Shiny Debugging & Troubleshooting Cheatsheet: Fix Issues Fast

Shiny Debugging & Troubleshooting

Essential diagnostic patterns and quick fixes for common Shiny development problems

1 Debug Setup & Essential Tools

# Enable comprehensive debugging
options(
  shiny.error = browser,
  shiny.reactlog = TRUE,
  shiny.trace = TRUE
)

# View reactive dependencies
reactlogShow()

# Performance profiling
library(profvis)
profvis({
  # Your expensive Shiny operations
  expensive_reactive()
})

# Memory monitoring
cat("Memory:", gc()[2,2], "MB\n")

Quick Tip: Enable reactlog to visualize which reactive expressions are firing and their dependencies

2 Reactive Expression Debugging

# Trace reactive execution
my_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 cascades
output$plot <- renderPlot({
  # Only react to specific inputs
  data <- isolate(expensive_data())
  plot(data[[input$x_var]], data[[input$y_var]])
})

# Control reactive timing
filtered_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 updating
output$my_output <- renderText({
  req(input$required_input)  # Wait for input
  req(input$text_input != "")  # Non-empty check
  
  paste("Result:", input$text_input)
})

# Fix: Object not found errors
output$safe_plot <- renderPlot({
  tryCatch({
    data <- get_data(input$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 packages
if (!require(ggplot2)) install.packages("ggplot2")

Safety First: Always use req() to validate inputs before processing

4 Performance Issue Solutions

# Cache expensive computations
expensive_analysis <- reactive({
  heavy_computation(input$large_dataset)
}) %>% bindCache(input$large_dataset)

# Show progress for long operations
output$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 loading
base_data <- reactiveVal()

# Load once on startup
observe({
  base_data(read_large_file())
}, once = TRUE)

# Fast filtering of pre-loaded data
filtered_data <- reactive({
  base_data() %>% filter(category == input$filter)
})

Performance Pattern

Load data once → Cache expensive operations → Use progress indicators → Filter pre-loaded data

5 Deployment Issue Solutions

# Environment-aware configuration
is_local <- Sys.getenv("SHINY_PORT") == ""

if (is_local) {
  data_path <- "local_data/"
} else {
  data_path <- "data/"  # Production path
}

# Robust error handling for production
output$results <- renderTable({
  tryCatch({
    process_data(input$file)
  }, error = function(e) {
    # Log error for debugging
    cat("Error:", e$message, "\n")
    
    # User-friendly message
    data.frame(Error = "Processing failed. Please try again.")
  })
})

# Check package dependencies
required_packages <- c("shiny", "ggplot2", "dplyr")
missing <- required_packages[!required_packages %in% 
  installed.packages()[,"Package"]]

if(length(missing) > 0) {
  install.packages(missing)
}

Deployment Tip: Use relative paths and test package availability before deployment

6 Advanced Error Handling

# Comprehensive input validation
validate_inputs <- function(input) {
  validate(
    need(input$file, "Please upload a file"),
    need(input$columns != "", "Please select columns"),
    need(is.numeric(input$threshold), "Threshold must be numeric")
  )
}

# Safe reactive wrapper
safe_reactive <- function(expr, fallback = NULL) {
  reactive({
    tryCatch(expr, error = function(e) {
      showNotification(paste("Error:", e$message), type = "error")
      fallback
    })
  })
}

# Error logging system
log_error <- function(message, session = getDefaultReactiveDomain()) {
  error_info <- list(
    timestamp = Sys.time(),
    session_id = session$token,
    message = message,
    user_agent = session$clientData$url_search
  )
  
  # Log to file or monitoring service
  write(jsonlite::toJSON(error_info), "error.log", append = TRUE)
}

Error Strategy

Validate inputs → Wrap in tryCatch → Log errors → Show user-friendly messages

Systematic Debugging Workflow

The TRACE Method for Shiny Issues:

T - Test Isolation: Isolate the problematic component in a minimal app

R - Read Errors: Examine error messages and console output carefully

A - Analyze Dependencies: Check reactive relationships with reactlog

C - Check I/O: Verify input values and output expectations

E - Examine Flow: Trace execution path with debug statements

Complete Troubleshooting Guide Testing & Debugging Best Practices

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