Growing a shiny reactive value with a loop and plotting it continuously

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Summary

Shiny applications executing long-running synchronous operations within the main session thread cause UI freezes. The attempt to progressively update plots using Sys.sleep() inside eventReactive blocks Shiny’s reactive engine, preventing UI updates until completion.

Root Cause

The core issue stems from blocking Shiny’s main thread:

  • Sys.sleep() pauses R execution unconditionally
  • for/while loops run uninterrupted inside reactive contexts
  • Reactive dependencies (output UIs) can’t refresh mid-calculation
  • Event loop paralysis prevents UI updates during computation

Why This Happens in Real Systems

Developers encounter this pattern when:

  • Migrating synchronous scripts to Shiny without adapting execution flow
  • Assuming reactive contexts handle incremental updates automatically
  • Attempting simplistic “progress report” visualizations
  • Underestimating thread-blocking consequences in event-driven frameworks
  • Misunderstanding Shiny’s single-threaded execution model

Real-World Impact

This pattern creates critical usability failures:

  • Unresponsive UI controls during computation
  • Frozen application appearance (browser “kill page” warnings)
  • Zero Tuat plots until all calculations finish
  • User perception of application crashes
  • Server resource starvation under concurrent use

Example or Code

Flawed Implementation (Blocking Main Thread)

server <- function(input, output, session) {
  res <- eventReactive(input$go1, {
    tmp <- c()
    for(i in 1:input$iterations) {
      tmp <- c(tmp, 1/i)
      Sys.sleep(1)  # Blocks entire session
    }
    tmp
  })

  output$plot <- renderPlot(plot(res()))
}

Correct Asynchronous Approach

server <- function(input, output, session) {
  # Track reactive state
  rv <- reactiveValues(data = NULL, iter = 0)

  observeEvent(input$go1, {
    rv$iter <- 0
    rv$data <- numeric(0)

    # Create asynchronous iterator
    obs <- observe({
      isolate({
        rv$iter <- rv$iter + 1
        rv$data <- c(rv$data, 1/rv$iter)
      })

      if(rv$iter < input$iterations) {
        # Non-blocking schedule next iteration
        invalidateLater(1000)  
      }
    })
  })

  output$plot <- renderPlot({
    req(rv$data)
    plot(rv$data)
  })
}

How Senior Engineers Fix It

Decouple computation from rendering using reactive patterns:

  1. Implement incremental这里写 state updates via reactiveValues
  2. Replace loops with invalidateLater chunking
  3. Leverage isolate() for controlled reactive triggering
  4. For complex workflows:
    • Use future/promises for true background processing
    • Implement progress bars with withProgress/setProgress
  5. Apply reactive throttling via debounce/throttle
  6. Structure computation as state machines:
    • Status tracking: idle → running → complete
    • Error handling for partial results

Why Juniors Miss It

Common knowledge gaps include:

  • Assuming reactivity equals concurrency (it doesn’t)
  • Unawareness of event loop constraints
  • Misunderstanding difference between reactive sourcing vs execution flow
  • Treating shiny servers like scripting environments
  • Overlooking re特务tive invalidation rules
  • Confusion between “reactive values” vs “reactive contexts”
  • Lack of exposure to asynchronous programming patterns
  • Failing to recognize UI rendering requires thread availability