Introduction to R

What is R?

R is a collection of packages containing pre-written statistics and graphics routines for computing and visual analysis of data. This open source sytem allows experts from various disciplines to contribute to this collection via packages and therefore making it one of the most widely used tool for statistical modelling for various applications.

The primary strength of R lies in the fact that it also provides an effective programming environment, and is highly extensible. That is, in addition to using the pre-written packages one can also develop codes for their own algorithms. Another advantage is that it produces high quality well designed plots for print materials. While default graphis settings are simple and effective, users have full control for making more versatile pictures if they desire.

So what are the disvantages? If you are new to this programming language, it is not going to be an easy language to learn on the fly. However, the good news is that one need not wait to master the language before starting to use it. In fact the effective way to learn this system is to learn while using it, or vice versa .

Why R?

We prefer to use R in this website to provide examples and alogorithms to support the statistical methods described here.

• As mentioned earlier, R is a also a programming language, so we are not limited by the ready-made packages. It is relatively easy to develop codes for new methods in R.

• Learn wile you use. This is true not only for learning R but also for exploring the data (datamining). With R one can split a complex procedure into various mid-level steps. This allows the user to learn about the data and make changes in the alogrithm on the basis of what he/she sees it.

• R is based on S from which the commercial package S-plus is derived. R is open-source software and may be freely redistributed. This makes it easy for the application developer to provide the statistical tools as a package to the clients. R is also available for other platforms like Linux, Macintosh, Windows and other UNIX versions and can be obtained from the R-project at www.r-project.org.

• SAS is the most common statistics package in general but R or S is most popular with researchers who use statistical tools in their research. A look at common Statistical journals confirms this popularity. R is also popular for quantitative applications in Finance, Econometrics, Business Intellience and image processing.