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It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product-unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets. You’ll learn the basics of Snow, Multicore, Parallel, and some Hadoop-related tools, 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 About the Authors Q Ethan McCallum is a consultant, writer, and technology enthusiast, though perhaps not in that order. His work has appeared online on The O’Reilly Network and Java.net, and also in print publications such as C/C++ Users Journal, Doctor Dobb’s Journal, and Linux Magazine. In his professional roles, he helps companies to make smart decisions about data and technology. Stephen Weston has been working in high performance and parallelcomputing for over 25 years. He was employed at Scientific Computing Associates in the 90's, working on the Linda programming system, invented by David Gelernter. He was also a founder of Revolution Computing, leading the development of parallel computing packages for R, including nws, foreach, doSNOW, and doMC. He works at Yale University as an HPC Specialist. Table of Contents Chapter 1 Getting Started     Why R?     Why Not R?     The Solution: Parallel Execution     A Road Map for This Book     In a Hurry?     Summary Chapter 2 snow     Quick Look     How It Works     Setting Up     Working with It     When It Works…     …And When It Doesn’t     The Wrap-up Chapter 3 multicore     Quick Look     How It Works     Setting Up     Working with It     When It Works…     …And When It Doesn’t     The Wrap-up Chapter 4 parallel     Quick Look     How It Works     Setting Up     Working with It     Summary of Differences     When It Works…     …And When It Doesn’t     The Wrap-up Chapter 5 A Primer on MapReduce and Hadoop     Hadoop at Cruising Altitude     A MapReduce Primer     Thinking in MapReduce: Some Pseudocode Examples     Binary and Whole-File Data: SequenceFiles     No Cluster? No Problem! Look to the Clouds…     The Wrap-up Chapter 6 R+Hadoop     Quick Look     How It Works     Setting Up     Working with It     When It Works…     …And When It Doesn’t     The Wrap-up Chapter 7 RHIPE     Quick Look     How It Works     Setting Up     Working with It     When It Works…     …And When It Doesn’t     The Wrap-up Chapter 8 Segue     Quick Look     How It Works     Setting Up     Working with It     When It Works…     …And When It Doesn’t     The Wrap-up Chapter 9 New and Upcoming     DoRedis     RevoScale R and RevoConnectR (RHadoop)     CloudNumbers.com