Good resources to learn R

Good resources to learn R

Since it’s the summer vacations, why not take some time to learn R. There are numerous free resources online to dive into this powerful language. For whomever wants to learn it, the challenge more related to finding the time rather than finding resources.

Coursera is an inevitable for online learning. There are a few good video courses offered for R beginners that are more or less oriented toward genomics : (Bioconductor is a life science packages repository).

You’ll found several free books about R starting with the manuals from CRAN (“An Introduction to R” and “R Data Import/Export” for example).
Roger D. Peng who teaches the first two Coursera’s courses mentioned above has written books for the students of his classes which can be downloaded for free (rprogramming, external data). For basic help and recipes for graph, I often consult the R-Cookbook.

R is an open-source version of S. Any documentation related to S or S-PLUS (which is the commercial implementation of S) can help you with the core functions in R. Hence, even if these references ( an introduction and a statistical manual) were written for S-PLUS, the content applies to R too.

Interactive coding
For those who prefer a more active approach, you can learn the basics at your own rhythm with Code School’s course. The concept is simple, read the explanations and the theory and practice the new learned concepts right away in the terminal displayed in your web browser.

The community
I’ve probably mentioned it before, the R community is really active. You will easily find interesting blogs posts on various R-related subjects. One of the oldest blog writting about R that I know is the R-Bloggers.

Lastly, you’ll find a number of answers to common problems on StackOverflow (where you can post your own question as well) and you’ll find numerous code examples on the web. Modifying existing code is a good way to learn too! So do not hesitate to “Google” your questions!

By | 2016-11-08T09:30:04+00:00 July 11, 2016|Categories: Bioinformatics, R|0 Comments

About the Author:

I’ve started in biochemistry but it is as a bioinformatician that I’ve been having fun for several years now : whether doing data analysis and visualization in R, building interactive web interfaces in javascript or exploring machine learning in python.

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