Resources for Learning R

1. Download R. This is the first step - getting the program!

2. Download R Studio. RStudio makes R much more user-friendly

3. ComputerWorld's PDF with helpful tips on how to get started, as well as some context about why R is the program of choice for so many.

4. Datacamp's free introduction to R. This is an interactive Tutorial to develop your basic R usage skills. You'll need to create an account.

5. Code School's Try R. This is another introductory tutorial that covers the material in a different way.

6. Once you've learned how to install and load packages, R has a package called "swirl" that runs a tutorial inside of RStudio. This is a great place to start.

7. Rachael's R Tutorials offers sets of tutorials at the Beginner, Intermediate, and Advanced levels.

8. Applied R provides examples and shows exactly the code used to create certain graphs. It covers a range of topics relevant to Social Science statistics.

9. The R Cookbook is a guide for finding out how to do specific tasks in R once you understand the basics of how the software works. It does have some introductory chapters but the interactive tutorials (listed above) are an easier way to learn this content.

10. ANOVA and ANCOVA in R is a tutorial that walks you through running ANOVA in R, and also graphing your output data.

11. Linear models and linear mixed effects models in R with linguistic applications is an in-depth introduction to running linear and mixed effects models in R.

12. Here are some GGplot Codes to help you create and alter graphs using the ggplot package. Lots of detail.

13. This is a Style Guide from Google that will help you maintain clean, legible code. Try to stick to these guidelines.

14. Read this guide for R Best Practices and try to implement them in your coding.

15. R for Data Science! Hadley Wickham has created a lot of awesome packages for R. His book is required reading if you want to really reach your potential.

16. This is a website where you can get cheatsheets for a number of things, including creating markdowns and data wrangling. Highly recommended.

17. Coursera has an Introduction to Probability and Data course set offered by Duke professors. This is a good place to start, and then depending on your skill level, Coursera offers other higher-level options to continue learning. You can audit the course for free--you just won't get a certificate at the end.

18. Slides from Mike Frank's ManyBabies Reproducibility Webinar for writing reproducible manuscripts with Rmarkdown.

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