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?.

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 ‘$’.

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.

R is an object oriented programming language. This means that virtually everything can be stored as an R object. Each object has a class. This class describes what the object contains and what each function does with it. For instance, plot(x) produces different outputs depending on whether x is a regression object or a vecto

The assignment symbol is "<-". Alternatively, the classical "=" symbol can be used.

Arguments are passed to functions inside round brackets (parentheses). One can easily combine functions. For instance you can directly type

The symbol "#" comments to the end of the line:

Commands are normally separated by a newline. If you want to put more than one statement on a line, you can use the ";" delimiter.

You can also have one statement on multiple lines. R is case sensitive: a and A are two different objects. Traditionally underscores "_" are not used in names. It is often better to use dots ".". One should avoid using an underscore as the first character of an object name.

Here are some things editors do to keep this book internally consistent. If you have something to contribute, go ahead and make your contribution. Other editors can touch up your edits afterwards so that they conform to the guidelines.

The local manual of style WB:LMOS for the R programming book, including a brief explanation of why we do it that way, is: Examples use "source" tags : <source lang="rsplus"> a <- 1:10 ; mean(a) </source>. That makes them look pretty to our readers. The name of packages are in bold  : '''Hmisc'''. Name of functions are in "code" tags: <code>lm()</code>. Page titles -- the part after "R Programming/" -- are in sentence case, like "R Programming/Working with data frames". We couldn't decide between sentence case and title case, so I flipped a coin. Every page has <noinclude>{{R Programming/Navigation}}</noinclude> at the top and {{R Programming/Navbar|Mathematics|Probability Distributions}} at the bottom. That makes it easier to navigate from one page to another online.

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.