Introduction To R

1. Introduction to The Dataminingtools.net Team

2. What is R ? R is a programming language and software environment for statistical computing and graphics. It is an implementation of the S programming language with lexical scoping semantics inspired by Scheme. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.

3. History Version 0.16 – This is the last alpha version developed . Much of the basic functionality from the "White Book" was implemented. The mailing lists commenced on April 1, 1997. Version 0.49 – April 23, 1997 – This is the oldest available source release, and compiles on a limited number of Unix-like platforms. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages. Alpha versions of R for Microsoft Windows and Mac OS are made available shortly after this version. Version 0.60 – December 5, 1997 – R becomes an official part of the GNU Project. The code is hosted and maintained on CVS. Version 1.0.0 – February 29, 2000 – Considered by its developers stable enough for production use [11]. Version 1.4.0 – S4 methods are introduced and the first version for Mac OS X is made available soon after. Version 2.0.0 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory. Version 2.1.0 – Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages. Version 2.9.0 – Package 'Matrix' is now a recommended package contained in the basic R distribution. -Source: http://en.wikipedia.org/wiki/R_(programming_language)

4. Features It's free! You don’t have to shell out any money for using R! R is part of the GNU project. Its source code is freely available under the GNU General Public License It is cross-platform and its packages are available for Windows, Mac OS and Linux.

5. Features Unparalleled platform for programming new statistical methods in an easy and straightforward manner. Supports advanced statistical routines hardly available in other packages. Amazing graphics capabilities.

6. Features Supported Statistical features: linear modeling nonlinear modeling classical statistical tests time-series analysis Classification clustering

7. Features R produces publication-quality graphs which can include mathematical symbols. R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hard copy.

8. Obtaining R R is available for Linux, Mac OS X, and Windows (95 or later) platforms. The software can be downloaded from one of the Comprehensive R Archive Network (CRAN) mirror sites.