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Parallel R
book

Parallel R

by Q. Ethan McCallum, Stephen Weston
October 2011
Intermediate to advanced
126 pages
3h 10m
English
O'Reilly Media, Inc.
Content preview from Parallel R

Chapter 1. Getting Started

This chapter sets the pace for the rest of the book. If you’re in a hurry, feel free to skip to the chapter you need. (The section In a Hurry? has a quick-ref look at the various strategies and where they fit. That should help you pick a starting point.) Just make sure you come back here to understand our choice of vocabulary, how we chose what to cover, and so on.

Why R?

It’s tough to argue with R. Who could dislike a high-quality, cross-platform, open-source statistical software product? It has an interactive console for exploratory work. It can run as a scripting language to repeat a process you’ve captured. It has a lot of statistical calculations built-in so you don’t have to reinvent the wheel. Did we mention that R is free?

When the base toolset isn’t enough, R users have access to a rich ecosystem of add-on packages and a gaggle of GUIs to make their lives even easier. No wonder R has become a favorite in the age of Big Data.

Since R is perfect, then, we can end this book. Right?

Not quite. It’s precisely the Big Data age that has exposed R’s blemishes.

Why Not R?

These imperfections stem not from defects in the software itself, but from the passage of time: quite simply, R was not built in anticipation of the Big Data revolution.

R was born in 1995. Disk space was expensive, RAM even more so, and this thing called The Internet was just getting its legs. Notions of “large-scale data analysis” and “high-performance computing” were reasonably rare. Outside ...

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Publisher Resources

ISBN: 9781449317850Errata