Books

R Programming

• R4DS – R for Data Science by Garrett Grolemund and Hadley Wickham.
• R4DS Solutions – Solutions and notes for R4DS by Jeffrey B. Arnold. Work in progress.
• ModernDive – Statistical Inference via Data Science: A moderndive into R and the tidyverse by Chester Ismay and Albert Y. Kim.
• Hands-On Programming with R – Hands-On Programming with R by Garrett Grolemund.
• Advanced R – Advanced R by Hadley Wickham.
• Advanced R Solutions – Advanced R Solutions by Malte Grosser & Henning Bumann.
• The tidyverse style guide – The tidyverse style guide by Hadley Wickham.
• Tidy evaluation – Tidy evaluation by Lionel Henry and Hadley Wickham.
• Efficient R Programming – Efficient R programming by Colin Gillespie and Robin Lovelace.
• Handling Strings with R – Handling Strings with R by Gaston Sanchez.
• R Programming for Data Science – R Programming for Data Science by Roger D. Peng.
• Data Science at the Command Line – Data Science at the Command Line by Jeroen Janssens.

• Books written as part of the Johns Hopkins Data Science Specialization:

  • Exploratory Data Analysis with R – Basic analytical skills for all sorts of data in R.
  • R Programming for Data Science – More advanced data analysis that relies on R programming by Roger D. Peng.
  • Report Writing for Data Science in R – R-based methods for reproducible research and report generation.

• An Introduction to R – A very good introductory text on R, also covers some advanced topics.

• The R Inferno – Patrick Burns gives insight into R’s ins and outs along with its quirks!

• R Books List – List of R Books (not all of the them are free).

• Learning statistics with R – A tutorial for psychology students and other beginners by Danielle Navarro.

• Tidynomicon – A Brief Introduction to R for Python Programmers by Greg Wilson.

Packages, Git, Reproducible Reporting

• Happy Git with R – Happy Git and GitHub for the useR by Jenny Bryan.
• Introduction to Open Data Science – Learn how to work with data in an open, reproducible, and collaborative way using a workflow with R, RStudio, Git, and GitHub.
• R Packages – R Packages by Hadley Wickham and Jenny Bryan.
• What They Forgot to Teach You About R – What They Forgot to Teach You About R by Jennifer Bryan, Jim Hester.
• R Markdown – R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund.
• blogdown – blogdown: Creating Websites with R Markdown by Yihui Xie, Amber Thomas, Alison Presmanes Hill.
• bookdown – bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie.
• Mastering Software Development in R – Mastering Software Development in R by Roger D. Peng, Sean Kross, and Brooke Anderson.
• rOpenSci – rOpenSci Packages: Development, Maintenance, and Peer Review.
• Writing R extensions – A guide to extending R, describing the process of creating R add-on packages, writing R documentation, R’s system and foreign language interfaces, and the R API, written by the R Core Team.
• rstudio4edu – A Handbook for Teaching and Learning with R and RStudio.

Data Visualisation

• ggplot2 – ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham.
• R, Plotly and Shiny – Interactive web-based data visualization with R, plotly, and shiny by Carson Sievert.
• Data Visualization – Data Visualization: A practical introduction by Kieran Healy.
• Fundamentals of Data Visualization – Fundamentals of Data Visualization by Claus O. Wilke.
• Data Visualization with R – Data Visualization with R by Rob Kabacoff.
• R Graphics Cookbook – Practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly by Winston Chang.

Shiny

• Mastering Shiny – Mastering Shiny by Hadley Wickham, written to help app authors develop a deeper understanding of Shiny.
• Production-Grade Shiny – Engineering Production-Grade Shiny Apps by Colin Fay and co-authors.
• Shiny UI – Outstanding User Interfaces with Shiny.
• Shinyapps – Shinyapps.io user guide.

Statistics and Machine Learning

• Forecasting – Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos.
• Tidyverts – Tidy time series forecasting with fable.
• Data Science Live Book – Data Science Live Book by Pablo Casas.
• Feature Engineering and Selection – Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson.
• Hands-on Machine Learning with R -- Hands-on Machine Learning with R by Bradley Boehmke.
• caret – The caret Package by Max
• Text Mining with R – Text Mining with R: A Tidy Approach by Julia Silge and David Robinson.
• Geocomputation with R – Geocomputation with R by Robin Lovelace, Jakub Nowosad, Jannes Muenchow.
• Introduction to Statistical Learning with Application in R – A simplified and operational version of The Elements of Statistical Learning. Free softcopy provided by its authors.
• Statistical Rethinking – Statistical Rethinking with brms, ggplot2, and the tidyverse by A Solomon Kurz.
• Statistical Rethinking (2nd edition) – Statistical Rethinking with brms, ggplot2, and the tidyverse: Second edition by A Solomon Kurz.
• Machine Learning Yearning – Free eBook from Andrew Ng, teaches you how to structure Machine Learning projects.
• Advanced Statistical Computing – Book by Roger D. Peng on the development and implementation of statistical algorithms.
• Multivariate Analysis with Optimal Scaling – Book on exploratory multivariate statistics by professors from the University of Leiden.