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It’s been about 3 months since the last update and I’m looking forward to getting back to more regular additions to the collection which now stands at over 400 free, open-source R books! I have a small backlog to get through, but as always if you wish to submit a book, please do so by raising an issue via github or submitting this google form.
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I want to give a special thanks to Isabella Velásquez and Jonathan Kitt for their contributions to this update.
R in Production
Hadley Wickham
An assembly of notes about R in Production.
https://www.bigbookofr.com/chapters/workflow#r-in-production
Exploring Complex Survey Data Analysis Using R
Stephanie A. Zimmer, Rebecca J. Powell, Isabella C. Velásquez
We focus on R to introduce survey analysis. Our goal is to provide a comprehensive guide for individuals new to survey analysis but with some familiarity with statistics and R programming. We use a combination of the {survey} and {srvyr} packages and present the code following best practices from the tidyverse
https://www.bigbookofr.com/chapters/social%20science#exploring-complex-survey-data-analysis-using-r
Causal Inference in R
Malcolm Barrett, Lucy D’Agostino McGowan, Travis Gerke
Answering causal questions is critical for scientific and business purposes, but techniques like randomized clinical trials and A/B testing are not always practical or successful. The tools in this book will allow readers to better make causal inferences with observational data with the R programming language
https://www.bigbookofr.com/chapters/statistics#causal-inference-in-r
Cleaning Biodiversity Data in R
Atlas of Living Australia
This book is a practical guide for cleaning geo-referenced biodiversity data using R. It focuses specifically on the processes and challenges you’ll face with biodiversity data. As such, this book isn’t a general guide to data cleaning but a targeted resource for those working with or interested in ecology, evolution, and geo-referenced biodiversity data.
https://www.bigbookofr.com/chapters/life%20sciences#cleaning-biodiversity-data-in-r
Statistics in Natural Resources: Applications with R
Matthew Russell
This is a book for current or aspiring natural resource professionals that are required to analyze data and perform statistical analyses in their daily work. It is especially well suited to graduate students enrolled in natural resources, agricultural, and environmental science disciplines that need a quantitative understanding of the data they collect to better communicate their research findings.
More seasoned professionals that have previously had a course or two in statistics will also find the content familiar. This book can also serve as a bridge between professionals that understand statistics and want to learn how to perform analyses in R.
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The post 5 Books added to Big Book of R appeared first on Oscar Baruffa.
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Continue reading: 5 Books added to Big Book of R
Following the Expansion of R Programming Resources
After a three-month hiatus, there’s been a significant update to the vast collection of over 400 free, open source books related to R programming: The Big Book of R. This extensive collection serves as an excellent resource for both novice and proficient R users alike, providing comprehensive guides and tutorials on various aspects of R.
Fresh Additions to the Collection
Five new valuable additions were introduced in the recent update. Key among them include:
- “R in Production” by Hadley Wickham, a collection of notes about the practical applications of R programming.
- “Exploring Complex Survey Data Analysis Using R” by Stephanie A. Zimmer, Rebecca J. Powell, and Isabella C. Velásquez. It provides a detailed guide to survey analysis using R.
- “Causal Inference in R” by Malcolm Barrett, Lucy D’Agostino McGowan, and Travis Gerke.
- “Cleaning Biodiversity Data in R” by the Atlas of Living Australia; a practical guide specifically suited for individuals working with geo-referenced biodiversity data.
- “Statistics in Natural Resources: Applications with R” by Matthew Russell; particularly suited to natural resource professionals and graduate students in related disciplines needing a quantitative understanding of data for improved research communications.
Long-Term Implications and Future Developments
With these additions, practitioners of R programming now have an extended palette of resources that provide nuanced insights into specialized areas such as complex survey data analysis, cleaning biodiversity data, and causal inference, amongst others. The increased breadth and depth of available material imply a more enlightened R programming community with a wider understanding of the applications of R in various fields.
This will likely encourage further innovations in the way R is used, expanding its reach, enhancing its capabilities, and introducing newer use-cases. Alongside this, with continual efforts to add more content to this living library, there is the potential for the exponential growth of R programming knowledge transfer.
Actionable Advice
Given the constant updates in The Big Book of R, avid learners are advised to subscribe to the Big Book of R newsletter to ensure they are abreast of any new additions. This not only provides up-to-date knowledge about advancements and new resources in the field of R programming but also allows learners to identify and fill gaps in their knowledge through a focused and continuous learning approach.