Reactive contexts

Reactive values are special constructs in Shiny; they are not seen anywhere else in R programming. As such, they cannot be used in just any R code, reactive values can only be accessed within a reactive context.

This is the reason why any variable that depends on a reactive value must be created using the reactive() function, otherwise you will get an error. The shiny server itself is not a reactive context, but the reactive() function, the observe() function, and all render*() functions are.

This exercise is part of the course

Case Studies: Building Web Applications with Shiny in R

Exercise instructions

You are provided with a Shiny app containing two numeric inputs, num1 and num2, and a text output. Your task is to:

In a reactive variable called my_sum, calculate the sum of the two numeric inputs (line 10).
In a reactive variable called my_average, calculate the average of the two inputs (line 14).
In the text output, display the calculated average using the reactive variables (line 23).

Hands-on interactive exercise

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

ui <- fluidPage(
  numericInput("num1", "Number 1", 5),
  numericInput("num2", "Number 2", 10),
  textOutput("result")
)

server <- function(input, output) {
  # Calculate the sum of the inputs
  my_sum <- reactive({
    input$num1 + ___
  })

  # Calculate the average of the inputs
  my_average <- ___({
    my_sum() / 2
  })
  
  output$result <- renderText({
    paste(
      # Print the calculated sum
      "The sum is", my_sum(),
      # Print the calculated average
      "and the average is", ___
    )
  })
}

shinyApp(ui, server)

Edit and Run Code

This exercise is part of the course

Case Studies: Building Web Applications with Shiny in R

IntermediateSkill Level

4.8+

Start Course for Free

In the first chapter, you'll review the essentials of Shiny development. You'll get reintroduced to the basic structure of a Shiny application, as well as some core Shiny concepts such as inputs, outputs, and reactivity. Completing this chapter will help refresh your Shiny knowledge and ensure you have the required skills to develop Shiny apps for real-life scenarios.

Exercise 1: Introduction Exercise 2: Simple text Exercise 3: Formatted text Exercise 4: Adding structure to your app Exercise 5: Inputs and outputs Exercise 6: Adding inputs Exercise 7: Adding placeholders for outputs Exercise 8: Constructing output objects Exercise 9: Reactivity 101 Exercise 10: Reactivity: simple reactive variable Exercise 11: Reactivity: composed reactive variable Exercise 12: Reactive contexts

Current Exercise

Imagine you're preparing a figure for a manuscript using R. You spend a lot of time recreating the same plot over and over again by rerunning the same code but changing small parameters each time. The size of the points, the color of the points, the plot title, the data shown on the plot—these criteria all have to be just right before publishing the figure. To save you from the hassle of rerunning the code many times, you will learn how to create a Shiny app to make a customizable plot.

Exercise 1: Make the perfect plot using Shiny Exercise 2: Explore the Gapminder data Exercise 3: More exploration of the Gapminder data Exercise 4: Adding simple inputs to modify a plot Exercise 5: Add a plot title: text input Exercise 6: Change the point size: numeric input Exercise 7: Fit a smooth curve: checkbox input Exercise 8: More input types Exercise 9: Add colours to your plot: radio buttons Exercise 10: Add a continent selector: select input Exercise 11: Add a year filter: numeric slider input Exercise 12: Advanced features to improve your plot Exercise 13: Add colours to your plot: color input Exercise 14: Making your plot larger Exercise 15: Make your plot interactive

Let’s say your supervisor is impressed by the plot you created with Shiny and now wants to get familiar with the dataset you used in the plot. They don't want to simply have a raw data file, they want an interactive environment where they can view the data, filter it, and download it. This chapter will guide you in creating such an application—a Shiny app for exploring the Gapminder dataset.

Exercise 1: Explore a dataset with Shiny Exercise 2: See the data in a table Exercise 3: Filter by life expectancy Exercise 4: Select a continent to view Exercise 5: Allow "All" continents to be viewed Exercise 6: More ways to view data: plot and download Exercise 7: Plot the data Exercise 8: Download the filtered data Exercise 9: Reactive variables Exercise 10: Reactive variables reduce code duplication Exercise 11: More benefits of reactive variables Exercise 12: Visual enhancements Exercise 13: Make the table interactive Exercise 14: Place different outputs on different tabs Exercise 15: Add CSS to modify the look of the app

Your friend really likes word clouds and has written an R function to generate them. They want to share this function with all their friends, but not all of them know how to use R. You offer to help by building a Shiny app that uses their function to let people create their own word clouds. This will allow all their friends—even the ones who are unfamiliar with R—to generate word clouds using a point-and-click interface. This chapter will guide you through the steps required to build this app.

Exercise 1: Word clouds in Shiny Exercise 2: Word cloud Shiny app Exercise 3: Change the word cloud parameters Exercise 4: Add a layout Exercise 5: Adding word sources Exercise 6: Use your own words Exercise 7: Upload a text file (ui)Exercise 8: Upload a text file (server)Exercise 9: Combining all the word sources Exercise 10: Choose the data source (ui)Exercise 11: Choose the data source (server)Exercise 12: Conditionally show or hide required inputs Exercise 13: Fine tune the reactivity Exercise 14: Don't continuously create new word clouds Exercise 15: Reactivity: effects of isolation Exercise 16: Create a new word cloud on demand Exercise 17: Wrap-up: Go and make your own apps!