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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?.

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.

> 2 + 3 * 5      # Note the order of operations. > log (10)       # Natural logarithm with base e=2.718282 > 4^2            # 4 raised to the second power > 3/2            # Division > sqrt (16)      # Square root > abs (3-7)      # Absolute value of 3-7 > pi             # The mysterious number > exp(2)         # exponential function > 15 %/% 4       # This is the integer divide operation > # This is a comment line

x<- log(2.843432) *pi

R is a software package especially suitable for data analysis and graphical representation. Functions and results of analysis are all stored as objects, allowing easy function modification and model building. R provides the language, tool, and environment in one convenient package. It is very flexible and highly customizable. Excellent graphical tools make R an ideal environment for EDA (Exploratory Data Analysis). Since most high level functions are written in R language itself, you can learn the language by studying the function code.

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

R community develops and releases new software continuously

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

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

It compiles and runs on a wide variety of UNIX

a suite of operators for calculations on arrays, in particular matrices

It includes

an effective data handling and storage facility

R is a language and environment for statistical computing and graphics

a large, coherent, integrated collection of intermediate tools for data analysis

graphical facilities for data analysis and display either on-screen or on hardcopy, and

a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities

text files (r/w)

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.

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,

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

an improve

R is similar to other programming languages

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.

For statisticians

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

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.”

has a graphic IDE that enables faster

Wide Platform Support

Parallel Processing Power

ANALYTICS

GRAPHICS AND VISUALIZATION

R APPLICATIONS and EXTENSIONS***

PROGRAMMING LANGUAGE FEATURES

Too Many Commands

Sloppy Control of Variables

Loop-a-phobia

Functions That Act Like Procedures

Naming and Renaming Variables is Way Too Complicated

Inability to Analyze Multiple Variables

Poor Ability to Select Variable Sets

Too Much Complexity

Lack of Graphical User Interface (GUI)