Your Complete Guide to Becoming a Full-Stack ML Engineer with R.

The R language has long been the workhorse of statistical analysis, yet when data scientists want to move into machine learning, they often face a frustrating gap. Most existing R books on ML stop at model training and evaluation, leaving readers stranded when it comes to the real-world challenges of deploying models into production, monitoring their performance, and explaining their predictions to non-technical stakeholders.

This book is designed to bridge that gap.

Written for data analysts and statisticians who are already comfortable with R and the tidyverse, this book takes you on an end-to-end journey through the entire machine learning lifecycle—from the first line of code to a live, containerized API in the cloud.

What You Will Learn:

Environment Setup: Choose the right R version, install RStudio, and use renv for project-specific, reproducible libraries—the foundation of all great ML projects.

Professional Project Structure: Organize your code, data, and models like a seasoned engineer, and master Git and GitHub for version control and collaboration.

Data Preparation: Transform raw, messy data into a clean, structured foundation for ML. Master advanced feature engineering, handle missing values and outliers, and create powerful features from dates, text, and interactions.

Model Building: Build, tune, and evaluate a wide range of machine learning models using both caret and tidymodels. Understand the trade-offs between different algorithms and when to use each.

Model Interpretability: Learn to explain your models using LIME, SHAP, and the iml package. Build trust, ensure regulatory compliance, and debug models effectively.

Model Deployment: Turn your R model into a production-ready REST API with Plumber. Containerize it with Docker using the Rocker project, and deploy it to AWS, Azure, or Google Cloud.

MLOps and Monitoring: Use vetiver for model versioning, deployment, and monitoring. Implement logging, alerting, and performance tracking to keep your models healthy in production.

Key Features:

Practical, hands-on approach: Every chapter is built around concrete case studies with real code and data.

Comprehensive coverage: 25+ worked examples per chapter, from fraud detection to image classification to gene expression analysis.

Production-focused: The only book on R machine learning that covers end-to-end deployment with Plumber, Docker, and cloud platforms.

Reproducible science: Learn to use renv, Docker, and Git to make your work perfectly reproducible.

All code and data available: The companion GitHub repository contains everything you need to follow along.

Whether you are a data analyst looking to expand your skills, a statistician moving into machine learning, or a data scientist aiming to see your models through to production, this book will equip you with the practical knowledge and confidence to succeed.

Your journey from data preparation to deployment starts here.