Using R Shiny

Faculty supports building R Shiny applications. R Shiny offers a flexible interface for building interactive dashboards entirely in R (you don’t need to write any JavaScript). The Shiny gallery has some great examples of dashboards, and the shiny-examples repository on github for many more that can be used as starting point for your own app.

Developing the application

When you are just starting out, you probably want to develop the application without exposing it to other people in your project.

The easiest way of doing this is in Rstudio, and will be familiar to you if you have built shiny apps with Rstudio in the past. To start, simply create an Rstudio server.

Now we need to write the code for our application. We will use one of the applications in the R Shiny gallery as an example: k-means clustering of the Iris dataset.

Let’s start by creating a directory /project/iris-kmeans in the project workspace, which you can either do using the Rstudio file browser


or by opening the terminal and running the command:

$ mkdir -p /project/iris-kmeans

Next create a file called app.R in /project/iris-kmeans, with the following contents:


ui <- pageWithSidebar(
  headerPanel('Iris k-means clustering'),
    selectInput('xcol', 'X Variable', names(iris)),
    selectInput('ycol', 'Y Variable', names(iris),
    numericInput('clusters', 'Cluster count', 3,
                 min = 1, max = 9)

server <- function(input, output, session) {

  # Combine the selected variables into a new data frame
  selectedData <- reactive({
    iris[, c(input$xcol, input$ycol)]

  clusters <- reactive({
    kmeans(selectedData(), input$clusters)

  output$plot1 <- renderPlot({
    palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
      "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))

    par(mar = c(5.1, 4.1, 0, 1))
         col = clusters()$cluster,
         pch = 20, cex = 3)
    points(clusters()$centers, pch = 4, cex = 4, lwd = 4)


shinyApp(ui = ui, server = server)

Note that is important that the file be called app.R for shiny-server to recognise it.

This code defines an application that will compute k-means clusters of the Iris data, allowing the user which features to use for the clustering and how many clusters to use.

To preview your app, simply click on “Run App” in the top right of your code pane in Rstudio.


By clicking on the dropdown arrow to the right of “Run App”, you can choose to preview your app in the Rstudio viewer pane, to open the app in a pop-up window, or to open the app in your browser. On Faculty the last two are broadly the same, though if you elect to view in a pop-up window you may need to configure your browser to allow pop-ups from Faculty.

Deploying the application

You have now developed a great dashboard, and you want to let other members of your project access it. Faculty supports hosting R Shiny applications. Head to the Apps tab in Faculty, click New, followed by R Shiny. You will be prompted to enter a name for your app.


Choose a name and click Create App. You will then be taken to the App settings page.

You will need to make the following changes to the application settings:

  • Change the APP DIRECTORY to /project/iris-kmeans.

Save your application by clicking the Save button, then click Start app to actually start your server. After a few seconds, you will see the status of your app change to Running. At this point, a URL will appear. Select that URL and place it in your browser search bar. You will be taken to the application! Behind the scenes, Faculty verifies that you are an observer in the project that the app belongs to.

Sharing your application

To share the application with a team member who is an observer in the project, just give them the URL of the application!

To invite people to your project, go to the Collaborators page, enter their Faculty username or the email that they used to sign up to Faculty, and assign them observer status.