Chance is that sooner or later, you're going to use R - one of the (if not the) most popular statistical programming languages in the world. It's not only that R is free which makes it so good, but because thousands of people across the world use it, document it, and develop it further.
Let us warn you: the R interface may be scary at first sight, especially if you're used to using Excel to do your data magic. But - trust us - investing in learning R will pay off greatly. Not only for your career at grad school, but also for your career in industry. R (next to Python) has been a de-facto standard when applying for jobs in marketing analytics or data science.
One thing that's great about R is that there are tons of resources available to learn it. Well - that brings the difficulty that you need to know which resources to use to learn it most efficiently.
Learn R fast
Here are a few tips to learn R efficiently.
Have a project!
"Huh - why to have a project? I first need to learn R!"
Well, if you really want to learn R, then you should already have a project in mind that you would like to tackle. R is such a powerful tool that you would get entirely lost if you didn't actually know what you would like to accomplish. If you don't have a project, and still would like to learn R, we suggest you to do some googling for some interesting datasets to get you started (e.g., Kaggle.com).
Follow our R/R Studio installation guide
- Enroll R courses at Datacamp.com (free-to-use with a Tilburg University account!)
- Must do's for novices
- Must do's if you have to manage data and prepare your own datasets
- Learn data.table, our preferred tool to wrangle with large data. Just search for data.table at datacamp.com. These are our favorites courses: Data manipulation and Joining data.
- Complementary to data.table, you should dive into Tidyverse, a collection of tools that will make your life much easier. This is our top-pick: Introduction to Tidyverse
- Learning your method
- As soon as your data is prepped, you can start analyzing. Of course, your method is informed by your specific research question. For most students, a refresher in regression analysis (e.g., OLS, Logit) may be exactly what they need.