Launch an Interactive R Shiny App

cnvrg is designed to help with all parts of your ML life cycle and that includes supporting comprehensive R functionality. One of the use cases supported by R is the ability to launch dynamic and interactive visualization dashboards using R Shiny. cnvrg has full support for R Shiny and in this tutorial, we will launch a simple example dashboard.

Create a New Project

1. Go to Projects and click Start Project.
2. Name the project r-shiny.

Add the R Shiny code

Before we can launch and R Shiny app, we need to add the example code. You can of course use the workspaces feature to spin up an R Studio workspace where you could build your R Shiny code. However, we will simply be using pre-prepared code.

1. Go to Files inside your r-shiny project.
2. Click New File.
3. Copy and paste the following code into the code editor:

   library(shiny)
   ui <- pageWithSidebar(
       headerPanel('Iris k-means clustering'),
       sidebarPanel(
       selectInput('xcol', 'X Variable', names(iris)),
       selectInput('ycol', 'Y Variable', names(iris),
               selected=names(iris)[[2]]),
       numericInput('clusters', 'Cluster count', 3,
                min = 1, max = 9)
       ),
       mainPanel(
           plotOutput('plot1')
       )
   )
   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))
           plot(selectedData(),
               col = clusters()$cluster,
               pch = 20, cex = 3)
           points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
       })
   } 
   shinyApp(ui = ui, server = server)
   

4. Name the file: app.R
5. Click Submit to save the file.

The above code will create a very simple R Shiny dashboard.

Launch the R Shiny app

Now we can launch the code we just added:

1. Go to Apps.
2. Click Publish New App.
3. Make sure R Shiny is selected.
4. Set File as app.R.
5. Click Advanced to expand the sub-heading and enter the following information:
   • Image: A compatible R image. For example the latest cnvrg_r image.
   • Compute: any compute of your choice.
6. Click Publish.

cnvrg will now start doing all of the MLOps work to create a fully dynamic and shareable R Shiny dashboard. In just a few minutes all the configuration will be completed and you will be able to play around with the simple visualization of clustering.