R Programming · Shopy 101

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R Programming

R Basics
- Introduction as of Sep 11, 2009"}'>
- Sample Session as of Jul 16, 2010"}'>
- Manage your workspace as of July 22 2011"}'>
- Settings and Installation as of May 20, 2011"}'>
- Documentation as of Sep 11, 2009"}'>
- Control Structures as of July 22 2011"}'>
- Working with functions as of July 22 2011"}'>
- Debugging as of August 19 2011"}'>
- Using C or Fortran
- Utilities as of -"}'>
- Estimation utilities as of -"}'>
- Packages as of July 22, 2011"}'>
Data Management
- Data types as of July 22, 2011"}'>
- Working with data frames as of Sep 11, 2009"}'>
- Importing and exporting data as of Sep 11, 2009"}'>
- Text Processing as of April 23, 2010"}'>
- Times and Dates
Graphics as of July 22, 2011"}'>
- Grammar of graphics
Publication quality output as of Sep 11, 2009"}'>
Descriptive Statistics as of July 22 2011"}'>
Mathematics as of Oct 4, 2009"}'>
- Optimization as of Sep 11, 2009"}'>
- Probability Distributions as of July 22 2011"}'>
- Random Number Generation as of Sep 11, 2009"}'>
Statistical Core Methods
- Maximum Likelihood as of Sep 11, 2009"}'>
- Method of Moments
- Bayesian Methods as of Sep 11, 2009"}'>
- Bootstrap as of Sep 11, 2009"}'>
- Multiple Imputation as of Sep 11, 2009"}'>
- Nonparametric Methods as of Sep 11, 2009"}'>
Regression Models
- Linear Models as of Apr 28, 2010"}'>
- Quantile Regression as of Sep 11, 2009"}'>
- Binomial Models as of Sep 17, 2009"}'>
- Multinomial Models as of Sep 11, 2009"}'>
- Tobit And Selection Models as of Oct 4, 2009"}'>
- Count Data Models as of Sep 11, 2009"}'>
- Duration Analysis as of Sep 11, 2009"}'>
Time Series as of July 22 2011"}'>
Factor Analysis as of Sep 17, 2009"}'>
Classification
- Ordination as of Sep 11, 2009"}'>
- Clustering as of Sep 11, 2009"}'>
Network Analysis as of Sep 11, 2009"}'>
High Performance Computing
- Profiling R code as of Jun 27, 2011"}'>
- Parallel computing with R as of Jun 27, 2011"}'>
Appendix
- Sources
- Index as of Oct 25, 2010"}'>

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Welcome to the R programming Wikibook

This book is designed to be a practical guide to the R programming language^[1].

R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)^[2] and many resources in specialized books, forums such as Stackoverflow^[3] and personal blogs^[4], but all of these resources are scattered and therefore difficult to find and to compare. The aim of this Wikibook is to be the place where anyone can share their knowledge and tricks on R. It is supposed to be organized by task but not by discipline^[5]. We try to make a cross-disciplinary book, i.e. a book that can be used by all people applying statistics to some specific fields.

How can you share your R experience ?

Explain the syntax of a command
Compare the different ways of performing each task using R.
Try to make unique examples based on fake data (ie simulated data sets).
As with any Wikibook please feel free to make corrections, expand explanations, and make additions where necessary.

Some rules :

Put the name of functions in the code environment : <code>lm()</code>
Put the name of packages in bold : '''stats'''
Except for the default packages, put the name of the package in parentheses after each function : <code>MCMCprobit()</code> ('''MCMCpack''')
Put examples in the source environment with argument lang="rsplus" : <syntaxhighlight lang="rsplus"></syntaxhighlight>
Use references to package documentations, academic literature and wikipedia.
Use the citations templates to make citations : Template:Cite book, Template:Cite web, Template:Cite journal
If you want to add a graph, you should load it on Commons and add the tag {{Created with R}} and, if it is applicable, [[Category:Images with R source code]].

Prerequisites

We assume that readers have a background in statistics. This book is not a book about statistics but a book about how to implement statistical methods using R. We try to use terms which are already defined on Wikipedia such that people can refer to the corresponding wikipedia page each time they have some doubts on a notion.

We also assume that readers are familiar with computers and that they know how to use software with a command-line interface. There are some graphical user interfaces for R but we are not going to explain how to use them in this textbook. Beginners should have a look at the Sample session for a first session with R. They can also have a look at the Statistical Analysis: an Introduction using R book.

References

↑ R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
↑ CRAN manuals
↑ R on Stackoverflow
↑ See R-Bloggers
↑ The CRAN Task View is already organized by discipline.
↑ Larry Wasserman All of Statistics Springer 2004 http://www.stat.cmu.edu/~larry/all-of-statistics/index.html

Wikipedia has related information at R programming language.

Wikiversity has learning materials about How to use R

See also the Wikibook Data Mining Algorithms In R.

See also the Wikibook Statistical Analysis: an Introduction using R.

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