Learning R isn't easy, but it can be incredibly rewarding. The best way to get started with R is to force yourself to use it. While you may feel most comfortable doing a short introduction at first - finding a small project, like exploring a small data set, to try on your own as you learn to use these resources is a great way to build your confidence. Working on a real problem that is relevant to you helps make what you learn about R stick.
R has a rich community, and there are plenty of free and paid ways to learn it. Here are our top recommendations for learning R, by type. Find what works for you and your learning style:
- R for Data Science is available free online, or you can buy a hard copy. This is an excellent place to get started.
- DataCamp offers a free Introduction to R (est. time, 4 hours). They have plenty of follow-up courses as well, some of which are free. DataCamp also offers short courses on individual topics, allowing you to fill in the holes you need.
- Coursera offers a 4-week course on R Programming as part of its data science track.
- For organizations that want help with R tailored to their team's strengths and needs, Project Evident offers R trainings on-site or remotely, where we can work with you to identify topics, aim at the right technical level, and incorporate your own data into demonstrations and exercises. Contact Project Evident.
- Swirl is a clever way to learn R, in R, on your computer. A bit of practice with Swirl is excellent for the mechanics of working in R, but can be a bit lacking in understanding the deeper concepts, so we recommend using it along with another method. Swirl pairs especially well with the R for Data Science book recommended above.
- Find a local R user group. Most big cities have R user groups, organizing talks and bringing in speakers. It's great way to network in your area and get inspired about using R.
Programming isn't easy, and R is no exception. However, one of its great advantages is that the cost of small failures is zero. Try things out, modify code, and run it to see what happens. It's nearly impossible to mess things up in a way that restarting R doesn't fix. So don't be shy about experimenting. When you get stuck — and you will get stuck, experienced R users still spend most of their time trying to get things to work — check out our companion article Getting Help with an R Problem.