inst/shiny-examples/shiny_tabset/app.R

In sortable: Drag-and-Drop in 'shiny' Apps with 'SortableJS'

## ---- shiny-tabset-app --------------------------------------------------
## Example shiny app to drag-and-drop tabsets in a shiny app


# all credit for code goes to RStudio
# https://github.com/rstudio/shiny-examples/tree/main/006-tabsets
library(sortable)
library(shiny)

ui = # Define UI for random distribution application
  shinyUI(fluidPage(

    # Application title
    titlePanel("Tabsets"),

    # Sidebar with controls to select the random distribution type
    # and number of observations to generate. Note the use of the
    # br() element to introduce extra vertical spacing
    sidebarLayout(
      sidebarPanel(
        radioButtons(
          "dist", "Distribution type:",
          c(
            "Normal" = "norm",
            "Uniform" = "unif",
            "Log-normal" = "lnorm",
            "Exponential" = "exp"
          )
        ),
        br(),

        sliderInput(
          "n",
          "Number of observations:",
          value = 500,
          min = 1,
          max = 1000)
      ),

      # Show a tabset that includes a plot, summary, and table view
      # of the generated distribution
      mainPanel(
        tabsetPanel(
          type = "tabs",
          id = "sortTab",
          tabPanel("Plot", plotOutput("plot")),
          tabPanel("Summary", verbatimTextOutput("summary")),
          tabPanel("Table", tableOutput("table"))
        )
      )
    ),
    sortable_js("sortTab")
  ))

server = function(input, output) {

  # Reactive expression to generate the requested distribution.
  # This is called whenever the inputs change. The output
  # functions defined below then all use the value computed from
  # this expression
  data <- reactive({
    dist <- switch(
      input$dist,
      norm = rnorm,
      unif = runif,
      lnorm = rlnorm,
      exp = rexp,
      rnorm
    )

    dist(input$n)
  })

  # Generate a plot of the data. Also uses the inputs to build
  # the plot label. Note that the dependencies on both the inputs
  # and the data reactive expression are both tracked, and
  # all expressions are called in the sequence implied by the
  # dependency graph
  output$plot <- renderPlot({
    dist <- input$dist
    n <- input$n

    hist(data(),
         main=paste('r', dist, '(', n, ')', sep=''))
  })

  # Generate a summary of the data
  output$summary <- renderPrint({
    summary(data())
  })

  # Generate an HTML table view of the data
  output$table <- renderTable({
    data.frame(x = data())
  })

}

shinyApp( ui, server )