Chapter 4 Basic Reactivity

Exercise 4.3.6.1

Draw the reactive graph for the following server functions:

server1 <- function(input, output, session) {
  c <- reactive(input$a + input$b)
  e <- reactive(c() + input$d)
  output$f <- renderText(e())
}

server2 <- function(input, output, session) {
  x <- reactive(input$x1 + input$x2 + input$x3)
  y <- reactive(input$y1 + input$y2)
  output$z <- renderText(x() / y())
}

server3 <- function(input, output, session) {
  d <- reactive(c() ^ input$d)
  a <- reactive(input$a * 10)
  c <- reactive(b() / input$c)
  b <- reactive(a() + input$b)
}

Solution. Solution

To create the reactive graph we need to consider the inputs, reactive expressions, and outputs of the app.

For server1 we have the following objects:

inputs: input$a, input$b, and input$d
reactives: c() and e()
outputs: output$f

Inputs input$a and input$b are used to create c(), which is combined with input$d to create e(). The output depends only on e().

For server2 we have the following objects:

inputs: input$y1, input$y2, input$x1, input$x2, input$x3
reactives: y() and x()
outputs: output$z

Inputs input$y1 and input$y2 are needed to create the reactive y(). In addition, inputs input$x1, input$x2, and input$x3 are required to create the reactive x(). The output depends on both x() and y().

For server3 we have the following objects:

inputs: input$a, input$b, input$c, input$d
reactives: a(), b(), c(), d()

As we can see below, a() relies on input$a, b() relies on both a() and input$b, and c() relies on both b() and input$c. The final output depends on both c() and input$d.

Exercise 4.3.6.2

Why will this code fail?

var <- reactive(df[input$var])
range <- reactive(range(var(), na.rm = TRUE))

Why is var() a bad name for a reactive?

Solution. Solution

This code doesn’t work because we called our reactive range, so when we call the range function we’re actually calling our new reactive. If we change the name of the reactive from range to col_range then the code will work. Similarly, var() is not a good name for a reactive because it’s already a function to compute the variance of x! ?cor::var

library(shiny)

df <- mtcars

ui <- fluidPage(
    selectInput("var", NULL, choices = colnames(df)),
    verbatimTextOutput("debug")
)

server <- function(input, output, session) {
    col_var <- reactive( df[input$var] )
    col_range <- reactive({ range(col_var(), na.rm = TRUE ) })
    output$debug <- renderPrint({ col_range() })

}

shinyApp(ui = ui, server = server)

Exercise 4.4.6.1

Use reactive expressions to reduce the duplicated code in the following simple apps.

Solution. Solution

Unclear about the apps mentioned in the exercise.