We live in times of Internet of Things—a large, world-wide network of interconnected devices, sensors, applications, environments, and interfaces. They generate, exchange, and consume massive amounts of data on a daily basis, and the ability to harness these huge quantities of information can provide us with novel understanding of physical and social phenomena.
The recent rapid growth of various open source and proprietary big data technologies allows deep exploration of these vast amounts of data. However, many of them are limited in terms of their statistical and data analytics capabilities. Some others implement techniques and programming languages that many classically educated statisticians and data analysts are simply unfamiliar with and find them difficult to apply in real-world scenarios.
R programming language—an open source, free, extremely versatile statistical environment, has a potential to fill this gap by providing users with a large variety of highly optimized data processing methods, aggregations, statistical tests, and machine learning algorithms with a relatively user-friendly and easily customizable syntax.
This book challenges traditional preconceptions about R as a programming language that does not support big data processing and analytics. Throughout the chapters of this book, you will be exposed to a variety of core R functions and a large array of actively maintained third-party packages that enable R users to benefit from most recent cutting-edge big data technologies and frameworks, such as Hadoop, Spark, H2O, traditional SQL-based databases, such as SQLite, MariaDB, and PostgreSQL, and more flexible NoSQL databases, such as MongoDB or HBase, to mention just a few. By following the exercises and tutorials contained within this book, you will experience firsthand how all these tools can be integrated with R throughout all the stages of the Big Data Product Cycle, from data import and data management to advanced analytics and predictive modeling.
Chapter 1, The Era of "Big Data", gently introduces the concept of Big Data, the growing landscape of large-scale analytics tools, and the origins of R programming language and the statistical environment.
Chapter 2, Introduction to R Programming Language and Statistical Environment, explains the most essential data management and processing functions available to R users. This chapter also guides you through various methods of Exploratory Data Analysis and hypothesis testing in R, for instance, correlations, tests of differences, ANOVAs, and Generalized Linear Models.
Chapter 3, Unleashing the Power of R From Within, explores possibilities of using R language for large-scale analytics and out-of-memory data on a single machine. It presents a number of third-party packages and core R methods to address traditional limitations of Big Data processing in R.
Chapter 4, Hadoop and MapReduce Framework for R, explains how to create a cloud-hosted virtual machine with Hadoop and to integrate its HDFS and MapReduce frameworks with R programming language. In the second part of the chapter, you will be able to carry out a large-scale analysis of electricity meter data on a multinode Hadoop cluster directly from the R console.
Chapter 5, R with Relational Database Management Systems (RDBMSs), guides you through the process of setting up and deploying traditional SQL databases, for example, SQLite, PostgreSQL and MariaDB/MySQL, which can be easily integrated with their current R-based data analytics workflows. The chapter also provides detailed information on how to build and benefit from a highly scalable Amazon Relational Database Service instance and query its records directly from R.
Chapter 6, R with Non-Relational (NoSQL) Databases, builds on the skills acquired in the previous chapters and allows you to connect R with two popular nonrelational databases a.) a fast and user-friendly MongoDB installed on a Linux-run virtual machine, and b.) HBase database operated on a Hadoop cluster run as part of the Azure HDInsight service.
Chapter 7, Faster than Hadoop: Spark with R, presents a practical example and a detailed explanation of R integration with the Apache Spark framework for faster Big Data manipulation and analysis. Additionally, the chapter shows how to use Hive database as a data source for Spark on a multinode cluster with Hadoop and Spark installed.
Chapter 8, Machine Learning Methods for Big Data in R, takes you on a journey through the most cutting-edge predictive analytics available in R. Firstly, you will perform fast and highly optimized Generalized Linear Models using Spark MLlib library on a multinode Spark HDInsight cluster. In the second part of the chapter, you will implement Naïve Bayes and multilayered Neural Network algorithms using R’s connectivity with H2O-an award-winning, open source, big data distributed machine learning platform.
Chapter 9, The Future of R: Big, Fast and Smart Data, wraps up the contents of the earlier chapters by discussing potential areas of development for R language and its opportunities in the landscape of emerging Big Data tools.
Online Chapter, Pushing R Further, available at https://www.packtpub.com/sites/default/files/downloads/5396_6457OS_ PushingRFurther.pdf, enables you to configure and deploy their own scaled-up and Cloud-based virtual machine with fully operational R and RStudio Server installed and ready to use.
All the code snippets presented in the book have been tested on a Mac OS X (Yosemite) running on a personal computer equipped with 2.3 GHz Intel Core i5 processor, 1 TB Solid State hard drive, and 16 GB of RAM. It is recommended that readers run the scripts on a Mac OS X or Windows machine with at least 4 GB of RAM. In order to benefit from the instructions presented throughout the book, it is advisable that readers install most recent R and RStudio on their machines as well as at least one of the popular web browsers: Mozilla Firefox, Chrome, Safari, or Internet Explorer.
This book is intended for middle level data analysts, data engineers, statisticians, researchers, and data scientists, who consider and plan to integrate their current or future big data analytics workflows with R programming language.
It is also assumed that readers will have some previous experience in data analysis and the understanding of data management and algorithmic processing of large quantities of data. However, they may lack specific R skills related to particular open source big data tools.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The -getmerge option allows to merge all data files from a specified directory on HDFS."
Any command-line input or output is written as follows:
$ sudo –u hdfs hadoop fs –ls /user
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."
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