As an interactive language (as opposed to a data-in-data-out black-box procedures), R promotes experimentation and exploration, which improves data analysis and often leads to discoveries that wouldn't be made otherwise. But once you learn the language, there are many benefits. That may sound daunting, but it's an easy language to learn, and a very natural and expressive one for data analysis. In R, you do data analysis by writing functions and scripts, not by pointing and clicking.

Thousands of experts around the world have done just that, and their contributions benefit the millions of people who use R today. But just as importantly, R is open: you can inspect the code and tinker with it as much as you like (provided you respect the terms of the GNU General Public License version 2 under which it is distributed). As an open-source project, you can use R free of charge: no worries about subscription fees, license managers, or user limits. There's lots of software available for data analysis today: spreadsheets like Excel, batch-oriented procedure-based systems like SAS point-and-click GUI-based systems like SPSS data mining systems, and so on. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. ) and graphical techniques, and is highly extensible. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering. There are some important differences, but much code written for S runs unaltered under R. R can be considered as a different implementation of S. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R is a language and environment for statistical computing and graphics.

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