I’m totally new to R!
Welcome to the carnival 🎪. R is a great choice of programming language to learn for anyone interested in econometrics or data science more broadly. It’s extremely popular, has high-quality tooling, and an incredibly inclusive community. Plus, it’s free!
As mentioned in the home page, R for Data Science is a great introductory text, and you should read it throughout the semester to gain new R skills. For the purposes of getting started with this website, I suggest at least reading the Introductory chapters as well as the “Workflow: basics” and “Workflow: scripts” chapters before returning to this page.
Installation
This package is not hosted on CRAN, so you can’t install it with the install.packages command. You need to use devtools to install it from my GitHub page:
library(devtools)
install_github("nateybear/metrics-in-r")You should see a bunch of other things installing too. This is the point: by installing my list of curated packages you should be “set up” and ready to start doing your homeworks in R.
Resources
There are many great resources for learning R and specific aspects of R. Here is a non-exhaustive list that I will update occasionally:
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R for Data Science by Hadley Wickham and Garrett Grolemund
- Mentioned above
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Introduction to Econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer
- This book mirrors Jeffery Wooldridge’s Introduction to Econometrics and uses R to demonstrate how to do each of the examples. Extremely helpful if you are using Wooldridge for your class.
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Advanced R by Hadley Wickham
- What it says on the cover. If you already have some experience with R or programming in general, this is a deeper diver to learn how the language works under the hood.