A great article in the recent PLoS Computational Biology – freely accessible to all! Additionally, check out: OpenWetWare’s topic on “R”.
A Quick Guide to Teaching R Programming to Computational Biology Students
by Stephen J. Eglen*, Cambridge Computational Biology Institute, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000482
The name “R” refers to the computational environment initially created by Robert Gentleman and 1 Robert Ihaka, similar in nature to the “S” statistical environment developed at Bell Laboratories (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 environment for statistical computing and visualisation, and is now used in many areas of scientific computation. R is free software, released under the GNU General 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 computational biologists use R is the Bioconductor project (http://www.bioconductor.org), 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 language that allows for rapid development of ideas, together with object-oriented features for rigorous software development. 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].
