Chapter 9 Uploads and Downloads

Exercise 9.4.1

Use the ambient package by Thomas Lin Pedersen to generate worley noise and download a PNG of it.


A general method for saving a png file is to select the png driver using the function png(). The only argument the driver needs is a filename (this will be stored relative to your current working directory!). You will not see the plot when running the plot function because it is being saved to that file instead. When we’re done plotting, we used the command to close the connection to the driver.

Exercise 9.4.2

Create an app that lets you upload a csv file, select a variable, and then perform a t.test() on that variable. After the user has uploaded the csv file, you’ll need to use updateSelectInput() to fill in the available variables. See Section 10.1 for details.


We can use the fileInput widget with the accept argument set to .csv to allow only the upload of csv files. In the server function we save the uploaded data to the the data reactive and use it to update input$variable, which displays variable (i.e. numeric data column) choices. Note that we put the updateSelectInput within an observe event because we need the input$variable to change if the user selects another file.

Exercise 9.4.3

Create an app that lets the user upload a csv file, select one variable, draw a histogram, and then download the histogram. For an additional challenge, allow the user to select from .png, .pdf, and .svg output formats.


Adapting the code from the example above, rather than print a t-test output, we save the plot in a reactive and use it to display the plot/download. We can use the ggsave function to switch between input$extension types.


ui <- fluidPage(
    br(), br(),
             fileInput("file", "Upload CSV", accept = ".csv"),
             selectInput("variable", "Select Variable", choices = NULL),
             radioButtons("extension", "Save As:",
                          choices = c("png", "pdf", "svg"), inline = TRUE),
             downloadButton("download", "Save Plot")
    column(8, plotOutput("results"))

server <- function(input, output,session) {
  # get data from file
  data <- reactive({
    # as shown in the book, lets make sure the uploaded file is a csv
    ext <- tools::file_ext(input$file$name)
    validate(need(ext == "csv", "Invalid file. Please upload a .csv file"))
    dataset <- vroom::vroom(input$file$datapath, delim = ",")
    # let the user know if the data contains no numeric column
    validate(need(ncol(dplyr::select_if(dataset, is.numeric)) != 0,
                  "This dataset has no numeric columns."))
  # create the select input based on the numeric columns in the dataframe
  observeEvent( input$file, {
    num_cols <- dplyr::select_if(data(), is.numeric)
    updateSelectInput(session, "variable", choices = colnames(num_cols))
  # plot histogram
  plot_output <- reactive({
    ggplot(data()) +
      aes_string(x = input$variable) +
  output$results <- renderPlot(plot_output())
  # save histogram using downloadHandler and plot output type
  output$download <- downloadHandler(
    filename = function() {
      paste("histogram", input$extension, sep = ".")
    content = function(file){
      ggsave(file, plot_output(), device = input$extension)

shinyApp(ui, server)

Exercise 9.4.4

Write an app that allows the user to create a Lego mosaic from any .png file using Ryan Timpe’s brickr package. Once you’ve completed the basics, add controls to allow the user to select the size of the mosaic (in bricks), and choose whether to use “universal” or “generic” colour palettes.