Get startedGet started for free

Reactive variables reduce code duplication

In the previous exercises, the code to filter gapminder according to the input values is duplicated three times: once in the table, once in the plot, and once in the download handler.

Reactive variables can be used to reduce code duplication, which is generally a good idea because it makes maintenance easier.

This exercise is part of the course

Case Studies: Building Web Applications with Shiny in R

View Course

Exercise instructions

The duplicated code chunks that filter the data have been removed. Your task is to add a reactive variable that filters the data, and use this variable instead. Specifically:

  • Create a reactive variable named filtered_data by using the reactive() function that uses the filtering code from the previous exercise (line 15).
  • Use the reactive variable to render the table output, the plot output, and the download file (lines 33, 42, and 50).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

ui <- fluidPage(
  h1("Gapminder"),
  sliderInput(inputId = "life", label = "Life expectancy",
              min = 0, max = 120,
              value = c(30, 50)),
  selectInput("continent", "Continent",
              choices = c("All", levels(gapminder$continent))),
  downloadButton(outputId = "download_data", label = "Download"),
  plotOutput("plot"),
  tableOutput("table")
)

server <- function(input, output) {
  # Create a reactive variable named "filtered_data"
  filtered_data <- ___({
    # Filter the data (copied from previous exercise)
    data <- gapminder
    data <- subset(
      data,
      lifeExp >= input$life[1] & lifeExp <= input$life[2]
    )
    if (input$continent != "All") {
      data <- subset(
        data,
        continent == input$continent
      )
    }
    data
  })
  
  output$table <- renderTable({
    # Use the filtered_data variable to render the table output
    data <- ___
    data
  })

  output$download_data <- downloadHandler(
    filename = "gapminder_data.csv",
    content = function(file) {
      # Use the filtered_data variable to create the data for
      # the downloaded file
      data <- ___
      write.csv(data, file, row.names = FALSE)
    }
  )

  output$plot <- renderPlot({
    # Use the filtered_data variable to create the data for
    # the plot
    data <- ___
    ggplot(data, aes(gdpPercap, lifeExp)) +
      geom_point() +
      scale_x_log10()
  })
}

shinyApp(ui, server)
Edit and Run Code