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The R Project for Statistical Computing R is a free software environment for statistical computing and graphics It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS To download R, please choose your preferred CRAN mirror
R (programming language) - Wikipedia R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland [13]
R CODER The best way to start learning R PROGRAMMING Are you a beginner in R programming? This tutorials offer you the basic contents to be able to continue learning R When carrying out a statistical study, it is necessary to transform raw data into data with the appropriate format Learn how to import, export and manipulate data
The Comprehensive R Archive Network R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc
R: What is R? - The R Project for Statistical Computing R is a language and environment for statistical computing and graphics It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT T, now Lucent Technologies) by John Chambers and colleagues
R: Documentation Browsable HTML versions of the manuals, help pages and NEWS for the developing versions of R “ R-patched ” and “ R-devel ”, updated daily CRAN has a growing list of contributed documentation in a variety of languages
The R Foundation Provide support for the R project and other innovations in statistical computing We believe that R has become a mature and valuable tool and we would like to ensure its continued development and the development of future innovations in software for statistical and computational research
Getting Help with R For information on how to ask questions, see, e g , the R mailing list posting guide, and the document about how to create reproducible examples for R on Stack Overflow