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86 lines
3.1 KiB
Markdown
86 lines
3.1 KiB
Markdown
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The Shiny Text application demonstrates printing R objects directly, as well as displaying data frames using HTML tables. To run the example, type:
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{% highlight console %}
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> library(shiny)
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> runExample("02_text")
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{% endhighlight %}
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The first example had a single numeric input specified using a slider and a single plot output. This example has a bit more going on: two inputs and two types of textual output.
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If you try changing the number of observations to another value, you'll see a demonstration of one of the most important attributes of Shiny applications: inputs and outputs are connected together "live" and changes are propagated immediately (like a spreadsheet). In this case, rather than the entire page being reloaded, just the table view is updated when the number of observations change.
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Here is the user interface definition for the application. Notice in particular that the `sidebarPanel` and `mainPanel` functions are now called with two arguments (corresponding to the two inputs and two outputs displayed):
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#### ui.R
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{% highlight r %}
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library(shiny)
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# Define UI for dataset viewer application
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shinyUI(pageWithSidebar(
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# Application title
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headerPanel("Shiny Text"),
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# Sidebar with controls to select a dataset and specify the number
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# of observations to view
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sidebarPanel(
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selectInput("dataset", "Choose a dataset:",
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choices = c("rock", "pressure", "cars")),
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numericInput("obs", "Number of observations to view:", 10)
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),
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# Show a summary of the dataset and an HTML table with the requested
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# number of observations
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mainPanel(
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verbatimTextOutput("summary"),
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tableOutput("view")
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)
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))
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{% endhighlight %}
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The server side of the application has also gotten a bit more complicated. Now we create:
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* A reactive expression to return the dataset corresponding to the user choice
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* Two other rendering expressions (`renderPrint` and `renderTable`) that return the `output$summary` and `output$view` values
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These expressions work similarly to the `renderPlot` expression used in the first example: by declaring a rendering expression you tell Shiny that it should only be executed when its dependencies change. In this case that's either one of the user input values (`input$dataset` or `input$n`).
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#### server.R
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{% highlight r %}
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library(shiny)
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library(datasets)
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# Define server logic required to summarize and view the selected dataset
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shinyServer(function(input, output) {
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# Return the requested dataset
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datasetInput <- reactive({
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switch(input$dataset,
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"rock" = rock,
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"pressure" = pressure,
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"cars" = cars)
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})
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# Generate a summary of the dataset
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output$summary <- renderPrint({
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dataset <- datasetInput()
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summary(dataset)
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})
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# Show the first "n" observations
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output$view <- renderTable({
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head(datasetInput(), n = input$obs)
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})
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})
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{% endhighlight %}
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We've introduced more use of reactive expressions but haven't really explained how they work yet. The next example will start with this one as a baseline and expand significantly on how reactive expressions work in Shiny.
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