User:Timothee Flutre/Notebook/Postdoc/2011/11/07

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** official introductory [http://cran.r-project.org/doc/manuals/R-intro.html manual]
** official introductory [http://cran.r-project.org/doc/manuals/R-intro.html manual]
** well-organized [http://www.statmethods.net/ how-to]
** well-organized [http://www.statmethods.net/ how-to]
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** condensed [http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf reference card]
** freely-available [http://adv-r.had.co.nz/ book] for advanced usage
** freely-available [http://adv-r.had.co.nz/ book] for advanced usage
** [http://www.r-bloggers.com/ aggregator] of R blogs
** [http://www.r-bloggers.com/ aggregator] of R blogs

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About R

  • Motivation: when analyzing data for any research project, it's essential to be able to quickly clean the raw data, transform them, plot intermediary results, calculate summary statistics, try various more-or-less sophisticated models, etc. This must be easily doable with small as well as large data sets, interactively or not. Several tools exist to fill exactly this need, and R is only one of them, but I especially recommend it because it is build by statisticians (this means that the implemented models are numerous and state-of-the-art). Moreover, it's open-source (and even free software), platform-independent, full of packages, with well-documented resources, etc, so give it a try!



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