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Revolution Analytics: R Language Features
A big list of the things R can do

R is an incredibly comprehensive statistics package. Even if you just look at the standard R distribution (the base and recommended packages), R can do pretty much everything you need for data manipulation, visualization, and statistical analysis. And for everything else, there's more than 5000 packages on CRAN and other repositories, and the big-data capabilities of Revolution R Enterprise.

As a result, trying to make a list of everything R can do is a difficult task. But we've made an effort in this list of R Language Features, a new section on the Revolution Analytics website. It's broken up into four main sections (analytics, graphics and visualization, R applications and extensions, and programming language features), each with their own subsections:

ANALYTICS Basic Mathematics Basic Statistics Probability Distributions Big Data Analytics * Machine Learning Optimization and Mathematical Programming Signal Processing Simulation and Random Number Generation Statistical Modeling Statistical Tests

GRAPHICS AND VISUALIZATION Static Graphics Dynamic Graphics Devices and Formats

R APPLICATIONS and EXTENSIONS*** Applications Data Mining and Machine Learning Statistical Methodology Other Distributions Available in Third-Party Packages ***

PROGRAMMING LANGUAGE FEATURES Input / Output Object-oriented programming Distributed Computing Included R Packages



The asterisks indicate features not part of the standard R distribution, as follows:
* Requires Revolution R Enterprise
** Requires Revolution R Enterprise for IBM Netezza
*** Requires additional open-source community packages from CRAN  

Click on the links above for details of R's capabilities within each of these sections. Is there anything R can do that we missed in the list? Let us know in the comments below.

Read the original blog entry...

About David Smith
David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid



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