55 Resources
55.1 R Project
The R Manuals include a number of resources, including:
- Introduction to R
- CRAN task views offer curated lists of packages by topic
55.2 Markdown
55.3 R markdown
- 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
55.4 Quarto
55.5 Writing R Documentation
55.6 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
55.7 Graphics
55.7.1 ggplot2
55.7.2 Plotly
55.8 Advanced R
- Efficient R Programming by Colin Gillespie & Robin Lovelace
- Rcpp: Seamless R and C++ Integration
- Parallel and Distributed Processing with future
55.9 Git and GitHub
- GitHub guides
- Pro Git Book by Scott Chacon and Ben Straub
55.10 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
55.11 Getting help
Stack Overflow is a massively popular Q&A site for programmers, part of the wider Stack Exchange network. Many R-related web searches will bring up posts in Stack Overflow. You can view all questions tagged with “r”.
When posting a question in any setting, it is strongly recommended to provide a minimal reproducible example (MRE). Stack Overflow provides guidelines on how to create an MRE.
55.12 Datasets
A number of online repositories offer free access to datasets suitable for data science / statistics / machine learning. Some of them are: