61 Resources
61.1 R Project
The R Manuals include a number of resources, including:
- Introduction to R
- CRAN task views offer curated lists of packages by topic
61.2 R programming
- Advanced R by Hadley Wickham
- Efficient R Programming by Colin Gillespie & Robin Lovelace
- Rcpp: Seamless R and C++ Integration
- Parallel and Distributed Processing with future
61.3 R for data science
- R Programming for Data Science by Roger D. Peng, based mostly on base R, and also covers the basics of dplyr.
- Data wrangling, exploration, and analysis with R b y Jenny Bryan
- R for Data Science by Hadley Wickham & Garrett Grolemund
61.4 Markdown
61.5 R markdown
Note: Quarto has superseded R Markdown as the recommended tool for authoring documents in R.
- R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund
- bookdown: Authoring Books and Technical Documents with R Markdown: how to make websites like this one you are on right now
61.6 Quarto
61.7 Writing R Documentation
61.8 Graphics
61.8.1 ggplot2
61.8.2 Plotly
61.9 Git and GitHub
- GitHub guides
- Pro Git Book by Scott Chacon and Ben Straub
61.10 Machine Learning
- An Introduction to Statistical Learning offers an accessible view of core learning algorithms, without being math-heavy.
- Elements of Statistical Learning offers a deeper and more extensive view on learning algorithms.
- Machine Learning with rtemis
61.11 Datasets
A number of online repositories offer free access to datasets suitable for data science / statistics / machine learning. Some of them are:
