Resources for R

Advice on trouble shooting

• You will learn a lot more if you Google your problems. Please try Google before emailing anyone.
• Use StackOverflow! You don’t have to directly search StackOverflow, but don’t be intimidated. Some of the threads sound (or are) really complicated, but I often find that the answer is out there if I’m willing to slow down and read.
• R documentation can be confusing, but it also very helpful—particularly if you can find packages’ vignettes or GitHub README files.

Guides for R

R is a very popular language, and there are lots of introductory (or advanced) guides to R floating around the internet. Here are a few.

• RStudio’s list of resources
• RStudio tutorial
• CRAN’s A (very) short introduction to R
• Dan Nguyen’s shortlist of R resources
• CRAN’s introduction to R
• OnePageR
• The R Guide
• Swirl: Learn R in R
• Hadley Wickham’s Advanced R
• The Pirate’s Guide to R
• A Newcomer’s (Angry) Guide to R
• A guide to the Hadley-verse
• The Undergraduate Guide to R
• Econometrics in R (old)
• ROpenSci’s Guidelines to Writing Code

Cheat sheets

• RStudio, Data wrangling
• RStudio, Data visualization
• RStudio, IDE
• RStudio, many cheatsheets

Style guides

Style matters. It helps with collaboration. It helps with getting better feedback from me. And it helps your future self judge your current self less harshly when you return to old R scripts.

• Hadley Wickham’s style guide
• Google’s R style guide
• Hands on Data Science
• R Style. An Rchaeological Commentary
• The State of Naming Conventions in R

General advice on code, data, and research

Here are some other resources on research, writing, presenting, and other activities relevant to (academic) research.

• Gentzkow and Shapiro on code and data
• A lot of links from Charles Martineau
• More advice I’ve archived (lots of stuff from Chris Blattman)