
        "Eisenhower (1957)", "Kennedy (1961)", "Johnson (1963)", "Nixon (1969)",
        "Nixon (1973)", "Ford (1974)", "Carter (1977)", "Reagan (1981)",
        "Reagan (1985)", "Bush Sr. (1989)", "Clinton (1993)", "Clinton (1997)",
        "Bush Jr. (2001)", "Bush Jr. (2005)", "Obama (2009)", "Obama (2013)",
        "Trump (2017)", "Biden (2021)", "Trump (2025)"
      ),
      inauguration_date = as.Date(c(
        "1957-01-20", "1961-01-20", "1963-11-22", "1969-01-20",
        "1973-01-20", "1974-08-09", "1977-01-20", "1981-01-20",
        "1985-01-20", "1989-01-20", "1993-01-20", "1997-01-20",
        "2001-01-20", "2005-01-20", "2009-01-20", "2013-01-20",
        "2017-01-20", "2021-01-20", "2025-01-20"
      )),
      election_date = as.Date(c(
        "1956-11-06", "1960-11-08", "1960-11-08", "1968-11-05",
        "1972-11-07", "1972-11-07", "1976-11-02", "1980-11-04",
        "1984-11-06", "1988-11-08", "1992-11-03", "1996-11-05",
        "2000-11-07", "2004-11-02", "2008-11-04", "2012-11-06",
        "2016-11-08", "2020-11-03", "2024-11-05"
      )),
      party = c(
        "Republican", "Democratic", "Democratic", "Republican",
        "Republican", "Republican", "Democratic", "Republican",
        "Republican", "Republican", "Democratic", "Democratic",
        "Republican", "Republican", "Democratic", "Democratic",
        "Republican", "Democratic", "Republican"
      ),
      stringsAsFactors = FALSE
    )

    # Convert date column to Date type
    presidents_data$inauguration_date <- as.Date(presidents_data$inauguration_date)
    presidents_data$election_date <- as.Date(presidents_data$election_date)

    # Write once for use in get_data.R
    # readr::write_csv(presidents_data, "presidents_data.csv")

    return(presidents_data)
}

# Function to load prepared data
load_data <- function() {
  # URL for the data files in GitHub
  market_data_url <- "https://raw.githubusercontent.com/jhelvy/potus-econ-scorecard/refs/heads/main/market_data.csv"

  # Load data
  tryCatch({
    market_data_raw <- read.csv(market_data_url, stringsAsFactors = FALSE)
    presidents_data <- load_presidents_data()

    # Convert date column to Date type
    market_data_raw$date <- as.Date(market_data_raw$date)
    
    # Organize market data by index
    market_data <- split(market_data_raw, market_data_raw$index_id)
    
    return(list(
      market_data = market_data,
      presidents_data = presidents_data
    ))
  }, error = function(e) {
    # Return empty data frames if loading fails
    warning("Error loading data: ", e$message)
    return(list(
      market_data = list(),
      presidents_data = data.frame()
    ))
  })
}

# Function to process market data for selected presidents and reference date
process_market_data <- function(market_data,
                                presidents_data,
                                selected_index,
                                selected_presidents,
                                reference_type,
                                party_filter,
                                days_to_show) {
  
  # Check if market data is available
  if (is.null(market_data) || length(market_data) == 0) {
    return(data.frame())
  }
  
  # Get index data
  index_data_list <- list(market_data[[selected_index]])
  names(index_data_list) <- selected_index
  
  # Filter presidents by party and selection
  filtered_presidents <- presidents_data %>%
    filter(party %in% party_filter, president %in% selected_presidents)
  
  # If no presidents selected, return empty dataframe
  if (nrow(filtered_presidents) == 0) {
    return(data.frame())
  }
  
  # Process data for each selected president and index
  result_data <- data.frame()
  
  for (pres_i in 1:nrow(filtered_presidents)) {
    pres_row <- filtered_presidents[pres_i, ]
    
    # Determine reference date based on user selection
    ref_date <- if (reference_type == "inauguration") {
      pres_row$inauguration_date
    } else {
      pres_row$election_date
    }
    
    # Process each index
    for (idx_id in names(index_data_list)) {
      index_data <- index_data_list[[idx_id]]
      
      # Find closest trading day to reference date
      closest_ref_date <- index_data %>%
        filter(date >= ref_date) %>%
        arrange(date) %>%
        slice(1) %>%
        pull(date)
      
      if (length(closest_ref_date) == 0) {
        # Skip if no suitable reference date found
        next
      }
      
      # Get the reference value
      ref_value <- index_data %>%
        filter(date == closest_ref_date) %>%
        pull(value)
      
      # Calculate days from reference and percent change
      pres_data <- index_data %>%
        filter(date >= closest_ref_date) %>%
        mutate(
          president = pres_row$president,
          party = pres_row$party,
          day = as.numeric(difftime(date, closest_ref_date, units = "days")),
          reference_value = ref_value,
          percent_change = (value / ref_value - 1) * 100
        ) %>%
        filter(day <= days_to_show)
      
      result_data <- rbind(result_data, pres_data)
    }
  }
  
  return(result_data)
}

# Function to process economic indicator data that shouldn't use percent change
process_econ_data <- function(market_data,
                              presidents_data,
                              selected_index,
                              selected_presidents,
                              reference_type,
                              party_filter,
                              days_to_show) {

  # Check if market data is available
  if (is.null(market_data) || length(market_data) == 0) {
    return(data.frame())
  }
  
  # Get index data
  index_data_list <- list(market_data[[selected_index]])
  names(index_data_list) <- selected_index
  
  # Filter presidents by party and selection
  filtered_presidents <- presidents_data %>%
    filter(party %in% party_filter, president %in% selected_presidents)
  
  # If no presidents selected, return empty dataframe
  if (nrow(filtered_presidents) == 0) {
    return(data.frame())
  }
  
  # Process data for each selected president and index
  result_data <- data.frame()
  
  for (pres_i in 1:nrow(filtered_presidents)) {
    pres_row <- filtered_presidents[pres_i, ]
    
    # Determine reference date based on user selection
    ref_date <- if (reference_type == "inauguration") {
      pres_row$inauguration_date
    } else {
      pres_row$election_date
    }
    
    # Process each index
    for (idx_id in names(index_data_list)) {
      index_data <- index_data_list[[idx_id]]
      
      # Find closest day to reference date
      closest_ref_date <- index_data %>%
        filter(date >= ref_date) %>%
        arrange(date) %>%
        slice(1) %>%
        pull(date)
      
      if (length(closest_ref_date) == 0) {
        # Skip if no suitable reference date found
        next
      }
      
      # For economic indicators, we show absolute values (not percent change)
      pres_data <- index_data %>%
        filter(date >= closest_ref_date) %>%
        mutate(
          president = pres_row$president,
          party = pres_row$party,
          day = as.numeric(difftime(date, closest_ref_date, units = "days"))
        ) %>%
        filter(day <= days_to_show)
      
      result_data <- rbind(result_data, pres_data)
    }
  }
  
  return(result_data)
}

#-------------------------------------------
# UI Definition ----
#-------------------------------------------

ui <- fluidPage(

  titlePanel(""),

  sidebarLayout(
    sidebarPanel(
      # Index selection
      radioButtons("selected_index", "Select Economic Indicator:",
                   choices = list(
                     "S&P 500" = "sp500", 
                     "Dow Jones" = "djia",
                     "NASDAQ" = "nasdaq",
                     "Unemployment Rate" = "unemployment",
                     "Inflation Rate" = "inflation"
                   ),
                   selected = "sp500"),
      
      # Baseline selection
      radioButtons("reference_date", "Reference Date:",
                   choices = list(
                     "Inauguration Day" = "inauguration", 
                     "Day Before Election" = "election"
                   ),
                   selected = "inauguration"),
      
      # Time period selection
      sliderInput("time_period", "Days to Display:",
                  min = 10, max = 365*4, value = 100, step = 10),
      
      
      # Party filter
      checkboxGroupInput("party_filter", "Filter by Party:",
                         choices = c("Democratic", "Republican"),
                         selected = c("Democratic", "Republican")),
      
      # President selection buttons
      div(
        style = "display: flex; justify-content: space-between; margin-bottom: 10px;",
        actionButton("select_all", "Select All", class = "btn-sm"),
        actionButton("deselect_all", "Deselect All", class = "btn-sm")
      ),
      
      # President checkboxes (dynamic)
      uiOutput("president_selection")
    ),
    
    # Main content
    mainPanel(
      plotlyOutput("economic_plot", height = "100%"),
      div(
        style = "display: flex; justify-content: flex-end; margin-top: 10px;",
        actionButton("export_plot", "Export Plot", class = "btn-sm"),
        actionButton("export_data", "Export Data", class = "btn-sm")
      ),
      uiOutput("export_modal")
    )
  )
)

#-------------------------------------------
# Server Logic ----
#-------------------------------------------

server <- function(input, output, session) {
  
  # Reactive values to store loaded data
  loaded_data <- reactiveVal(NULL)
  
  # Show loading message
  showModal(modalDialog(
    title = "Loading Data",
    "Loading economic data. This may take a moment...",
    footer = NULL,
    easyClose = FALSE
  ))
  
  # Load data on startup
  observe({
    # Get data
    data_list <- load_data()
    
    # Store the data
    loaded_data(data_list)
    
    # Remove loading message
    removeModal()
  })
  
  # Update default days depending on starting reference point
  observeEvent(input$reference_date, {
    # Calculate appropriate default days based on reference type
    default_days <- if (input$reference_date == "inauguration") {
      100  # Default for inauguration
    } else {
      180  # Default for election (includes transition period + first 100 days)
      # You can adjust this value as needed
    }
    
    # Update the slider with the new default value
    updateSliderInput(session, "time_period", value = default_days)
  })
  
  # Dynamic UI for president selection - reversed order
  output$president_selection <- renderUI({
    data <- loaded_data()
    
    if (is.null(data) || nrow(data$presidents_data) == 0) {
      return(NULL)
    }
    
    # Get presidents from data and reverse the order
    presidents <- rev(data$presidents_data$president)
    
    # Return checkbox group input with reversed order
    checkboxGroupInput("selected_presidents", 
                       "Select Presidents:", 
                       choices = presidents,
                       selected = head(presidents, 4)) # Select the first 4 presidents in the reversed list (most recent)
  })
  
  # Process data based on user selections
  processed_data <- reactive({
    # Get data
    data <- loaded_data()
    
    # Make sure data is loaded
    req(data, data$market_data, input$selected_presidents)
    
    # Get user selections
    selected_index <- input$selected_index
    reference_type <- input$reference_date
    selected_presidents <- input$selected_presidents
    party_filter <- input$party_filter
    days_to_show <- input$time_period
    
    # Choose the appropriate processing function based on the selected index
    if (selected_index %in% c("unemployment", "inflation")) {
      # For economic indicators, use absolute values
      return(process_econ_data(
        data$market_data,
        data$presidents_data,
        selected_index,
        selected_presidents,
        reference_type,
        party_filter,
        days_to_show
      ))
    } else {
      # For market indices, use percent change from reference date
      return(process_market_data(
        data$market_data,
        data$presidents_data,
        selected_index,
        selected_presidents,
        reference_type,
        party_filter,
        days_to_show
      ))
    }
  })
  
  # Render the plot
  output$economic_plot <- renderPlotly({
    # Get processed data
    plot_data <- processed_data()
    
    # Make sure there's data to plot
    req(nrow(plot_data) > 0)
    
    # Reference type for title
    ref_type <- if (input$reference_date == "inauguration") {
      "Inauguration Day"
    } else {
      "Day Before Election"
    }
    
    # Define party colors
    party_colors <- c("Democratic" = "blue", "Republican" = "red")
    
    if (input$selected_index %in% c("unemployment", "inflation")) {
      # Plot for economic indicators (absolute values)
      index_name <- unique(plot_data$index_name)[1]
      
      # Get end points for each president
      end_points <- plot_data %>% 
        group_by(president, party) %>% 
        filter(day == max(day)) %>%
        mutate(label = paste0(president, " (", round(value, 1), ")"))
      
      # Create base ggplot with enhanced tooltips
      p <- ggplot(plot_data, aes(x = day, y = value, color = party, group = president, 
                                 text = paste0(president, "<br>Day: ", day, "<br>Value: ", round(value, 2)))) +
        geom_line(size = 1, alpha = 0.8) +
        geom_point(data = end_points, size = 3) +
        scale_color_manual(values = party_colors) +
        labs(
          title = paste(index_name, "Since", ref_type),
          subtitle = paste0("Showing first ", input$time_period, " days"),
          x = paste("Days Since", ref_type),
          y = index_name,
          color = "Party"  # Capitalize Party in legend
        ) +
        theme_minimal() +
        theme(
          plot.title = element_text(face = "bold"),
          panel.grid.minor = element_blank(),
          legend.position = "bottom"
        ) +
        # Add extra space on right for labels
        coord_cartesian(xlim = c(0, max(plot_data$day) * 1.3))
      
      # For unemployment and inflation, we might want to set y-limits
      if (input$selected_index == "unemployment") {
        p <- p + ylim(0, max(plot_data$value) * 1.1)  # Ensure we start at 0 for unemployment
      }
      
      # Convert to plotly
      p_ly <- ggplotly(p, tooltip = "text") %>%
        layout(
          legend = list(orientation = "h", y = -0.2),
          # Add more space on the right margin for labels
          margin = list(r = 100)
        )
      
      # Add annotations for each end point
      for (i in 1:nrow(end_points)) {
        point <- end_points[i, ]
        
        p_ly <- p_ly %>% 
          add_annotations(
            x = point$day,
            y = point$value,
            text = point$label,
            showarrow = TRUE,
            arrowhead = 0,
            arrowsize = 0.7,
            arrowwidth = 1,
            arrowcolor = "gray70",
            xanchor = "left",
            yanchor = "middle",
            textangle = 0,
            font = list(color = "black", size = 11),
            ax = 50,
            ay = 0
          )
      }
      
      return(p_ly)
      
    } else {
      # Plot for a single market index (percent change)
      index_name <- unique(plot_data$index_name)[1]
      
      # Get end points for each president
      end_points <- plot_data %>% 
        group_by(president, party) %>% 
        filter(day == max(day)) %>%
        mutate(label = paste0(president, " (", round(percent_change, 1), "%)"))
      
      # Create base ggplot with enhanced tooltips
      p <- ggplot(plot_data, aes(x = day, y = percent_change, color = party, group = president, 
                                 text = paste0(president, "<br>Day: ", day, "<br>Change: ", round(percent_change, 2), "%"))) +
        geom_line(size = 1, alpha = 0.8) +
        geom_point(data = end_points, size = 3) +
        geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
        scale_color_manual(values = party_colors) +
        labs(
          title = paste(index_name, "Performance Since", ref_type),
          subtitle = paste0("Showing first ", input$time_period, " days (0% = value on reference date)"),
          x = paste("Days Since", ref_type),
          y = "Percent Change (%)",
          color = "Party"  # Capitalize Party in legend
        ) +
        theme_minimal() +
        theme(
          plot.title = element_text(face = "bold"),
          panel.grid.minor = element_blank(),
          legend.position = "bottom"
        ) +
        # Add extra space on right for labels
        coord_cartesian(xlim = c(0, max(plot_data$day) * 1.3))
      
      # Convert to plotly
      p_ly <- ggplotly(p, tooltip = "text") %>%
        layout(
          legend = list(orientation = "h", y = -0.2),
          # Add more space on the right margin for labels
          margin = list(r = 100)
        )
      
      # Add zero line
      p_ly <- p_ly %>% 
        add_segments(x = 0, xend = max(plot_data$day) * 1.3, 
                     y = 0, yend = 0,
                     line = list(dash = "dash", color = "gray", width = 1),
                     showlegend = FALSE, 
                     hoverinfo = "none")
      
      # Add annotations manually for each end point
      for (i in 1:nrow(end_points)) {
        point <- end_points[i, ]
        
        p_ly <- p_ly %>% 
          add_annotations(
            x = point$day,
            y = point$percent_change,
            text = point$label,  # Include percentage in label
            showarrow = TRUE,
            arrowhead = 0,
            arrowsize = 0.7,
            arrowwidth = 1,
            arrowcolor = "gray70",
            xanchor = "left",
            yanchor = "middle",
            textangle = 0,
            font = list(color = "black", size = 11),
            ax = 50,  # Increased offset for better visibility
            ay = 0
          )
      }
      
      return(p_ly)
    }
  })
  
  # Handle select all button
  observeEvent(input$select_all, {
    data <- loaded_data()
    req(data, data$presidents_data)
    
    # Get reversed president list to match the UI
    presidents <- rev(data$presidents_data$president)
    
    updateCheckboxGroupInput(session, "selected_presidents",
                             choices = presidents,
                             selected = presidents)
  })
  
  # Handle deselect all button
  observeEvent(input$deselect_all, {
    data <- loaded_data()
    req(data, data$presidents_data)
    
    # Get reversed president list to match the UI
    presidents <- rev(data$presidents_data$president)
    
    updateCheckboxGroupInput(session, "selected_presidents",
                             choices = presidents,
                             selected = character(0))
  })
  
  # Export plot handler - with embedded URL in caption and more right space
  observeEvent(input$export_plot, {
    req(processed_data())
    plot_data <- processed_data()
    
    # Reference type for title
    ref_type <- if (input$reference_date == "inauguration") {
      "Inauguration Day"
    } else {
      "Day Before Election"
    }
    
    # Define party colors
    party_colors <- c("Democratic" = "blue", "Republican" = "red")
    
    # Create a static plot with ggrepel labels for download
    if (input$selected_index %in% c("unemployment", "inflation")) {
      # Plot for economic indicators
      index_name <- unique(plot_data$index_name)[1]
      
      # Create end labels data
      end_labels <- plot_data %>% 
        group_by(president) %>% 
        filter(day == max(day)) %>%
        mutate(label = paste0(president, " (", round(value, 1), ")"))
      
      # Create ggplot with embedded URL in caption and more right space
      p <- ggplot(plot_data, aes(x = day, y = value, color = party, group = president)) +
        geom_line(size = 1) +
        geom_point(data = end_labels, size = 3) +
        # Add end labels
        geom_text_repel(
          data = end_labels,
          aes(label = label),
          direction = "y",
          hjust = 0,
          vjust = 0,
          nudge_x = 5,
          force = 3,
          segment.size = 0.2,
          box.padding = 0.5,
          show.legend = FALSE
        ) +
        scale_color_manual(values = party_colors) +
        labs(
          title = paste(index_name, "Since", ref_type),
          subtitle = paste0("Showing first ", input$time_period, " days"),
          x = paste("Days Since", ref_type),
          y = index_name,
          caption = paste0("Generated on ", Sys.Date(), " from https://jhelvy.github.io/potus-econ-scorecard/"),
          color = "Party"
        ) +
        theme_minimal() +
        theme(
          plot.title = element_text(face = "bold", size = 16),
          plot.subtitle = element_text(size = 12),
          panel.grid.minor = element_blank(),
          legend.position = "bottom",
          legend.title = element_text(face = "bold"),
          axis.title = element_text(face = "bold"),
          plot.caption = element_text(size = 9, hjust = 0)
        ) +
        coord_cartesian(xlim = c(0, max(plot_data$day) * 1.3))  # More right space
      
      # Set y-limits for unemployment to start at 0
      if (input$selected_index == "unemployment") {
        p <- p + ylim(0, max(plot_data$value) * 1.1)
      }
    } else {
      # Plot for a single market index
      index_name <- unique(plot_data$index_name)[1]
      
      # Create end labels data
      end_labels <- plot_data %>% 
        group_by(president) %>% 
        filter(day == max(day)) %>%
        mutate(label = paste0(president, " (", round(percent_change, 1), "%)"))
      
      # Create ggplot with embedded URL in caption and more right space
      p <- ggplot(plot_data, aes(x = day, y = percent_change, color = party, group = president)) +
        geom_line(size = 1) +
        geom_point(data = end_labels, size = 3) +
        # Add end labels
        geom_text_repel(
          data = end_labels,
          aes(label = label),
          direction = "y",
          hjust = 0,
          vjust = 0,
          nudge_x = 5,
          force = 3,
          segment.size = 0.2,
          box.padding = 0.5,
          show.legend = FALSE
        ) +
        geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
        scale_color_manual(values = party_colors) +
        labs(
          title = paste(index_name, "Performance Since", ref_type),
          subtitle = paste0("Showing first ", input$time_period, " days (0% = value on reference date)"),
          x = paste("Days Since", ref_type),
          y = "Percent Change (%)",
          caption = paste0("Generated on ", Sys.Date(), " from https://jhelvy.github.io/potus-econ-scorecard/"),
          color = "Party"
        ) +
        theme_minimal() +
        theme(
          plot.title = element_text(face = "bold", size = 16),
          plot.subtitle = element_text(size = 12),
          panel.grid.minor = element_blank(),
          legend.position = "bottom",
          legend.title = element_text(face = "bold"),
          axis.title = element_text(face = "bold"),
          plot.caption = element_text(size = 9, hjust = 0)
        ) +
        coord_cartesian(xlim = c(0, max(plot_data$day) * 1.3))  # More right space
    }
    
    # Show the plot in a modal with save instructions
    showModal(
      modalDialog(
        title = "Plot Export",
        size = "l",
        plotOutput("export_plot_preview", height = "500px"),
        hr(),
        tags$div(
          style = "text-align: center;",
          tags$p("To save this image, right-click on the plot above and select 'Save image as...'")
        ),
        footer = tagList(
          modalButton("Close")
        )
      )
    )
    
    output$export_plot_preview <- renderPlot({
      p
    }, height = 500, width = 800, res = 100)  # Increased resolution for better quality
  })
  
  # Export data handler - client-side download approach
  observeEvent(input$export_data, {
    req(processed_data())
    export_data <- processed_data() %>%
      mutate(
        reference_type = if(input$reference_date == "inauguration") "Inauguration Day" else "Day Before Election",
        data_generated = as.character(Sys.Date())
      )
    
    # Create a unique ID for this download
    download_id <- paste0("download_", round(runif(1, 1, 100000)))
    
    # Convert data to CSV format
    csv_content <- reactive({
      # Create the header row
      headers <- paste(colnames(export_data), collapse = ",")
      
      # Create data rows (escaping any commas in string fields)
      rows <- apply(export_data, 1, function(row) {
        # Convert row values to character and handle special cases
        row_vals <- sapply(row, function(val) {
          if (is.character(val) && grepl(",", val)) {
            # Wrap strings containing commas in quotes
            return(paste0('"', val, '"'))
          } else {
            return(as.character(val))
          }
        })
        paste(row_vals, collapse = ",")
      })
      
      # Combine header and rows
      csv_data <- paste(c(headers, rows), collapse = "\n")
      return(csv_data)
    })
    
    # Get index name for the filename
    index_name <- switch(input$selected_index,
                       "sp500" = "SP500",
                       "djia" = "DowJones",
                       "nasdaq" = "NASDAQ",
                       "unemployment" = "Unemployment",
                       "inflation" = "Inflation")
    
    filename <- paste0(index_name, "_data_", Sys.Date(), ".csv")
    
    # Create modal with preview and client-side download link
    showModal(
      modalDialog(
        title = "Data Export",
        size = "l",
        p("The complete dataset is ready for download."),
        p(paste0("Number of rows in dataset: ", nrow(export_data))),
        
        # Display a small preview of the data
        tags$div(
          style = "max-height: 300px; overflow-y: auto;",
          renderTable({
            head(export_data, 10)  # Show just first 10 rows as preview
          })
        ),
        
        # Add JavaScript to enable client-side download
        tags$script(HTML(sprintf(
          "
          function downloadCSV() {
            var csvContent = %s;
            var blob = new Blob([csvContent], { type: 'text/csv;charset=utf-8;' });
            var link = document.createElement('a');
            link.href = URL.createObjectURL(blob);
            link.setAttribute('download', '%s');
            document.body.appendChild(link);
            link.click();
            document.body.removeChild(link);
          }
          ",
          paste0("document.getElementById('", download_id, "').getAttribute('data-csv')"),
          filename
        ))),
        
        # Hidden span to store the CSV data
        tags$span(
          id = download_id,
          style = "display: none;",
          `data-csv` = csv_content()
        ),
        
        footer = tagList(
          modalButton("Close"),
          tags$button(
            "Download Complete Dataset",
            class = "btn btn-primary",
            onclick = "downloadCSV()"
          )
        )
      )
    )
  })
}

#-------------------------------------------
# Run the application
#-------------------------------------------
shinyApp(ui, server)