R for Research
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.
What is R!
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.
Why should we learn 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.