What is R?
R is a language and environment for statistical computing and graphics. 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 can be considered as a different implementation of S. There are some significant differences, but much code written for S runs unaltered under R.
R provides a broad diversity of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical technologies, and is very extensible. 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.
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. Good care has been taken over the defaults for the minor design choices in graphics, but the user retains utter control.
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a broad multitude of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.
The R environment
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
- an effective data treating and storage facility,
- a suite of operators for calculations on arrays, in particular matrices,
- a large, coherent, integrated collection of intermediate instruments for data analysis,
- graphical facilities for data analysis and display either on-screen or on hardcopy, and
- a well-developed, plain and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible instruments, as is frequently the case with other data analysis software.
R, like S, is designed around a true computer language, and it permits users to add extra functionality by defining fresh functions. Much of the system is itself written in the R dialect of S, which makes it effortless for users to go after the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
Many users think of R as a statistics system. We choose to think of it of an environment within which statistical mechanisms are implemented. R can be extended (lightly) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very broad range of modern statistics.
R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.