A curated collection of the best free R programming resources — selected for supply chain professionals, data analysts, and anyone who wants to turn data into better decisions.

Last updated: March 2026

R Basics & Learning R

• R for Data Science (2nd Edition) — Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund. The essential introduction to modern R and the tidyverse: importing, tidying, transforming, visualizing, and modeling data.
• Advanced R (2nd Edition) — Hadley Wickham. Deep dive into R internals: environments, functional programming, metaprogramming, performance, and OOP systems.
• Hands-On Programming with R — Garrett Grolemund. Beginner-friendly introduction through hands-on projects covering functions, data structures, and simulation.
• R Cookbook (2nd Edition) — JD Long, Paul Teetor. Over 275 practical recipes for data analysis, statistics, and graphics.
• R Packages (2nd Edition) — Hadley Wickham, Jennifer Bryan. The definitive guide to creating, testing, documenting, and sharing R packages.
• Big Book of R — Oscar Baruffa (curator). A directory of 400+ free R books organized by topic — the master index of free R resources.

Data Visualization & ggplot2

• ggplot2: Elegant Graphics for Data Analysis (3rd Edition) — Hadley Wickham, Danielle Navarro, Thomas Lin Pedersen. The authoritative reference on the grammar of graphics in R.
• R Graphics Cookbook (2nd Edition) — Winston Chang. 150+ practical recipes for creating publication-quality graphs with ggplot2.

Data Wrangling & Tidyverse

• R for Data Science — Transform Section — The best starting point for dplyr, tidyr, readr, stringr, and the pipe operator.
• Tidyverse Skills for Data Science — Carrie Wright, Shannon Ellis, Stephanie Hicks, Roger Peng. Johns Hopkins course covering the tidyverse end-to-end.
• Learn the Tidyverse — Official curated resources from the tidyverse maintainers: books, cheatsheets, and tutorials for each package.
• R Markdown: The Definitive Guide — Yihui Xie, J.J. Allaire, Garrett Grolemund. The authoritative reference for reproducible reports and documents.

Interactive Dashboards

• Mastering Shiny — Hadley Wickham. Comprehensive guide to building interactive web apps with Shiny: reactive programming, modules, testing, and deployment.
• Interactive Web-Based Data Visualization with R, plotly, and Shiny — Carson Sievert. The definitive book on plotly for R — interactive charts, maps, 3D plots, animations, and linked views.
• htmlwidgets for R — Framework and gallery for 50+ interactive widget packages (Leaflet, DT, dygraphs, networkD3, etc.).
• Quarto Dashboards — Official guide for building dashboards in Quarto — the modern successor to flexdashboard.
• Shiny Official Site — Official documentation, tutorials, gallery, and deployment guides for Shiny.
• Quarto Guide — Comprehensive documentation for Quarto, Posit’s next-generation publishing system (successor to R Markdown).

Forecasting & Time Series

• Forecasting: Principles and Practice (3rd Edition) — Rob J. Hyndman, George Athanasopoulos. The gold-standard textbook for time series forecasting — covers ETS, ARIMA, dynamic regression, and hierarchical forecasting using the tidyverts ecosystem.
• A Little Book of R for Time Series — Avril Coghlan. A concise, beginner-friendly introduction covering decomposition, exponential smoothing, and ARIMA basics.

Supply Chain Analytics with R

• planr — Tools for Supply Chain Management — R package for demand/supply planning: projected inventories, coverages, and replenishment plans at any time bucket granularity.
• SCperf — Supply Chain Performance — R package implementing inventory models (newsboy, reorder point, Wagner-Whitin) and bullwhip effect calculations.

Statistics & Machine Learning

• An Introduction to Statistical Learning (2nd Edition) — James, Witten, Hastie, Tibshirani. The most accessible introduction to statistical learning and ML — free PDF, all examples in R.
• The Elements of Statistical Learning (2nd Edition) — Hastie, Tibshirani, Friedman. The advanced companion to ISLR — free PDF covering boosting, SVMs, neural networks, and random forests in depth.
• Tidy Modeling with R — Max Kuhn, Julia Silge. Official guide to the tidymodels framework: preprocessing, model fitting, tuning, resampling, and evaluation.
• Modern Statistics with R (2nd Edition) — Mans Thulin. Comprehensive modern statistics textbook using R — from data wrangling to inference, regression, and prediction.
• Supervised Machine Learning for Text Analysis in R — Emil Hvitfeldt, Julia Silge. Guide to NLP with tidymodels — tokenization, embeddings, classification, and regression on text data.
• Text Mining with R: A Tidy Approach — Julia Silge, David Robinson. Tidy text analysis: sentiment analysis, tf-idf, topic modeling, and more.