How to get started with R

I often get asked how to get started with learning R if there is not currently a class offered. Here is what I recommend:

1. Start with a free online Code School tutorial

First of all, check out this (free) online course: https://www.codeschool.com/courses/try-r
No need to install anything, no need to pay. Students in my bioinformatics class liked this online Code School course a lot. It will not make you a master of R, but it’s a nice starting point.

2. Install R, Rstudio and swirl on your computer

Next, it is time to install R and Rstudio on your computer. Once you have that, install the swirl package. Instructions for installing R, Rstudio and swirl can be found here: http://swirlstats.com/students.html
swirl is an R package that helps you learn R while you are in the Rstudio environment. I highly recommend using the Rstudio environment! The swirl tutorials teach you the basics of vectors, matrices, logical expressions, base graphics, apply functions and many other topics. Kind words included (“Almost! Try again. Or, type info() for more options.”)

3. Dive in with great Udacity class …

If you are ready to really dive in (and have some time to invest), try out this great Udacity class: https://www.udacity.com/course/data-analysis-with-r–ud651 (no need to pay for it, you can do the free version). This class is taught by people from the Facebook data science team. They do a great job guiding you through a lot of R coding. Importantly, they always take the time to explain why you’d want to do something before they let you do it. A large part of the course is focused on using the ggplot2 package.

… or start reading The R Book

The R Book is a book by biologist and R hero Michael Crawley. The pdf of the book is available from many websites (for example: ftp://ftp.tuebingen.mpg.de/pub/kyb/bresciani/Crawley%20-%20The%20R%20Book.pdf). Make sure you also download the example data that come with the book (http://www.bio.ic.ac.uk/research/mjcraw/therbook/).

The R Book is a great resource and very clearly written. The students in my lab enjoy reading from it and trying out the code. If you are a biologist, it’ll be fun to work with the biology examples in the R book.

4. Find others who are using R or learning R.

Learning R is hard. You will get frustrated sometimes. If you know someone who is learning with you or who could help you when you are stuck, things will be easier! If there is no one near you, try to find R minded people on Twitter or elsewhere online. Also, check out the R forum on Stack Overflow (http://stackoverflow.com/questions/tagged/r) for many questions and answers on R.

Good luck!