A Quick Guide to Teaching R Programming to Computational Biology Students

Abstract

The name R refers to the computa- tional environment initially created by Robert Gentleman and Robert Ihaka, similar in nature to the S statistical environment developed at Bell Laborato- ries (http://www.r-project.org/about. html) 1. It has since been developed and maintained by a strong team of core developers (R-core), who are renowned researchers in computational disciplines. R has gained wide acceptance as a reliable and powerful modern computational en- vironment for statistical computing and visualisation, and is now used in many areas of scientific computation. R is free software, released under the GNU Gen- eral Public License; this means anyone can see all its source code, and there are no restrictive, costly licensing arrangements. One of the main reasons that computa- tional biologists use R is the Bioconductor project which is a set of packages for R to analyse genomic data. These packages have, in many cases, been provided by researchers to complement descriptions of algorithms in journal articles. Many computational biologists regard R and Bioconductor as fundamental tools for their research. R is a modern, functional programming lan- guage that allows for rapid development of ideas, together with object-oriented features for rigorous software develop- ment. The rich set of inbuilt functions makes it ideal for high-volume analysis or statistical simulations, and the packaging system means that code provided by others can easily be shared. Finally, it generates high-quality graphical output so that all stages of a study, from modelling/analysis to publication, can be undertaken within R. For detailed discussion of the merits of R in computational biology, see 2. How to Teach R to Students This brief article is an introduction to teaching R, based on my experience in teaching computational biology graduate students. R is a powerful environment for teaching many aspects of computational biology, including functional genomics, computational neuroscience, dynamical sys- tems, statistical genetics, and network biol- ogy. I provide resources and suggestions for teachingRand describe common difficulties faced by students when learning R.

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