Parallel R: Data Analysis in the Distributed World

It's tough to argue with R as a high-quality, cross-platform, open source statistical software productóunless you're in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You'll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE and Hadoop Streaming, including how to find them, how to use them, when they work well and when they don't. With these packages, you can overcome R's single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R's memory barrier. Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop provides low-level access to a popular form of cluster computing RHIPE: uses Hadoop's power with R's language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations

Disclaimer:

• Parallel R: Data Analysis in the Distributed World book is not for reading online or for free download in PDF or eBook format.
• Table of Contents, Index, Syllabus, summary and image of Parallel R: Data Analysis in the Distributed World may be of a different edition or of the same title.
• Price can change due to reprinting, price change by publisher or sourcing cost change for imported books.