The R language (and open-source software) is the de facto standard among statisticians for the development of statistical software, and is widely used for statistical software development and data analysis. R is a modern implementation of S, one of several statistical programming languages designed at Bell Laboratories.
R is much more than a programming language. It’s an interactive environment for performing statistics. R offers a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The ability to download and install R packages is a key factor which makes R an excellent language to learn. Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. The CRAN package repository hosts over 10,000 packages, and Bioconductor hosts nearly 1,300 packages.
Many statisticians use R with the command line. However, the command line can be quite daunting to a beginner of R. Fortunately, there are many different graphical user interfaces available for R which help to flatten the learning curve.
| Graphical User Interfaces for R | |
| RStudio | Integrated development environment for R |
| Tinn-R | GUI for R Language and Environment |
| Rattle | Gnome cross platform GUI for Data Mining using R |
| Deducer | Intuitive, cross-platform graphical data analysis system |
| RKWard | Easy to use, transparent frontend |
| JGR | Universal and unified graphical user interface for R |
| RCommander | Commonly known as Rcmdr, a graphical user interface for R |
| Are you interested in learning the art of programming? There are lots of excellent free and open source programming books that teach you how to program in every popular programming language. Read these Free Books. |
Other resources for R:
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