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d7391b19bc
* - Convert all example apps to single file app.R file - Make relevant updates to Readmes to match up with app.R structure - Add color to plots (RStudio blue) - In 04_mpg example: Show outliers by default, as opposed to hide, since this is more routine - In 06_tabsets and 08_html examples: Don't name random data vector "data" - Add extensive comments to app.R files and use consistent formatting of comments across examples - In 09_upload example: Use req() to check for NULL entry * add news entry summarizing changes * use true RStudio blue, #75AADB * Conver shinyApp calls at the end to drop argument name in examples 3-11, except for the custom HTML example. Kept them in for examples 1&2 for completeness in first exporuse to function. * Pull news items that got added before this PR was merged * Update comment for shinyApp function -- it creates an app object, doesn't run the app
93 lines
2.4 KiB
R
93 lines
2.4 KiB
R
library(shiny)
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# Define UI for random distribution app ----
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ui <- fluidPage(
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# App title ----
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titlePanel("Tabsets"),
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# Sidebar layout with input and output definitions ----
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sidebarLayout(
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# Sidebar panel for inputs ----
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sidebarPanel(
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# Input: Select the random distribution type ----
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radioButtons("dist", "Distribution type:",
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c("Normal" = "norm",
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"Uniform" = "unif",
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"Log-normal" = "lnorm",
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"Exponential" = "exp")),
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# br() element to introduce extra vertical spacing ----
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br(),
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# Input: Slider for the number of observations to generate ----
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sliderInput("n",
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"Number of observations:",
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value = 500,
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min = 1,
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max = 1000)
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),
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# Main panel for displaying outputs ----
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mainPanel(
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# Output: Tabset w/ plot, summary, and table ----
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tabsetPanel(type = "tabs",
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tabPanel("Plot", plotOutput("plot")),
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tabPanel("Summary", verbatimTextOutput("summary")),
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tabPanel("Table", tableOutput("table"))
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)
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)
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)
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)
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# Define server logic for random distribution app ----
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server <- function(input, output) {
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# Reactive expression to generate the requested distribution ----
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# This is called whenever the inputs change. The output functions
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# defined below then use the value computed from this expression
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d <- reactive({
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dist <- switch(input$dist,
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norm = rnorm,
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unif = runif,
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lnorm = rlnorm,
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exp = rexp,
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rnorm)
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dist(input$n)
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})
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# Generate a plot of the data ----
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# Also uses the inputs to build the plot label. Note that the
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# dependencies on the inputs and the data reactive expression are
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# both tracked, and all expressions are called in the sequence
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# implied by the dependency graph.
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output$plot <- renderPlot({
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dist <- input$dist
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n <- input$n
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hist(d(),
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main = paste("r", dist, "(", n, ")", sep = ""),
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col = "#75AADB", border = "white")
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})
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# Generate a summary of the data ----
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output$summary <- renderPrint({
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summary(d())
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})
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# Generate an HTML table view of the data ----
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output$table <- renderTable({
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d()
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})
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}
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# Create Shiny app ----
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shinyApp(ui, server)
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