Here is an existing example
library(shiny)
runExample("06_tabsets")
And you will see you can choose distribution type in radiobutton and there are three tabs "Plot", "Summary", and "Table".
My question is how can I add a selectInput under the sliderInput(number of observations) with two values. The default one is "NULL", the second one is "1". Once users select "1", the previous three tabs would disappear. Instead, a new tab would show whatever it content is.
This is the modified "06_tabsets". A select input is added and the UI is generated depending of the selection. The only difference is that is not using NULL, but two options. I could make it run with NULL. Let me know if this helps.
ui.R
library(shiny)
# 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),
selectInput("contentSelect", "Select content to dislay:", choices = c("1", "2"), selected = 1)
),
# Show a tabset that includes a plot, summary, and table view
# of the generated distribution
mainPanel(
uiOutput("content")
)
)
))
server.R
library(shiny)
# Define server logic for random distribution application
shinyServer(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$table <- renderTable({
data.frame(x=data())
})
output$textA <- renderText({
paste(input$contentSelect, " A")
})
observeEvent(input$contentSelect, {
if (input$contentSelect == "1") {
output$content <- renderUI({
tabsetPanel(type = "tabs",
tabPanel("Plot", plotOutput("plot")),
tabPanel("Summary", verbatimTextOutput("summary")),
tabPanel("Table", tableOutput("table"))
)
})
} else {
output$content <- renderUI({
tabsetPanel(type = "tabs",
tabPanel("A", textOutput("textA"))
)
})
}
})
})
