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

• Leek (2015)
• Peng (2020)
• Peng (2018)
• Chang (2013)
• Wickham and Grolemund (2017)

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

• Johns Hopkins Coursera Course on R. Part of the Data Science Specialization. Courses are free (I think?) but the certificate costs money.
• DataQuest
• I do not recommend DataCamp
• DC instructors have moved a lot of their materials elsewhere. Check out learnR4free (That’s the English link, there are also resources in Español and Türkçe.)

7.3 Videos

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

• rstudio::conf is a yearly event, and they record and post videos of many talks rstudio::global 2021, rstudio::conf 2020, etc
  • one of my favorite recent talks is Jenny Bryan’s Object of type ‘closure’ is not subsettable, about error messages and debugging in R
  • another great one is Miles McBain’s Our colour of magic: The open sourcery of fantastic R packages
  • oh! Hilary Parker’s Cultivating creativity in data work
  • cut me off! I could pick talks all day
• Another R-related conference is useR! Many of their talks are on the R Consortium YouTube
  • typically, you can’t go wrong with the plenary talks
  • try Julia Stewart Lowndes’ 2019 keynote, R for better science in less time
  • Roger Peng’s 2018 keynote, Teaching R to New Users: From tapply to Tidyverse
  • shameless self-promotion? My 2020 keynote, Speaking R
• More shameless self-promotion, I have tons of videos on YouTube. A lot of the material ehre was modified from my STAT 320 materials, so watching some of those videos might help iluminate the code here.

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:

• me! Amelia McNamara, University of St Thomas
• Hadley Wickham, RStudio
• Jenny Bryan, on leave from UBC, at RStudio
• Hilary Parker, data scientist at StitchFix
• Roger Peng, biostatistician at JHU
• Jeff Leek, biostatistician at JHU
• David Robinson, formerly of StackOverflow, now Heap
• Karl Broman, biostatistician at UW
• Karthik Ram, rOpenSci
• Renee Teate, BecomingDataSci
• Mine Cetinkaya-Rundel, University of Edinburgh, RStudio.
• Julia Silge, tidytext, RStudio, formerly of StackOverflow

Hashtags:

• #rstats
• #tidyverse
• #rcatladies
• #tidytuesday

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

• R for Data Science Online Learning Community A place to hang out, ask questions, work through books with people. Primarily on Slack.

• StackOverflow A place to search, not to post (yet?)

• RStudio Community A friendly place to ask questions, even if they have been asked before!

• When posting online, you probably need/want to make a “reprex.” The tidyverse site has lots of guidelines!

7.6.2 “Physical”

• There are R meetups in many major cities (search “R user group” or similar).
• If you are a gender minority, check out R-ladies meetups. There is also an R-ladies community Slack

References

Baumer, Benjamin S, Daniel T Kaplan, and Nicholas J Horton. 2017. Modern Data Science with R. CRC Press. https://mdsr-book.github.io/.

Chang, Winston. 2013. R Cookbook. O’Reilly. www.cookbook-r.com/.

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. https://moderndive.com/index.html.

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. https://leanpub.com/datastyle.

Peng, Roger. 2018. The Art of Data Science. https://leanpub.com/artofdatascience.

Peng, Roger. 2020. R Programming for Data Science. https://leanpub.com/rprogramming.

Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science. O’Reilly. https://r4ds.had.co.nz.