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 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, 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 environmentMulticore: popular for multiprocessor and multicore computersParallel: part of the upcoming R 2.14.0 releaseR+Hadoop: provides low-level access to a popular form of cluster computingRHIPE: uses Hadoop’s power with R’s language and interactive shellSegue: lets you use Elastic MapReduce as a backend for lapply-style operations Its tough to argue with R as a high-quality, cross-platform, open source statistical software productunless youre 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. Youll 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 dont. With these packages, you can overcome Rs single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address Rs memory barrier. Annotation R is a wonderful thing: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations become especially troublesome in the current era of large-scale data analysis. It's possible to break past these boundaries by putting R on the parallel path
