By Meghna Gangwar
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R is one of the most popular scripting languages for statistical programming today. The demand of R programmers has been constantly on the rise since the early 2010s and R still enjoys the status as a go-to programming language among data scientists.
R has also been adapted to deep learning these days and this helped several statisticians take on to deep learning in their respective fields easily, making R an indispensable part of the current burgeoning AI scenario.
Recommended Read: Python Data Science Libraries
R has a precursor named S (S stands for statistics) language, developed by AT&T for statistical computation. AT&T began its work on S in 1976, as a part of its internal statistical analysis environment, which was earlier implemented as FORTRAN libraries.
The man behind S was John Chambers. The single-letter name S was inspired by the ubiquitous C language for programming at the time.
R was developed by Ross Ihaka and Ross Gentleman in a project that was conceived in 1992 at the University of Auckland, New Zealand. The first version was released in 1995 and the first stable beta version came up in the year 2000.
R initially differed from S as it added lexical scoping semantics on top of the existing S functionalities. The mono-letter name R was inspired by S again, taking the first letter of both the authors’ first names.
R was developed under GNU public license and openly distributable.
S programming language was later developed into S-plus by TIBCO corporation that bought it from AT&T, by adding some advanced analytical abilities and OOP capabilities.
R still remains more dominantly used statistical programming language compared to S and S-plus, and rightly so, owing to many of its virtues.
R is thought to be the least disliked programming language. Despite all its advantages, R is far from perfect, like any other language. Before plunging into learning R, it will be useful to keep the shortcomings in mind.
R is available as a command-line interface environment at CRAN project (standing for Comprehensive R Archive Network). However, as a beginner you will learn faster with the help of an IDE, of which there are quite a few for R.
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