Resources
We’ll add resources here as they become relevant or we find them. Feel free to send along things you’ve found useful!
Getting Started with R
- Instructions for installing R, RStudio, and all the tidyverse packages.
- Posit (formerly RStudio) Cloud Guide, run a full instance of RStudio in your web browser, a great way to start if you’re struggling with installation (you can get 25 free hours of use a month, but it’s not really designed to handle large datasets).
- Posit (formerly RStudio) Primers,
- The Tidyverse Style Guide, suggestions for good coding practices
- Stata to R::Cheat Sheet
- LearnR4Free
Free R Books
We use a chapter or four from several of these, but you might find other sections useful as well. (Incredibly, this is only a partial list.)
- James Scott. 2021. Data Science in R: A Gentle Introduction.
- Hadley Wickham and Garrett Grolemund. 2017. R for Data science. O’Reilly.
- Rafael A. Irizarry. 2021. Introduction to Data Science.
- Chester Ismay and Albert Y. Kim. 2021. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse.. CRC Press.
- Bradley Boehmke & Brandon Greenwell. 2021. Hands-On Machine Learning with R. Routledge.
- Paul Roback and Julie Legler. 2021. Beyond Multiple Linear Regression. Routledge.
- Kieran Healy. 2018. Data Visualization: A Practical Introduction. Princeton University Press.
- Carson Sievert. 2019. Interactive web-based data visualization with R, plotly, and shiny. CRC Press.