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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.
Your chance to contribute: Help me fund Big Book of R’s 2024 costs
Your chance to contribute: Help me fund Big Book of R’s 2024 costsHelp 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 
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.
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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Continue reading: Big Book of R at 400 [New milestone!]
Big Book of R reaches 400 entries: A delve into the implications and future developments
The Big Book of R is a valuable resource for statisticians, data analysts and anyone interested in learning practical R programming skills. Recently, the collection expanded to over 400 entries. This impressive library is helping thousands of individuals all around the world, with an estimated 80k visitors per year. But what do these admirable strides entail for the future? In this article, we look into the long-term implications and potential future developments resulting from this prolific growth.
Implications for the Data Science Community
The addition of new titles to the Big Book of R signifies a diversity of knowledge resources becoming readily available for interested learners. As the content ranges from introductory materials to advanced applications in various fields, it’s evident that data science is gaining traction as a multidisciplinary practice. These credentials help foster an inclusive community where anyone can hone their skills or contribute their expertise.
Knowledge shared is knowledge multiplied. This phrase fits perfectly in the case of the Big Book of R’s success story.
Potential Future Developments and Advice
Looking at the current trajectory, it’s reasonable to expect continuous growth in content and user base for the Big Book of R. As more and more professionals in diverse fields realize the benefits of data analysis, the collection’s expansion could see new categories relating to those professions, thereby widening its appeal.
In light of these implications and potential developments, here are some actionable insights:
- Contributors: Consider writing about unique applications of R in your fields. This will help to diversify the content and make it more resourceful for professionals from all industry sectors
- Learners: Regularly visit the Big Book of R website to check on updates and new entries
- Data Science Communities: Promote resources like Big Book of R and encourage data enthusiasts to contribute to such platforms
Funding the Big Book of R
The annual costs associated with hosting the Big Book of R, RScreencasts and OscarBaruffa domains is currently being funded through donations, book sales and affiliate sales. The community’s support in sharing the operational expenses is highly encouraged and appreciated.
The more the support, the further this resource can go to provide value for its users. Therefore, if you’ve found value from these resources, consider contributing however you can to help sustain this invaluable content flow in the long run.
Conclusion
The continuous growth of the Big Book of R is a positive development for the R programming and data science community. However, to sustain its operations and maintain its ever-growing content, funding is a key element. Thus, it behoves both users and contributors to work hand in hand to ensure the success of this invaluable resource. Let’s keep the wheel of knowledge turning!