7 May 2023

Thanks to Niels Ohlsen for for helping me add the books, and to Adejumo Ridwan Suleiman and Mokandil for their submissions.

If you’d like to support the Big Book of R financially, you’re welcome to leave a donation at my Kofi account. Thank you very much to the two folks who already gave a token of thanks

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Yet Again: R + Data Science

by Albert Rapp

There are one thousand and one introductory courses on data science using the statistical software R. This is another one of those. My own take at teaching a selection of topics in R and data science I
picked up throughout my time using R and reading a couple of those one thousand and one introductory courses.
The corresponding lecture videos can be found on YouTube.

https://www.bigbookofr.com/data-science.html#yet-again-r-data-science

Building energy statistical modelling

by Simon Rouchier

The topic of this book is statistical modelling and inference applied to building energy performance assessment. It has two target audiences: building energy researchers and practitioners who need a gentle introduction to statistical modelling; statisticians who may be interested in applications to energy performance.

https://www.bigbookofr.com/field-specific.html#building-energy-statistical-modelling

Data Management in Large-Scale Education Research

by Crystal Lewis

This book begins, like many other books in this subject area, by describing the research life cycle and how data management fits within the larger picture. The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase. Considerations on whether you should implement, and how to integrate those practices into your workflow will be discussed.

https://www.bigbookofr.com/data-databases-and-engineering.html#data-management-in-large-scale-education-research

An Introduction to Spatial Data Analysis and Statistics: A Course in R

by Antonio Paez

The objective of this book is to introduce selected topics in applied spatial statistics. My aim with this book is to introduce key concepts and techniques in the
statistical analysis of spatial data in an intuitive way. While there are other resources that offer more advanced treatments of every single
one of these topics, this book should be appealing to undergraduate students or others who are approaching the topic for the first time.

https://www.bigbookofr.com/geospatial.html#an-introduction-to-spatial-data-analysis-and-statistics-a-course-in-r

Introduction to R for Data Science: A LISA 2020 Guidebook

by Jacob D. Holster

This guidebook aims to provide readers an opportunity to make a start towards learning R for a variety of data science tasks, include (a) data cleaning and preparation, (b) statistical analysis, (c) data visualization, (d) natural language processing, (e) network analysis, and (f) Structural Equation Modeling

https://www.bigbookofr.com/data-science.html#introduction-to-r-for-data-science-a-lisa-2020-guidebook

An Introduction to ggplot2

by Ozancan Ozdemir

This book aims to show how you can make a well-known statistical plots by using ggplot2, and also how you can improve or customize them.

https://www.bigbookofr.com/data-visualization.html#an-introduction-to-ggplot2

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