User:Timothee Flutre/Notebook/Postdoc/2011/11/07: Difference between revisions

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** [http://menugget.blogspot.de/2013/01/my-template-for-controlling-publication.html procedure] for publication-quality plots
** [http://menugget.blogspot.de/2013/01/my-template-for-controlling-publication.html procedure] for publication-quality plots
** [https://sites.google.com/site/rosselldavid/home/tips tutorial] to debug within Emacs some C/C++ code called by R
** [https://sites.google.com/site/rosselldavid/home/tips tutorial] to debug within Emacs some C/C++ code called by R
** make your own R package: [https://github.com/jtleek/rpackages policy] from Jeff Leek, [https://github.com/hadley/devtools devtools] from Hadley Wickham, [http://projecttemplate.net/ ProjectTemplate]
** make your own R package: [https://github.com/jtleek/rpackages policy] from Jeff Leek, [http://r-pkgs.had.co.nz/ book] from Hadley Wickham, [http://projecttemplate.net/ ProjectTemplate]
** R [http://google-styleguide.googlecode.com/svn/trunk/Rguide.xml style guide] from Google
** R [http://google-styleguide.googlecode.com/svn/trunk/Rguide.xml style guide] from Google
** [http://cran.r-project.org/bin/linux/ubuntu/ upgrade] R on Ubuntu




Revision as of 15:01, 1 July 2015

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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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