Chapter 7 Learn more

Want to continue learning R and statistics? Great! Here are some resources.

7.1 Books

(Many of these are available as bookdown books on the web!)

7.1.1 R/data science

More suggestions from the tidyverse page

7.1.2 Statistics/modeling

  • Ismay and Kim (2021)
  • James et al. (2013) or Friedman et al. (2001)
  • Baumer, Kaplan, and Horton (2017)

7.2 Online learning/courses

7.3 Videos

Less interactive, but maybe you want to watch awesome talks about R.

7.4 Blogs, etc

  • Simply Statistics, blog by Roger Peng, Jeff Leek, and Rafa Irizarry
  • Not so standard deviations (soundcloud, or, wherever you get your podcasts) podcast by Hilary Parker and Roger Peng
  • R Weekly open-sourced aggregator of all things R

7.5 Twitter!

Who to follow:


7.6 Communities

You may want a physical or online place to go to learn more, ask questions, and get support.

7.6.1 Online

7.6.2 “Physical”


Baumer, Benjamin S, Daniel T Kaplan, and Nicholas J Horton. 2017. Modern Data Science with R. CRC Press.

Chang, Winston. 2013. R Cookbook. O’Reilly.

Friedman, Jerome, Trevor Hastie, Robert Tibshirani, and others. 2001. The Elements of Statistical Learning. Vol. 1. 10. Springer series in statistics New York.

Ismay, Chester, and Albert Y Kim. 2021. Statistical Inference via Data Science. Chapman; Hall/CRC.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Vol. 112. Springer.

Leek, Jeff. 2015. Elements of Data Analytic Style.

Peng, Roger. 2018. The Art of Data Science.

Peng, Roger. 2020. R Programming for Data Science.

Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science. O’Reilly.