Extra learning resources

R learning resources

These are websites and books that we’ve found to be the most helpful for learning R.

• R for Data Science A freely available book that focuses on the “tidyverse” family of packages developed by Hadley Wickham and others
• Computer World article on R resources with many more helpful links
• RStudio’s cheatsheet page PDF documents that list the most useful functions from a lot of different R packages — they look great pinned to your office bulletin board!
• R Weekly A newsletter that comes out once a week. It is a curated list of links to articles showing examples of how people are using R, new package releases, and blog posts with R tutorials for beginner and advanced users. Highly recommended!
• Introduction to Statistical Learning in R A freely available book that introduces data science techniques in a very accessible way with R examples

Online Communities and Forums

If you are struggling learning R, you are not alone! We have all been there. These are three of the best resources for you to ask for help online.

• StackOverflow, questions tagged R These are the questions asked by users on the site StackOverflow that are related to the R language. If you search the site for past questions, it is very likely that someone has already had the same problem as you, answered by one of the experienced R coders in the SO community.
• RStudio community forum A site where users can ask questions both about RStudio and R code, and experienced users can help them solve their problems.
• R for Data Science learning community Originally created to help beginners work through the R for Data Science book, this community now hosts an active Slack workspace for all kinds of R-related questions.

Comparison of R and Python

To get an idea of the features of both R and Python, it might help to look at a side-by-side comparison of the two languages. Be warned — there are a lot of “R Versus Python” blog posts where the author presents a heavily biased viewpoint and criticizes one or the other of the languages. These two posts provide a more balanced view, emphasizing that there is no one “best” language.

• RStudio blog post on R and Python
• Towardsdatascience post on R and Python

If you need to catch-up before a section of code will work, just squish it's 🍅 to copy code above it into your clipboard. Then paste into your interpreter's console, run, and you'll be ready to start in on that section. Code copied by both 🍅 and 📋 will also appear below, where you can edit first, and then copy, paste, and run again.

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