R2-R for Research
This website provides resources, including tutorials and past seminar presentations to help students, researchers and R enthusiasts in general in learning R. R is one of the most popular statistical software used by both Industry and Academia. It is invaluable to research and the goal here is to make showcase its capabilities in research.
R is an open source software package and environment for statistical computing and graphics. The R language has received a lot of attention in the last 5 years or so particularly with its growing use by Statisticians and data miners for their empirical analysis and software development. R started as a freely available implementation of the S programming language. R was created by Ross Ihakaand Robert Gentlemanat the University of Auckland, New Zealand, and now, R is developed by the R Development Core Team. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
The official R website http://www.r-project.org/ , provides the best description and explanation for R.
The growth in the number of R users in recent years has indicated that researchers around the world are either using R or will use it at some point. There are several advantage of using R for statistical computing other than its open source and doesn’t cost anything. The benefits of R for an introductory student and instructors are
R is free.
R is open-source and runs on UNIX, Windows and Macintosh, so it can be used on multiple platforms.
R has an excellent built-in help system and also various online help pages including mailing lists and boards.
R has excellent graphing capabilities which are customizable.
R's language has a powerful, easy to learn syntax with many built-in statistical functions which are provided by default built in packages.
R is a computer programming language. It is easier for programmers to learn it and its intuitive enough for beginners.
R provides specific functions bundled in user created packages for a particular field. This makes it easier for an applied researcher to use these functions rather than reinvent the wheel.