Drumroll please……………….!!!!!

With the addition of these 7 new books, the collection now stands at over 400 entries of (mostly) free R books! Many thanks to Markus Gesmann, Jacobus, Max Cotera, Luis, Olivier Leroy and Gary for their latest contributions.

This a truly a one-of-a-kind resource. I want to give immense thanks to all the authors, contributors and of course, the broader R community. Big Book of R currently gets about 80k visitors per year which is a great estimate to the top quality content. From the humble beginnings of 100 books in August 2020 (which even then was a sizeable collection), it’s grown to a pretty impressive library.

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Your chance to contribute: Help me fund Big Book of R’s 2024 costs

Help me cover the annual costs of hosting Big Book of R and the newsletter! Big Book of R, RScreencasts and OscarBaruffa domains; $50/ Plausible.io privacy-focussed Analytics: $ 100. ConvertKit Mailing service (newsletter, to make sure you get notified of new books): $500.

I’m already putting all previous donations, recent book sales and affiliate sales into the fund and your help will be much appreciated!

As the time of print, I’ve already reached 31% of the goal

Help me hit the target

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And now, onto the new entries!

Big Data Analytics

by Ulrich Matter

This is the website of the 1st edition of “Big Data Analytics”. The book provides an introduction to Big Data Analytics for academics and practitioners

https://www.bigbookofr.com/big-data#big-data-analytics

Hierarchical Compartmental Reserving Models

by Markus Gesmann, Jake Morris

Hierarchical compartmental reserving models provide a parametric framework for describing aggregate insurance claims processes using differential equations. We discuss how these models can be specified in a fully Bayesian modeling framework to jointly fit paid and outstanding claims development data, taking into account the random nature of claims and underlying latent process parameters. We demonstrate how modelers can utilize their expertise to describe specific development features and incorporate prior knowledge into parameter estimation. We also explore the subtle yet important difference between modeling incremental and cumulative claims payments. Finally, we discuss parameter variation across multiple dimensions and introduce an approach to incorporate market cycle data such as rate changes into the modeling process. Examples and case studies are shown using the probabilistic programming language Stan via the brms package in R.

https://www.bigbookofr.com/field-specific#hierarchical-compartmental-reserving-models

Targeted Learning in R: Causal Data Science with the tlverse Software Ecosystem

by Will Landau

targets has an elaborate structure to support its advanced features while ensuring decent performance. This bookdown site is a design specification to explain the major aspects of the internal architecture, including the data storage model, object oriented design, and orchestration and branching model

https://www.bigbookofr.com/packages#the-targets-r-package-design-specification

R para epidemiología aplicada y salud pública

by Neale Batra, Alex Spina, Paula Bianca Blomquist

EpiRhandbook es un manual de referencia de R aplicado a la epidemiología y la salud pública.

(This is the EpiR Handbook in Spanish)

https://www.bigbookofr.com/espa%C3%B1ol.html#r-para-epidemiolog%C3%ADa-aplicada-y-salud-p%C3%BAblica

Targeted Learning in R: Causal Data Science with the tlverse Software Ecosystem

by Mark van der Laan, Jeremy Coyle, Nima Hejazi, Ivana Malenica, Rachael Phillips, Alan Hubbard

It is a fully reproducible, open-source, electronic handbook for applying Targeted Learning methodology in practice using the software stack provided by the tlverse ecosystem.

https://www.bigbookofr.com/data-science#targeted-learning-in-r-causal-data-science-with-the-tlverse-software-ecosystem

Psychometrics in Exercises using R and RStudio

by Anna Brown

Provides a comprehensive set of exercises for practicing all major Psychometric techniques using R and RStudio. The exercises are based on real data from research studies and operational assessments, and provide step-by-step guides that an instructor can use to teach students, or readers can use to learn independently. Each exercise includes a worked example illustrating data analysis steps and teaching how to interpret results and make analysis decisions, and self-test questions that readers can attempt to check own understanding.

https://www.bigbookofr.com/social-science#psychometrics-in-exercises-using-r-and-rstudio

R Bytecode Book

by Mikefc aka coolbutuseless

This is a book about the bytecode which drives the virtual machine at the heart of R code execution. This book represents my current (and still evolving) understanding of bytecode, and I hope to use this understanding to break R in new and exciting ways.

https://www.bigbookofr.com/r-programming#r-bytecode-book

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