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Two R-ticles on Programming with R

Aug 20, 2003, 08:30 (6 Talkback[s])
(Other stories by Cameron Laird, Ramin Nakisa)

developerWorks: R Handy for Crunching Data

[ Thanks to Bob for this link. ]

"developerWorks has published several recent articles on the expanding role of open source software in scientific and engineering work... One of the recurring points scientists made in the interviews for those articles is that they're assessing adoption of open source applications that are worthy competitors to their commercial counterparts, in the dimensions that matter: the programs have nearly all the capabilities of proprietary products, and occasionally more.

"R is just such a program. And although it came up often during that earlier cycle of profiles, I found then that I had to exclude it from those stories, simply to limit the articles to manageable sizes. Several researchers have since emphasized to me that, while R might be statistical rather than scientific in some pedantic sense, it's so important that it deserves prompt attention. Let's take a look, then, at R and related software, with a view to discussing what R means for the server-side developers and administrators who read this column..."

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Linux User & Developer: The Language of Business

[ Thanks to Daniel James for this link. ]

"Although acronyms come and go according to the fortunes of consultancies that coin them, the algorithms and statistical methods that underlie their success are unchanging. One often comes across the same old algorithms dressed in new marketing-speak. This is because a marketing department is unlikely to be excited by the phrase "maximisation of expected utility", but would thrill at the prospect of re-branding this statistical workhorse as a "Customer Delight Engine". This short series of articles will show how a statistical package called R, distributed under the GNU General Public licence at no charge, can be used as the analytical engine for many different Business Intelligence applications.

"R has been designed to allow people to interactively explore data in search of patterns that are not immediately obvious, and is therefore an excellent data mining tool. To stretch the mining analogy further, once an interesting data nugget has been found, a solid, and mathematically well-founded, statistical procedure is required to refine the ore. When tested for statistical significance many data nuggets turn out to be fool's gold. Unlike some commercial applications, R is capable of going beyond the exploratory stage and can be used as a touchstone for assessing the predictive value of data..."

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