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Add CSS to modify the look of the app

CSS is an extremely popular markup language that is used to tell the browser how to display elements on a page. You need to use CSS if you want to deviate from the default look-and-feel of Shiny and want to customize the appearance of different items in your app.

Recall that CSS is comprised of a set of rules, where each rule is a property: value pair associated with an element on the page. It's possible to include CSS in your app by writing it in a separate file and importing it with includeCSS(), but in this course we will use the simpler approach of placing the CSS code inside tags$style() in the UI.

Diese Übung ist Teil des Kurses

Case Studies: Building Web Applications with Shiny in R

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Anleitung zur Übung

  • Write CSS rules to modify the app in the following ways:
    • Change the background color of the download button to orange (line 5).
    • Change the text size of the download button to 20 pixels (line 8).
    • Change the text color of the table to red (line 13).
  • Add these CSS rules to the Shiny app (line 20).

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

my_css <- "
#download_data {
  /* Change the background color of the download button
     to orange. */
  background: ___;

  /* Change the text size to 20 pixels. */
  font-size: ___px;
}

#table {
  /* Change the text color of the table to red. */
  color: ___;
}
"

ui <- fluidPage(
  h1("Gapminder"),
  # Add the CSS that we wrote to the Shiny app
  tags$style(___),
  tabsetPanel(
    tabPanel(
      title = "Inputs",
      sliderInput(inputId = "life", label = "Life expectancy",
                  min = 0, max = 120,
                  value = c(30, 50)),
      selectInput("continent", "Continent",
                  choices = c("All", levels(gapminder$continent))),
      downloadButton("download_data")
    ),
    tabPanel(
      title = "Plot",
      plotOutput("plot")
    ),
    tabPanel(
      title = "Table",
      DT::dataTableOutput("table")
    )
  )
)

server <- function(input, output) {
  filtered_data <- reactive({
    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 <- DT::renderDataTable({
    data <- filtered_data()
    data
  })

  output$download_data <- downloadHandler(
    filename = "gapminder_data.csv",
    content = function(file) {
      data <- filtered_data()
      write.csv(data, file, row.names = FALSE)
    }
  )

  output$plot <- renderPlot({
    data <- filtered_data()
    ggplot(data, aes(gdpPercap, lifeExp)) +
      geom_point() +
      scale_x_log10()
  })
}

shinyApp(ui, server)
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