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his section presumes the reader has some familiarity with statistical methodology, in particular with regression analysis and the analysis of variance. Later we make some rather more ambitious presumptions, namely that something is known about generalized linear models and nonlinear regression. The requirements for fitting statistical models are sufficiently well defined to make it possible to construct general tools that apply in a broad spectrum of problems. R provides an interlocking suite of facilities that make fitting statistical models very simple. As we mention in the introduction, the basic output is minimal, and one needs to ask for the details by calling extractor functions.

When you use the R program it issues a prompt when it expects input commands. The default prompt is ‘>’, which on UNIX might be the same as the shell prompt, and so it may appear that nothing is happening. However, as we shall see, it is easy to change to a different R prompt if you wish. We will assume that the UNIX shell prompt is ‘$’.

R can be regarded as an implementation of the S language which was developed at Bell Laboratories by Rick Becker, John Chambers and Allan Wilks, and also forms the basis of the S-Plus systems. The evolution of the S language is characterized by four books by John Chambers and coauthors. For R, the basic reference is The New S Language: A Programming Environment for Data Analysis and Graphics by Richard A. Becker, John M. Chambers and Allan R. Wilks. The new features of the 1991 release of S are covered in Statistical Models in S edited by John M. Chambers and Trevor J. Hastie. The formal methods and classes of the methods package are based on those described in Programming with Data by John M. Chambers. See References, for precise references. There are now a number of books which describe how to use R for data analysis and statistics, and documentation for S/S-Plus can typically be used with R, keeping the differences between the S implementations in mind. See What documentation exists for R?.

At this point, the future of predictive analytics is pointing directly to R, something we are calling the "perfect storm of predictive analytics."

It's not surprising that Oracle has moved to support R, which "has become a real ubiquitous force in advanced analytics," said Forrester Research analyst James Kobielus. "It's everywhere. Enterprise adoption of it has been growing steadily. When we ask our customers what they're using for statistical modeling they'll say SAS or [IBM's] SPSS, but they increasingly say R in the same breath."

adoption

It's not surprising that Oracle has moved to support R, which "has become a real ubiquitous force in advanced analytics," said Forrester Research analyst James Kobielus. "It's everywhere. Enterprise adoption of it has been growing steadily. When we ask our customers what they're using for statistical modeling they'll say SAS or [IBM's] SPSS, but they increasingly say R in the same breath."

It's not surprising that Oracle has moved to support R, which "has become a real ubiquitous force in advanced analytics," said Forrester Research analyst James Kobielus. "It's everywhere. Enterprise adoption of it has been growing steadily. When we ask our customers what they're using for statistical modeling they'll say SAS or [IBM's] SPSS, but they increasingly say R in the same breath."

It's available as an Oracle 11g database option and costs $23,000 per processor license.

multiple linear regression (including diagnostics, cross-validation and variable selection), analysis of variance (including ANCOVA and MANOVA), and statistics based on resampling.

these bloggers wrote around 3200 posts about R

 list of the top visited posts 

programming environment

contribute their R article

(unofficial) online journal of the R statistical

.  Bellow you can see a few figures comparing the statistics of 2012

Reproducible R blogging – make it to blog from R to WordPress and blogger (via knitr, RStudio, etc.)

Creating online interactive visualization using R

More guest posts on R-bloggers

There are also around 5000 extension packages for R dedicated to performing specialised tasks.

R is also used by Google for data analysis.

R provides a wide variety of statistical

and graphical techniques,

R is an integrated suite

of software facilities for data manipulation

storage facility,

operators for calculations on arrays,

collection of intermediate tools for data analysis,

graphical facilities

simple and effective programming language

is designed around a true computer languag

R, like S

allows users to add additional functionalit

defining new functions

For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time

R can be found on http://cran.r-project.org, the master site of comprehensive R archive network, or at one of its mirrors.

with an FAQ list, and an introduction to the language and how to use R for doing statistical analysis and graphics in PDF and other formats.

R is more than a programming language. It is an interactive environment for doing statistics.

This document is a work in progress. Corrections and comments are welcome.

5 Arrays and matrices

10 Writing your own functions

11 Statistical models in R

R is an integrated suite of software facilities for data manipulation, calculation and graphical display.

Manage variables in active data set > Convert

numeric

Statistics > Summaries > Numerical summaries