Quick Tip: Enable reactlog to visualize which reactive expressions are firing and their dependencies
2 Reactive Expression Debugging
# Trace reactive executionmy_reactive <-reactive({cat("Reactive executing at:", Sys.time(), "\n")cat("Input values:", input$x, input$y, "\n")# Your reactive code result <-process_data(input$x, input$y)cat("Result rows:", nrow(result), "\n") result})# Stop unwanted reactive cascadesoutput$plot <-renderPlot({# Only react to specific inputs data <-isolate(expensive_data())plot(data[[input$x_var]], data[[input$y_var]])})# Control reactive timingfiltered_data <-reactive({my_data() %>%filter(category == input$filter)}) %>%bindEvent(input$update_btn)
Debug Pattern
Add cat() statements → Check reactive log → Use isolate() or bindEvent() to control flow
3 Common Error Solutions
# Fix: Output not updatingoutput$my_output <-renderText({req(input$required_input) # Wait for inputreq(input$text_input !="") # Non-empty checkpaste("Result:", input$text_input)})# Fix: Object not found errorsoutput$safe_plot <-renderPlot({tryCatch({ data <-get_data(input$dataset)req(input$x_var %in%names(data))plot(data[[input$x_var]], data[[input$y_var]]) }, error =function(e) {plot.new()text(0.5, 0.5, "Data not available") })})# Fix: App won't start# Check for syntax errors and missing packagesif (!require(ggplot2)) install.packages("ggplot2")
Safety First: Always use req() to validate inputs before processing
4 Performance Issue Solutions
# Cache expensive computationsexpensive_analysis <-reactive({heavy_computation(input$large_dataset)}) %>%bindCache(input$large_dataset)# Show progress for long operationsoutput$results <-renderTable({withProgress(message ="Processing...", value =0, {setProgress(0.3, detail ="Loading data...") data <-load_data()setProgress(0.7, detail ="Analyzing...") result <-analyze_data(data)setProgress(1.0) result })})# Optimize data loadingbase_data <-reactiveVal()# Load once on startupobserve({base_data(read_large_file())}, once =TRUE)# Fast filtering of pre-loaded datafiltered_data <-reactive({base_data() %>%filter(category == input$filter)})
Performance Pattern
Load data once → Cache expensive operations → Use progress indicators → Filter pre-loaded data
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