Introduction

R is an in-memory language. When working with large datasets, long-running loops, parallel processes, or external web APIs, system memory can quickly fill up, causing R to crash.

devkit provides resource optimization and resilience modules designed to keep your environment lean, secure, and crash-proof.

🧹 Interactive Memory Cleanup

R does not always immediately release memory back to the operating system when objects are deleted. Large data frames or matrices can linger in your environment.

Sweeping Large Global Objects

sweep_memory() inspects the global environment, identifies objects exceeding a specified size threshold (in MB), and prompts you to delete them.

# Interactively sweep objects larger than 10MB
sweep_memory(threshold = 10)

Cleaning Temporary Files & Orphaned Devices

R sessions generate temporary directories and graphics devices (e.g., PDFs, PNGs). If a script errors out before closing a device, the file handles remain locked.

hunt_zombies() scans for and closes orphaned graphics devices and removes standard R temp files.

# Close zombie graphics devices and flush temp files
hunt_zombies()

sweep_temp_cache() specifically targets cache directories created by packages (such as knitr, raster, or memoise), reclaiming disk space.

# Flush cache directories to free disk space
sweep_temp_cache()

🛡️ Safeguarding Iterations with the Loop Guardian

When running large loops that generate or accumulate data, you run the risk of running out of RAM (Out of Memory/OOM crash).

loop_guardian() checks your system’s free memory at the end of each iteration. If the available RAM drops below a critical threshold, it halts the loop safely, saving your state and preventing a system-wide crash.

# Define a long loop with the loop guardian
data_list <- list()
for (i in 1:1000) {
  # Perform heavy computation
  data_list[[i]] <- runif(1e6)
  
  # Guard loop; will halt if free memory is less than 500MB
  loop_guardian(threshold_mb = 500, current_iteration = i)
}

💾 Crash-Resilient Batch Processing (Save & Resume)

For jobs that run for hours or days, an unexpected error or power outage can wipe out all progress.

dispatch_checkpoints() wraps batch operations in a checkpointing system. It saves progress at specified intervals. If the run is interrupted, re-running the command automatically resumes execution from the last saved checkpoint.

# List of items to process
items <- paste0("item_", 1:100)

# Resilient batch processing with checkpoints
results <- dispatch_checkpoints(
  items = items,
  process_fun = function(item) {
    # Perform computation
    Sys.sleep(0.1)
    return(paste(item, "processed"))
  },
  checkpoint_dir = "checkpoints",
  checkpoint_interval = 10
)

⚡ Scaffolding Parallel Pipelines

Setting up parallel clusters in R requires boilerplate code (registering cores, setting up clusters, handling errors, and cleaning up clusters on exit).

scaffold_parallel() generates a production-ready parallel execution template tailored to your specific data object and core requirements.

# Generate parallel setup code for a dataframe 'sales_data' inside a function
scaffold_parallel(
  data_obj = "sales_data",
  func_name = "process_sales",
  cores = 4
)

🌐 Resilient and Polite Network Requests

When fetching data from web APIs, network hiccups or rate limits (HTTP status 429) can break your pipeline.

network_diplomat() wraps standard HTTP requests, implementing exponential backoff (retrying with increasing delays) and automatically respecting the rate limit headers sent by servers.

# Make a rate-resilient API request
api_response <- network_diplomat(
  url = "https://api.example.com/data",
  method = "GET",
  max_retries = 5,
  backoff_factor = 2
)

Performance, Memory, and Resilience Workflows