I wanted to create a space for economics research assistants to share advice & ask questions on how to tackle common R issues (based on recent Twitter convo originally raised by Prof. Dina Pomeranz) . Lots of us have been trained in Stata, and now are suddenly jumping into R!
Please share questions or advice below!
R for Data Science is an incredible introductory text regardless of discipline (free here!). Mastering the tools in the book will give you the tools to approach most analytic problems.
Using R for Introductory Econometrics is a companion book to Wooldridge's introductory econometrics book. It doesn't rely on the tidyverse, so it may be a confusing sequel to R4DS, but it is a useful reference for econometric techniques.
CRAN task views are an essential resource for domain-specific R. The CRAN task view for econometrics is thorough.
I work at the Urban Institute. We have our own website for reference and sharing code called R Programming at the Urban Institute. It doesn't contain a lot of information about modeling, but it's useful for our research assistants. We're adding more content all of the time! We'll probably add some modeling information in the future. Requests and pull requests welcome!
Finally, the R community is amazing. #rstats on twitter is a useful resource! So are local user groups!
Thanks for doing this! I look forward to hearing suggestions from others!
My sense is that many people favor R for the visualization and analysis of spatial data. Geocomputation with R is a great place to start. It begins with an introduction to the most common spatial data types, covers operations in the burgeoning sf package, and even extends to modeling and cross validation in mlr.
If you're looking to do econometrics in R, Introduction to Econometrics with R is a great tool, especially if you're coming from a Stata background.
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