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For R, the basic reference is The New S Language: A Programming Environment for Data Analysis and Graphics by Richard A. Becker, John M. Chambers and Allan R. Wilks. The new features of the 1991 release of S are covered in Statistical Models in S edited by John
R Tutorial- Unlock the power of R with our expert-guided R Language tutorial. From basic syntax to advanced data analysis techniques, dive deep into free R programming tutorial for robust statistical modeling and visualization.
R is slower and more memory intensive, than more traditional programming languages such as C/C++, Java, Perl, and Python. In this course, we will learn R from the ground up and try to get you through that initial steep learning curve (whether or not you have previous programming experience).
This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning 1 S system, which was developed at Bell Laboratories by John Chambers et al.
These lecture notes are intended for reference, and will (by the end of the course) contain sections on all the major topics we cover. Lectures will not follow the notes exactly, so be prepared to take your own notes; the practical classes will complement the lectures, and you can be examined on anything we study in either.
This cheat sheet will cover an overview of getting started with R. Use it as a handy, high-level reference for a quick start with R. For more detailed R Cheat Sheets, follow the highlighted cheat sheets below.
The R system for statistical computing is an environment for data analysis and graphics. The root of R is the S language, developed by John Chambers and colleagues (Becker et al., 1988, Chambers and Hastie, 1992, Chambers, 1998) at Bell Laboratories (formerly AT&T, now owned by Lucent Technolo-gies) starting in the 1960s.