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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 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.

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

R is also used by Google for data analysis.

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?.

Open-source software is free for anyone to use and modify

an improve

R is similar to other programming languages

R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia

For statisticians

R is particularly useful because it contains a number of built-in mechanisms for organizing data, running calculations on the information and creating graphical representations of data sets.

statisticians, engineers and scientists can improve the software’s code or write variations for specific tasks

software’s

Packages written for R add advanced algorithms, colored and textured graphs and mining techniques to dig deeper into databases

While it is difficult to calculate exactly how many people use R, those most familiar with the software estimate that close to 250,000 people work with it regularly

“R is a real demonstration of the power of collaboration, and I don’t think you could construct something like this any other way,” Mr. Ihaka said. “We could have chosen to be commercial, and we would have sold five copies of the software.”

Conditionnals may be used inside other constructions.

While loop

Actually, R is a programming language

For loop

Switch

Repeat loop

R belongs to the family of functionnal languages

R is a full-fledged programming language with a radically different approach to processing large and complex datasets

R also offers benefits to companies trying to reduce their software expenditures with enterprise software vendors such as SAS and SPSS.

R community develops and releases new software continuously

R is especially useful for quickly and easily generating charts and graphics

R allows

to create high-quality charts and graphs

R allowed them to effectively communicate their analytic results in ways that were easily understood by non-technical audiences

text files (r/w)

Great resource for R beginners! Thanks!