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The base R distribution is designed to operate with data that fits into computer memory. Often, the data we want to analyze is so large that processing it all in the memory of a single computer isn’t possible. In some cases, we can take advantage of on-demand computing resources, such as Amazon’s EC2, and have access to machines with over 100 GB of memory. To do this efficiently, however, we often need to be aware that processing very large data sets, even in memory, can still be very time consuming in R and we may continue to need a way to improve performance. Consequently, the approaches for handling BigData in R can be roughly grouped into three broad areas.

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The first approach to handling Big Data is to carry out sampling. That is, we will not use all of the data available to us to build our model, but will create a representative sample of these data. Sampling is generally the least recommended approach as it is natural to expect degradation in model performance when we use fewer training data. This approach can potentially work quite well if the size of the sample we are able to use is still very large in absolute size (for example, a billion rows) as well as in relative size with respect to the original data set. Great care must be taken in order to avoid introducing any form of bias in the sample.
A second approach to working with Big Data is to take advantage of distributed processing. The key idea here is to split our data across different machines working together in a cluster. Individually, the machines need not be very powerful because they will only process chunks of the data.
The Programming with Big Data in R project has a number of R packages for high-performance computing that interface with parallel processing libraries. More details on this project can be found through the project’s website,, and by first starting out with the pbdDEMO package, which is designed for newcomers to this project.
Another alternative is to interface R to work directly with a distributed processing platform such as Apache Hadoop. An excellent reference for doing this is BigData Analytics with R and Hadoop published by Packt Publishing. Finally, an exciting new alternative to working with Hadoop is the Apache Spark project. SparkR is a package that allows running jobs on a Spark cluster directly from the R shell. This package is currently available at
The third possible avenue for working with Big Data is to work with (potentially on-demand) resources that have very high memory and optimize performance on a single machine. One possibility for this is to interface with a language such as C++ and leverage access to advanced data structures that can optimize the processing of data for a particular problem. This way, some of the processing can be done outside of R.
In R, the package Rcpp provides us with an interface to work with C++. Another excellent package for working with large data sets, and the one we will use in this chapter when we load some real-world data sets, is the package data.table, specifically designed to work with machines that have a lot of memory.
Loading data sets on the order of 100 GB on a 64-bit machine is a common use case when working with the data.table package. This package has been designed with the goal of substantially reducing the computation time of common operations that are performed on data frames. More specifically, it introduces the notion of a data table as a replacement data structure for R’s ubiquitous dataframe. This is not only a more efficient data structure on which to perform operations, but has a number of shortcuts and commands that make programming with data sets faster as well.
A critical advantage of this package is that the data table data structure is accepted by other packages anywhere a data frame is. Packages that are unaware of the data table syntax can use data frame syntax for working with datatables. An excellent online resource to learn more about the data.tablepackage is an online course by Matt Dowle, the main creator of the package, and can be found at Without further ado, we will start building some recommender systems where we will load the data in data tables using the data.table package.
The term Big Data has been used to describe the ever growing volume, velocity, and variety of data being generated on the Internet in connected devices and many other places. Many organizations now have massive datasets that measure in petabytes (one petabyte is 1,048,576 gigabytes), more than ever before. Processing and analyzing Big Data is extremely challenging for traditional data processing tools and database architectures.
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In 2005, Doug Cutting and Mike Cafarella at Yahoo! developed Hadoop, based on earlier work by Google, to address these challenges. They set out to develop a new data platform to process, index, and query billions of web pages efficiently. With Hadoop, the work which would have previously required very expensive supercomputers can now be done on large clusters of inexpensive standard servers. As the volume of data grows, more servers can simply be added to a Hadoop cluster to increase the storage capacity and computing power. Since then, Hadoop and its ecosystem of tools has become one of the most popular suites of tools to collect, store, process and analyze large datasets. In this chapter, we will learn how to tap into the power of Hadoop from R.
Big Data
Big Data with R caters to two areas of concern:
  • The amount of data that you want to analyze might not fit in the memory of one machine
  • The amount of time needed to process all of the data might be considerable, and you can split up the processing among machines or nodes in a clusterR Bigdata Training Mumbai
Along with this effort, an interesting avenue is running your R program against Big Data on an Amazon cluster. Amazon AWS offers support for R in its service offerings. There is also a free trial period where you can try out these services. I have used AWS for other projects and found it very convenient and reasonably priced.
Also, note that many of the packages used in Big Data are not available for your typical Windows machine. You can attempt to install them, but the install will throw an error message like Binaries not available for 3.1.1. Source available, which means that the authors never counted on someone installing pbdR or its colleague libraries on a desktop machine.
The pbdR project was started to organize all of the separate efforts involved with Programming with Big Data in R. The group has utility libraries available, such as pdbDEMO, pdbMPI, and pdbPROF. The focus is on the single program / multiple data model: one R program over various chunks of the data possibly distributed over several machines.R Bigdata training Mumbai
A good showcase for pbdR is the pbdDEMO library. It provides prebuilt samples using their other packages, so you can quickly see the effects of your implementation.

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