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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?.

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.”

Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell use it.

R is similar to other programming languages, like C, Java and Perl

. I.B.M., Hewlett-Packard and Dell make billions of dollars a year selling servers that run the open-source Linux operating system

Web sites are displayed using an open-source application called Apache,

the end results of all this technology via the Firefox Web

can help illuminate data trends more clearly than is possible by entering information into rows and columns

What makes R so useful

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

advanced algorithms, colored and textured graphs and mining techniques to dig deeper into databases.

Packages written for R add

the professors considered their coding efforts more of an academic game than anything else.

the notion of devising something like R sprang up during a hallway conversation.

They both wanted technology better suited for their statistics students

R immediately gained a following with people who saw the possibilities in customizing the free software.

Many people view the end results of all this technology via the Firefox Web browser, also open-source software.

R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca partly because data mining has entered a golden age

Companies as diverse as Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell use it.

R has also quickly found a following because statisticians, engineers and scientists without computer programming skills find it easy to use.

Many people view the end results of all this technology via the Firefox Web browser, also open-source software.

Most Web sites are displayed using an open-source application called Apache, and companies increasingly rely on the open-source MySQL database to store their critical information. Many people view the end results of all this technology via the Firefox Web browser, also open-source software.

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.

What makes R so useful

is that statisticians, engineers and scientists can improve the software’s code or write variations for specific tasks. Packages written for R add advanced algorithms, colored and textured graphs and mining techniques to dig deeper into databases.

The financial services community has demonstrated a particular affinity for R; dozens of packages exist for derivatives analysis alone.

Why is R named R?

The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language `S'

As you can see, for organizations who need to create advanced analytics applications, R is integrated throughout the analytics stack: for data access, for presentation of results, and of course for the statistical analysis process itself. This degree of integration by so many companies is indicative of the level of demand for R throughout the enterprise. As the leading provider of commercial software and support for R, Revolution Analytics supports R users througout the organization,

People chimed in with the unique ways they’re using the technology

talked about its passion

R helps people deal with large volumes of data in a wide variety of industries

for tweaking

Speaking of R, Mr. Chambers said, “It’s way beyond anything we could have imagined at Bell Labs.”

Were Pfizer to use R in clinical studies

“It’s very hard to displace the industry standard in those types of cases,” said Max Kuhn, associate director of nonclinical statistics at Pfizer.

Revolution offers ways to speed up the software on certain applications and to run it on large computers

Revolution provides support services to customers like Pfizer and Bank of America

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 similar to other programming languages, like C, Java and Perl,

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

What makes R so useful

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

“The great beauty of R is that you can modify it to do all sorts of things

“And you have a lot of prepackaged stuff that’s already available,

R first appeared in 1996,

The company says R users will be able to process, visualize and model terabyte-class data sets in a fraction of the time of legacy products, without the need for expensive or specialized hardware.

e processing of large d

multi-core processing of lar