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library(shiny)
# 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) {
# 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)
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