**What is R (Language)**

R is an open-source (GPL) programming language for statistical computing and graphics, made after S and S-plus language. The S language was developed by AT & T laboratories in the late ’80s. Robert Gentleman and Ross Ihaka started the research project of the statistics department of the University of Auckland in 1995 and called R Language.

The R language is currently maintained by the R core-development team (an international team of volunteer developers). The (R Project website) is the main site for information about R. From this page information about obtaining the software, accompanying package and many other sources of documentation (help files) can be obtained.

R provides a wide variety of statistical and graphical techniques such as linear and non-linear modeling, classical statistical tests, time-series analysis, classification, multivariate analysis, etc., as it is an integrated suite of software having facilities for data manipulation, calculation and graphics display. It includes

- Effective data handling and storage facilities
- Have a suite of operators for calculation on arrays, particularly for matrices
- Have a large, coherent, integrated collection of intermediate tools for data analysis
- Graphical data analysis
- Conditions, loops, user-defined recursive functions, and input-output facilities.

**Obtaining R Software**

R program can be obtained/downloaded from the R Project site the ready-to-run (binaries) files for several operating systems such as Windows, Mac OS X, Linux, Solaris, etc. The source code for R is also available for download and can be compiled for other platforms. R language simplifies many statistical computations as R is a very powerful statistical language having many statistical routines (programming code) developed by people from all over the world and are freely available from the R project website as “Packages”. The basic installation of R language contains many powerful sets of tools and it includes some basic packages required for data handling and data analysis.

Many users of R think of R as a statistical system, but it is an environment within which statistical techniques are implemented. R can also be extended via packages.

**Installing R**

For windows, the operating system binary version is available from http://cran.r- project.org/bin/windows/base/. “R-3.0.0-win.exe. R-3.0.0” is the latest version of R released on 03-April-2013, by Duncan Murdoch.

After downloading the binary file double-click it, almost automatic installation of the R system will start although the customized installation option is also available. Follow the instruction during the installation procedure. Once the installation process is complete, you have the R icon on your computer desktop.

**The R Console**

When R starts, you will see R console windows, where you type some commands to get the required results. Note that commands are typed on the R Console command prompt. You can also edit the commands previously typed on the command prompt by using left, right, up, down arrow keys, home, end, backspace, insert and delete keys from the keyboard. Command history can be got by up and down arrow keys to scroll through recent commands. It is also possible to type commands in a file and then execute the file using the source function in the R console.

**Books**

Following books can be useful for learning the R and S language.

- “Psychologie statistique avec R” by Yvonnick Noel. Partique R. Springer, 2013.
- “Instant R: An introduction to R for Statistical Analysis” by Sarah Stowell. Jotunheim Publishing, 2012.
- “Financial Risk Modeling and Portfolio Optimization with R” by Bernhard Pfaff. Wiley, Chichester, Uk, 2012.
- “An R Companion to Applied Regression” by John Fox and Sanford Weisberg, Sage Publications, Thousand Oaks, CA, USA, 2nd Edition, 2011,
- “R Graphs Cookbook” by Hrishi Mittal, Packt Publishing, 2011
- “R in Action” by Rob Kabacoff. Manning, 2010.
- “The statistical analysis with R Beginners Guide” by John M. Quick. Packt Publishing, 2010.
- “Introducing Monte Carlo Methods with R” by Christian Robert and George Casella. Use R. Springer, 2010.
- “R for SAS and SPSS users” by Robert A. Muenchen. Springer Series in Statistics and Computing. Springer, 2009.