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R is an open source programming language and software environment for statistical computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
R is an implementation of the S programming language. The R language came to use quite a bit after S had been developed. One key limitation of the S language was that it was only available in a commercial package, S-PLUS. In 1991, R was created by Ross Ihaka and Robert Gentleman in the Department of Statistics at the University of Auckland. In 1993 the first announcement of R was made to the public.
A major advantage that R has over many other statistical packages and is that it’s free in the sense of free software. R is both flexible and powerful. It has an amazing ecosystem for developers and It has wide range of packages for data access, data cleaning or munging, performing Analysis, creating Reports etc.
The primary R system is available from the Comprehensive R Archive Network, also known as CRAN. CRAN also hosts many add-on packages that can be used to extend the functionality of R. Over 6,789 packages are available on CRAN that have been developed by users and programmers around the world.
If you are ready to learn R Programming by reading stuff, of course my website will help you to learn that step by step . There are few very useful resources available from CRAN:
Hands-On Programming with R
This book is written by RStudio’s chief trainer Garrett Grolemundand is filled with several practical data analysis projects of a casino (Weighted Dice, Playing Cards, Slot Machine).
It is not a comprehensive guide to the R programming language, but an excellent primer to get started with the language.
The Art of R Programming – A Tour of Statistical Software Design
If you are a software engineer looking to build software in R, this is the book for you. On the other hand, the book might not be ideal if you are a statistician trying to learn programming through R.
Beginning R: The Statistical Programming Language
An excellent book for beginners who want to learn the notoriously complex R concepts through clear simple statistical examples.