Resources
An attempt to gather all the links and resources I recommended throughout.
If you look at one thing, make it:
Other things I mentioned:
Everything I know is from Jenny Bryan:
- R packages, Hadley Wickham and Jenny Bryan.
- Testing chapter in R Packages.
- What They Forgot to Teach You About R, Jenny Bryan, Jim Hester, Shannon Pileggi, E. David Aja.
- Happy git and GitHub for the useR, Jenny Bryan, the STAT 545 TAs, Jim Hester.
- Code “smells” and feels, Jenny Bryan.
- Lazy evalution, Jenny Bryan.
- Good enough practices in scientific computing, Greg Wilson, Jenny Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, Tracy Teal.
Miscellaneous goodness:
- Best Practices for Scientific Computing
- Evaluating the Design of the R Language This is for my computer scientists who want the nitty-gritty about R as a language.
- Documenting datasets
- CRAN task views
- R Packages for Data Quality Assessments and Data Monitoring: A Software Scoping Review with Recommendations for Future Developments and list on GitHub
From the Posit/tidyverse folks:
- Tidyverse style guide, the tidyverse team
- Tidy design principles
- Three functions in a trenchcoat case study of
rep()from tidy design. - R for Data Science, Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund.
- Packages, by Hannah Frick
- What are quosures and when are they needed?, rlang documentation.
- Masking tidyverse functions
- Advanced R, Hadley Wickham
testthatdocumentation- Interactivity and Programming in the Tidyverse, Lionel Henry on data-masking and tidy-select.
- Programming with dplyr
- Using ggplot2 in packages
- Tidy evaluation in 5 mins, Hadley Wickham
- {haven} package for working with SAS, SPSS, Stata data files
- lifecycle stages