Book description
Utilize R to uncover hidden patterns in your Big Data
About This Book
 Perform computational analyses on Big Data to generate meaningful results
 Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases,
 Explore fast, streaming, and scalable data analysis with the most cuttingedge technologies in the market
Who This Book Is For
This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows.
It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.
What You Will Learn
 Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities
 Deploy Big Data analytics platforms with selected Big Data tools supported by R in a costeffective and timesaving manner
 Apply the R language to realworld Big Data problems on a multinode Hadoop cluster, e.g. electricity consumption across various sociodemographic indicators and bike share scheme usage
 Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platform
In Detail
Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing.
The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and nonrelational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.
Style and approach
This book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easytofollow steps on how to process, transform and analyse large data sets.
Publisher resources
Table of contents

Big Data Analytics with R
 Big Data Analytics with R
 Credits
 About the Author
 Acknowledgement
 About the Reviewers
 www.PacktPub.com
 Preface
 1. The Era of Big Data
 2. Introduction to R Programming Language and Statistical Environment
 3. Unleashing the Power of R from Within

4. Hadoop and MapReduce Framework for R
 Hadoop architecture
 A singlenode Hadoop in Cloud

HDInsight  a multinode Hadoop cluster on Azure

Creating your first HDInsight cluster
 Creating a new Resource Group
 Deploying a Virtual Network
 Creating a Network Security Group
 Setting up and configuring an HDInsight cluster
 Starting the cluster and exploring Ambari
 Connecting to the HDInsight cluster and installing RStudio Server
 Adding a new inbound security rule for port 8787
 Editing the Virtual Network's public IP address for the head node
 Smart energy meter readings analysis example – using R on HDInsight cluster

Creating your first HDInsight cluster
 Summary
 5. R with Relational Database Management Systems (RDBMSs)
 6. R with NonRelational (NoSQL) Databases
 7. Faster than Hadoop  Spark with R
 8. Machine Learning Methods for Big Data in R
 9. The Future of R  Big, Fast, and Smart Data
Product information
 Title: Big Data Analytics with R
 Author(s):
 Release date: July 2016
 Publisher(s): Packt Publishing
 ISBN: 9781786466457
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