library(shiny) global <- 123 # Define UI for random distribution app ---- ui <- fluidPage( # App title ---- titlePanel("Tabsets"), # Sidebar layout with input and output definitions ---- sidebarLayout( # Sidebar panel for inputs ---- sidebarPanel( # Input: Select the random distribution type ---- radioButtons("dist", "Distribution type:", c("Normal" = "norm", "Uniform" = "unif", "Log-normal" = "lnorm", "Exponential" = "exp")), # br() element to introduce extra vertical spacing ---- br(), # Input: Slider for the number of observations to generate ---- sliderInput("n", "Number of observations:", value = 500, min = 1, max = 1000) ), # Main panel for displaying outputs ---- mainPanel( # Output: Tabset w/ plot, summary, and table ---- tabsetPanel(type = "tabs", tabPanel("Plot", plotOutput("plot")), tabPanel("Summary", verbatimTextOutput("summary")), tabPanel("Table", tableOutput("table")) ) ) ) ) # Define server logic for random distribution app ---- server <- function(input, output, session) { # Reactive expression to generate the requested distribution ---- # This is called whenever the inputs change. The output functions # defined below then use the value computed from this expression d <- 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 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$n hist(d(), main = paste("r", dist, "(", n, ")", sep = ""), col = "#75AADB", border = "white") }) # Generate a summary of the data ---- output$summary <- renderPrint({ summary(d()) }) # Generate an HTML table view of the data ---- output$table <- renderTable({ d() }) } # Create Shiny app ---- shinyApp(ui, server)