Parallel Processing in R: A Practical Guide

Introduction

Parallel processing can significantly speed up computationally intensive tasks in R. This guide explores different approaches and best practices for parallel computing.

Using the future Package

library(future)
library(furrr)

# Set up parallel processing
plan(multisession)

# Parallel map operation
result <- future_map(1:1000, function(x) {
  # Complex computation
  sqrt(sum(rnorm(1000)^2))
}, .progress = TRUE)

Memory Management

Learn about efficient memory usage and avoiding common pitfalls in parallel processing.

Resources

• future Package Documentation
• furrr Package Documentation