10  Reactive Programming

Author

Affiliation

Dr Randy Johnson

Hood College

Introduction to Shiny

• A framework for creating web apps using R

• No HTML/CSS/JS required (but you can use them if you want!)

• ui: The front-end (what the user sees)

• server: The back-end (the R logic)

ui <- fluidPage(
  # front end interface
)

server <- function(input, output, session) {
  # back end logic
}

shinyApp(ui, server)

Hello, Shiny!

Try running the following code in R:

library(shiny)

ui <- fluidPage(
  "Hello, world!"
)

server <- function(input, output, session) {
}

shinyApp(ui, server)

Basic UI: Inputs & Outputs

• In this section, we’ll focus on the UI components of a Shiny app

UI Inputs

• Each input typically has
  • inputID: internal name, must be unique and follow R variable naming conventions
  • label: visible text

• Different types of inputs:

  • Free Text: textInput(), textAreaInput()

  • Numbers: numericInput(), sliderInput().

  • Choices: selectInput(), radioButtons(), checkboxGroupInput().

UI Outputs

• Outputs are placeholders in the UI

• Every Output() in the UI must have a corresponding render() in the server.

  • textOutput() → renderText()

  • tableOutput() → renderTable()

  • plotOutput() → renderPlot()

Layouts

• fluidPage() manages the responsive layout

  • Use sidebarLayout() for a common sidebar + main content structure

Basic UI Example

The following is an example of a simple UI that includes a slider input and a plot output:

# Old Faithful Geyser Data UI (see https://shiny.posit.co/r/gallery/start-simple/faithful/)
# Define UI for application that draws a histogram
ui <- fluidPage(

    # Application title
    titlePanel("Old Faithful Geyser Data"),

    # Sidebar with a slider input for number of bins 
    sidebarLayout(
        sidebarPanel(
            sliderInput("bins",
                        "Number of bins:",
                        min = 1,
                        max = 50,
                        value = 30)
        ),

        # Show a plot of the generated distribution
        mainPanel(
           plotOutput("distPlot")
        )
    )
)

Basic Reactivity

• In this section we will introduce reactivity
• Reactivity is what makes Shiny apps dynamic and interactive

The Server Function

• Three arguments:

  • input: A list-like object, Read-only

  • output: A list-like object, Write-only

  • session: An environment for managing user sessions (R handles this in the background)

Declarative vs. Imperative

• Imperative (Normal R): “Take X, do Y, save as Z”

• Declarative (Shiny): “This is the recipe for Z. Update it whenever the ingredients change.”

The Reactive Graph

• A reactive graph is a network of dependencies

• For example, when output$y depends on input$x:

  • output$y <- renderText({ paste("You selected", input$x) })

  • When input$x changes, output$y will be updated

• A reactive graph can be a helpful tool to visualize and debug dependencies

Reactive Expressions (reactive())

• To avoid duplicated code or minimize expensive calculations

• Use reactive({...}) to create a cached calculation

• We call a reactive expression like a function to retrieve the object: my_data()

Example of duplicate code from section 3.4.2 of Wickham (2021):

server <- function(input, output, session) {
  output$hist <- renderPlot({
    x1 <- rnorm(input$n1, input$mean1, input$sd1)
    x2 <- rnorm(input$n2, input$mean2, input$sd2)

    freqpoly(x1, x2, binwidth = input$binwidth, xlim = input$range)
  }, res = 96)

  output$ttest <- renderText({
    x1 <- rnorm(input$n1, input$mean1, input$sd1)
    x2 <- rnorm(input$n2, input$mean2, input$sd2)

    t_test(x1, x2)
  })
}

Reactive Expressions (reactive())

Simplified with reactive():

server <- function(input, output, session) {
  x1 <- reactive(rnorm(input$n1, input$mean1, input$sd1))
  x2 <- reactive(rnorm(input$n2, input$mean2, input$sd2))

  output$hist <- renderPlot({
    freqpoly(x1(), x2(), binwidth = input$binwidth, xlim = input$range)
  }, res = 96)

  output$ttest <- renderText({
    t_test(x1(), x2())
  })
}

Basic Server Example

This server code complements the UI example from above:

# Old Faithful Geyser Data Server (see https://shiny.posit.co/r/gallery/start-simple/faithful/)
# Define server logic required to draw a histogram
server <- function(input, output) {

    output$distPlot <- renderPlot({
        # generate bins based on input$bins from ui.R
        x    <- faithful[, 2]
        bins <- seq(min(x), max(x), length.out = input$bins + 1)

        # draw the histogram with the specified number of bins
        hist(x, breaks = bins, col = 'darkgray', border = 'white',
             xlab = 'Waiting time to next eruption (in mins)',
             main = 'Histogram of waiting times')
    })
}

Copy and paste the UI and server examples into R along with this code to run the complete app:

# Run the application 
shinyApp(ui = ui, server = server)

References

Wickham, Hadley. 2021. Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by R. First edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly.