In jr-packages/jrShiny: R Shiny Exercises

library("jrShiny")
library("shiny")

Exercise 6 (10 minutes)

Open chapter2/exercise6.Rmd. This contains the answers to Exercise 5. Here we're calculating the variable selected twice which is inefficient.

• Use the reactive() function so that the variable selected is only calculated once.

Exercise 7 (10 minutes)

Use chapter2/exercise6.Rmd again.

• (Harder) Use an actionButton() and an eventReactive() to make it so that the plot and text only render when the button is clicked.
• Change actionButton() to actionLink()

Exercise 8a (10 minutes)

Open chapter2/exercise8.Rmd. Here we have one button for the flower setosa and one for versicolor. We've also set up a reactiveValues() that initialises to the entire iris data set
* Use the observeEvent() function to update the reactiveValues to the correctly filtered data set, depending on which button the user clicks

Exercise 8b

• Convert the plot in exercise 8a to a ggplot2 graph
• Convert the plot from a ggplot2 graph to a plotly graph

Exercise 8c

• Using renderTable(), add a static table of the selected data
• Using the DT library, make this table an interactive table

Exercise 9 (10 minutes)

• Open chapter2/exercise9.Rmd and run.
• Include the line r ## Arguments: id, code to execute on the server observeEvent(input$n, { print(input$n) } ) and look at the R Markdown tab.
• Try a different shiny widget.

Exercise 10 (5 minutes)

• Open chapter2/exercise10a.Rmd and run.
  • Add a second slider and display the sum of both sliders.
  • Let the slider determine the value of n in an rnorm call, i.e. rnorm(input$n).
  • Using renderPlot(), generate a scatter plot of random numbers where the sample size is determined by a slider.
• Open chapter2/exercise10b.Rmd and run.
  • Create another sliderInput() widget to control the mean value used in the rnorm() function call, i.e. r ## input$mean comes from a second slider rnorm(input$n, input$mean)
  • Add in a selectInput() widget to switch between generating random numbers from a normal distribution and a uniform distribution. For a uniform distribution you would use the runif() function.
  • Add renderText(input$n) to the script to print the value of input$n.
  • Instead of printing input$n, print out the largest value in the sample.
    • Do you see a problem?

Exercise 11

• Open chapter2/exercise11.Rmd and run.
• Fix the syncing problem between the plot and the text by using the reactive() function.
• Add in a selectInput() widget to switch between generating random numbers from a normal distribution and a uniform distribution.

Exercise 12

• Open chapter2/exercise12.Rmd and run.
• Using eventReactive(), only redraw the graph and calculate the maximum value when the Plot it button is pressed.
• Change actionButton() to actionLink()

Exercise 13

• Open chapter2/exercise13.Rmd and run.
• Using reactiveValues() and observeEvent() to only redraw the plot when a button is clicked.

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Further Reading

When I was developing this tutorial, I created a couple of simple examples to illustrate different shiny components. These examples are found in chapter2/ and the files start with Why_.

jr-packages/jrShiny documentation built on Feb. 16, 2020, 9:13 p.m.