analysing shiny server log to create statistics on usage

Question

I would like to figure out, which feature of my shiny app is used most... What is the preferred way on doing this? At the moment I parse the shiny server access.log and could find some links like .../session/69d4f32b3abc77e71097ae4beefbd135/dataobj/lifecycle_table which indicates when a DT object called lifecycle_table is loaded. But I can only see this for these DT objects. Are there better ways? Would love to create this statistics per unique IP. Basically which tabs are clicked. I am not interested in the search strings etc.

Answer 1

Edit: For getting info about the clicked tabs have a look in: ?tabsetPanel You see that you can specify an id for the panel. So tabsetPanel(id="tabs",...) will enable you to track the selected tabpanel on the server side with input$tabs.

See an example below: (based on https://shiny.rstudio.com/articles/tabsets.html)

library(shiny)

ui <- shinyUI(pageWithSidebar(

  # Application title
  headerPanel("Tabsets"),

  # Sidebar with controls to select the random distribution type
  # and number of observations to generate. Note the use of the br()
  # element to introduce extra vertical spacing
  sidebarPanel(
    radioButtons("dist", "Distribution type:",
                 list("Normal" = "norm",
                      "Uniform" = "unif",
                      "Log-normal" = "lnorm",
                      "Exponential" = "exp")),
    br(),

    sliderInput("n", 
                "Number of observations:", 
                value = 500,
                min = 1, 
                max = 1000)
  ),

  # Show a tabset that includes a plot, summary, and table view
  # of the generated distribution
  mainPanel(
    tabsetPanel(id = "tabs", 
                tabPanel("Plot", plotOutput("plot")), 
                tabPanel("Summary", verbatimTextOutput("summary")), 
                tabPanel("Visited Tabs", tableOutput("table"))
    )
  )
))


# Define server logic for random distribution application
server <- shinyServer(function(input, output, session) {
  global <- reactiveValues(visitedTabs = c())

  # Reactive expression to generate the requested distribution. This is 
  # called whenever the inputs change. The renderers defined 
  # below then all use the value computed from this expression
  data <- reactive({  
    dist <- switch(input$dist,
                   norm = rnorm,
                   unif = runif,
                   lnorm = rlnorm,
                   exp = rexp,
                   rnorm)

    dist(input$n)
  })

  observe({
    input$tabs
    isolate({
      userTabInfo <- paste0(" selected: ",input$tabs)
      print(userTabInfo)
      global$visitedTabs = c(global$visitedTabs, userTabInfo)
    })
  })

  # Generate a plot of the data. Also uses the inputs to build the 
  # plot label. Note that the dependencies on both 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(data(), 
         main=paste('r', dist, '(', n, ')', sep=''))
  })

  # Generate a summary of the data
  output$summary <- renderPrint({
    str(session$userData)
    # session$user
  })

  # Generate an HTML table view of the data
  output$table <- renderTable({
    data.frame(global$visitedTabs)
  })
})

shinyApp(ui, server)

Concerning the IP: I know about 4-5 code snippets to get the IP and they all use JSS or XSS-style how you call it :) I agree it should be somehow possible, but since people already asked 3-4 years ago, I am not sure its really a matter of awareness from the shiny team. Hope the tab tracking helps anyway. If you like I can add the JS snippet to get the IP again.