Additional Resources

Here is a Dropbox folder with a few books for learning R (don’t ask where I got them). I’ve used some of these, others were recommended to me but I never got around to reading them. Please not that I use all of the books in this folder, meaning that some of them will likely be more advanced than what you’re looking for. Feel free to download anything and hold onto them for when your methods skills are a little more advanced (the Scott Long book is particularly useful for MLE, as is Gill 2014, but those are more about statistics than coding). I’ll highlight a few specific ones below and link other ones that are not in that folder as well.

R for Data Science

This book is fantastic. I used the first edition of it (which can be found here), but either way, the book is fantastic. Hadley Wickham made the tidyverse package, and this book is basically a go-to source when trying to learn how to use tidyverse. The book covers not only ways to write code, but it also covers important things that we don’t fully have time to cover (things like a proper workflow with scripts and file structures, using Rprojects, etc.). And, the book is $Free.99 which is a really nice bonus (though they do have a paper version if you’re interested).

Tidyverse Style Guide

This website is a style guide for using tidyverse. Having clean code is really important for both reproducability and your own sanity. You don’t have to follow everything that is in here, but it is a great starting point for learning how to manage writing your code.

Graphical Data Analysis with R

I have little experience with this book, but Matt Pietryka highly recommends it. This book is by Anthony Unwin, it includes code examples, and helps with learning ggplot and base R plots. Not just learning functionally how to use ggplot, but how to effectively communicate information through graphs. I do have a paper copy of it if anyone needs to borrow it!

Statistical Rethinking

This one is too advanced now, but I really enjoyed it. It is a book about Bayesian statistics, and while I personally found Gill (2014) more engaging and easier to understand, Statistical Rethinking is still very well written and includes code examples on how to implement models that you can follow along with. Plus, you can go here for the tidyverse version of the book, again for $Free.99.