Statistics with R. Warning Here are the notes I took while discovering and using the statistical environment R.
However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document. 1. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License. R Programming - Manuals. R Basics The R & BioConductor manual provides a general introduction to the usage of the R environment and its basic command syntax.
Code Editors for R Several excellent code editors are available that provide functionalities like R syntax highlighting, auto code indenting and utilities to send code/functions to the R console. Programming in R using Vim or Emacs Programming in R using RStudio Integrating R with Vim and Tmux Users interested in integrating R with vim and tmux may want to consult the Vim-R-Tmux configuration page. Finding Help Reference list on R programming (selection)R Programming for Bioinformatics, by Robert GentlemanAdvanced R, by Hadley WickhamS Programming, by W. Control Structures Conditional Executions Comparison Operators equal: ==not equal: ! R news & tutorials from the web.
"R" you ready? R - Books. Introducing R. The purpose of these notes, an update of my 1992 handout Introducing S-Plus, is to provide a quick introduction to R, particularly as a tool for fitting linear and generalized linear models.
A printer-friendly PDF version is available here. 1 Introduction R is a powerful environment for statistical computing which runs on several platforms. These notes are written specially for users running the Windows version, but most of the material applies to the Mac and Linux versions as well. 1.1 The R Language and Environment R was first written as a research project by Ross Ihaka and Robert Gentleman, and is now under active development by a group of statisticians called 'the R core team', with a home page at www.r-project.org.
R was designed to be 'not unlike' the S language developed by John Chambers and others at Bell Labs. R is available free of charge and is distributed under the terms of the Free Software Foundation's GNU General Public License. 1.2 Bibliographic Remarks. Introduction to R. FeaturesInstalling RR: DocumentationGraphical interface -- R for non-statisticians and non-programmersR: Some elementary functions In this part, we give a bird's eye view of the software: what is its position with respect to other software for numeric computations?
What are its advantages, its drawbacks? What can we do with it? What are its limitations? What is its syntax? Features Statistics vs Signal processing It is a statistical software: contrary to other numerical computation software (Scilab, Octave), it already provides functions to perform non trivial statistical operations, be they classic (regression, logistic regression, analysis of variance (anova), decision trees, principal component analysis, etc.) or more modern (neural networks, bootstrap, generalized additive models (GAM), mixed models, etc.). GUI -- or lack thereof Speed issues or you can program everything yourself (in C or C++), with the help of a few libraries Memory issues Graphics Freedom Examples.
One R Tip A Day. Learning R.