I’m happy to announce 9 additions to the Big Book of R! Many thanks to Gary, Luis, Roger and Stephen for their contributions!

The collection stands at almost 450 free, open-source (and some paid) books!

Biological Data Science with R

• Stephen Turner

Introductory R book with a focus on tidy data analysis principles applied to life sciences.

https://www.bigbookofr.com/chapters/life%20sciences#biological-data-science-with-r

Marginal Effects Zoo

• Vincent Arel-Bundock

It includes over 30 chapters overs a wide range of topics, including how the marginaleffects package can facilitate the analysis of: Experiments, Observational data, Causal inference with G-Computation, Machine learning models, Bayesian modeling, Multilevel regression with post-stratification (MRP), Missing data, Matching, Inverse probability weighting, and Conformal prediction.

https://www.bigbookofr.com/chapters/packages#marginal-effects-zoo

Scaling Up With R and Arrow

• Nic Crane
• Jonathan Keane
• Neal Richardson

Arrow allows you to work with larger-than-memory data directly from R without needing to set up additional infrastructure. It implements the dplyr API, which means that if you are familiar with dplyr functions, you can use those same functions with arrow and don’t have to learn a whole new framework.

https://www.bigbookofr.com/chapters/big%20data#scaling-up-with-r-and-arrow

Coding Club: Creating an R Package

• Lisa DeBruine

Making an R package develops generic coding skills and gives you valuable insight to how R works. We’ll cover setting up a package project, creating functions, documenting them with roxygen, creating vignettes, unit testing, package testing, version control with git, and distribution with github.

You will need only very basic R skills and a willingness to learn. The only people this would not be suitable for are those with zero previous experience in R (unless you know you pick up coding languages quickly). If you can install packages and have written an analysis script, you’ll be fine.

https://www.bigbookofr.com/chapters/package%20development#coding-club-creating-an-r-package

Visualization for Social Data Science

• Roger Beecham

vis4sds provides end-to-end skills in visual data analysis. The book demonstrates how data graphics and modern statistics can be used in tandem to process, explore, model and communicate data-driven social science. It is packed with detailed data analysis examples, pushing you to do visual data analysis. As well as introducing, and demonstrating with code, a wide range of data visualizations for exploring patterns in data, Visualization for Social Data Science shows how models can be integrated with graphics to emphasise important structure and de-emphasise spurious structure and the role of data graphics in scientific communication – in building trust and integrity. Many of the book’s influences are from data journalism, as well as information visualization and cartography.

The book is being published by Chapman Hall/CRC Press in 2025, but the online development version will remain and be maintained by the author.

https://www.bigbookofr.com/chapters/social%20science#visualization-for-social-data-science

DataViz protocols: An introduction to data visualization protocols for wet lab scientists

• Joachim Goedhart

This book aims to lower the barrier for wet lab scientists to use R and ggplot2 for data visualization. First, by explaining some basic principles in data processing and visualization. Second, by providing example protocols, which can be applied to your own data, and I hope that the protocols serve as inspiration and a starting point for new and improved protocols.

https://www.bigbookofr.com/chapters/data%20visualization#dataviz-protocols-an-introduction-to-data-visualization-protocols-for-wet-lab-scientists

Probability of Default Rating Modeling with R: Comprehensive overview of the modeling processes, principles, and design

• Andrija Djurovic

This book bridges theory and practice in PD rating modeling, offering practical steps, real-world examples, and a focus on design. It enables readers to shape customized solutions for diverse institutions, transforming the landscape of credit risk modeling.

https://www.bigbookofr.com/chapters/finance#probability-of-default-rating-modeling-with-r-comprehensive-overview-of-the-modeling-processes-principles-and-design

Applied Data Science for Credit Risk: A Practical Guide in R and Python

• Andrija Djurovic

This book provides a practical guide to critical data science methods, focusing on their application in credit risk management. Using examples in R and Python, it presents step-by-step processes for applying various analytical techniques while highlighting the importance of aligning methods with the specific characteristics of the data. Designed for practitioners and those with foundational data science and banking knowledge, the book bridges theory and practice with real-world examples.

https://www.bigbookofr.com/chapters/finance#applied-data-science-for-credit-risk-a-practical-guide-in-r-and-python

An Introduction to Quantitative Text Analysis for Linguistics

• Jerid Francom

By the end of this textbook, readers will be able to identify, interpret and evaluate data analysis procedures and results to support research questions within language science. Additionally, readers will gain experience in designing and implementing research projects that involve processing and analyzing textual data employing modern programming strategies. This textbook aims to instill a strong sense of reproducible research practices, which are critical for promoting transparency, verification, and sharing of research findings.

https://www.bigbookofr.com/chapters/text%20analysis#an-introduction-to-quantitative-text-analysis-for-linguistics

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