“R” only appears like a humorous name for a language until you realize
that more than half the alphabet has been used up for one-letter programming
language names. And when you learn that “R” is just an implementation of
another language called “S”. R is named partly after the first names of the
first two R authors and partly as a
play on the name of “S”.R is discovered by Ross Ihaka and Robert Gentleman and S stand for Statistical
programming language.
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For data
scientists, R bids a multitude of features making statistical analysis of
large data sets simple:
- Linear and non-linear modeling
- Time-series analysis
- Clustering
- Easy extensibility and interfaces to other
programming languages
- Sizable shared code package repository
R has a sturdy Integrated Development Environment
(IDE) reachable in R Studio and is
accessible from a number of scripting languages widely used in the data science
community. Anyone seeing a career in data science is going to need more than a fleeting
familiarity with R language.
The main aim of any programming language is to
jumble the numbers and text such way that they can add value to them in order
to give them some meaning. Complexity rises and falls until they achieve
something useful of it, and that’s the reason why programming language is
equipped with a lot of other internal support tools. But certain languages are
designed easier to use with definite tasks. And R is all about data manipulation and visualization. From
Scheme, R adopts lexical scoping and a more object-friendly syntax.
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