The Basics of R Language
Haven’t heard the name of R language? Well, it is a programming language that is widely in use since a decade for graphics as well as statistical computing. It aids statisticians in data analysis, visualization, and prognostic modeling. Today, the R language is the de facto paradigm for creating statistical programs. Let us now explore this language in more detail.
How Did the R Language Originate?
Technically, this language is an achievement of the S language that was introduced by John Chambers at the Bell Labs. Created by Robert Gentleman and Ross Ihaka at the University of Auckland, the R language is named after the initial letter of the first name of these two authors. Today, the language belongs to the GNU project and is available as an open source code (free). R typically works with a command line interface, but there are many GUIs with which you can use the language.
What are its Statistical Features?
The R Language offers a myriad of statistical and graphical features that are actually the useful techniques for the statisticians. These techniques include classical statistical tests, clustering, classification, time-series analysis, and linear as well as nonlinear modeling. Another feature of the language is its extensibility, which means you can include new functions of your own if you are an expert programmer. In addition, the R language can be combined with C, Fortran, and C++ code during the run time for more intensive tasks. Even vice-versa is possible wherein the users can alter the R objects via the C or Java code.
Currently, much of the code for R is same as that of the S language, but several of its standard functions exist in R itself, which enables the users to make algorithmic choices that one can follow easily. The R language also allows the users to define objects for time-series, regression models, and geo-spatial coordinates. Because of the inheritance from S, the R language features object-oriented programming concepts stronger than any other statistical programming languages. As a result, a 2010 survey has concluded that the R language is the most used data mining tool.
One more appealing feature of the R language is static graphics due to which it can generate publication-quality graphs. In case you need dynamic graphics, you can go for complementary packages such as RGL. Above all, it follows a comprehensive documentation format that offers documentation in soft as well as hard copy.
What are its Programming Features
R refers to an interpreted language that needs a command line interpreter. Further, it extends support to data structures such as matrices, scalars, vectors, lists, and frames (tables). In addition, it offers both procedural programming as well as object-oriented programming.
Editors and Graphical User Interfaces (GUIs) for R
Some of the text editors supporting the R language are Bluefish, Emacs, Gedit, WinEdt, Crimson Editor, Eclipse, ConTEXT, and RStudio, while a few GUIs are RGUI, Red-R, R Commander, Deducer, Rattle GUI, RExcel, Sage, R AnalyticFlow, Cantor, and RapidMiner.