Book Image

Big Data Analytics with R and Hadoop

By : Vignesh Prajapati
Book Image

Big Data Analytics with R and Hadoop

By: Vignesh Prajapati

Overview of this book

<p>Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. New methods of working with big data, such as Hadoop and MapReduce, offer alternatives to traditional data warehousing. <br /><br />Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.<br /><br />You will start with the installation and configuration of R and Hadoop. Next, you will discover information on various practical data analytics examples with R and Hadoop. Finally, you will learn how to import/export from various data sources to R. Big Data Analytics with R and Hadoop will also give you an easy understanding of the R and Hadoop connectors RHIPE, RHadoop, and Hadoop streaming.</p>
Table of Contents (16 chapters)
Big Data Analytics with R and Hadoop
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Preface
Index

Learning about data files as database


While dealing with the data analytics activities, we need to do data importing, loading, or exporting functionalities all the time. Sometimes the same operations need to be iterated with R programming language. So, we can use the available R function for performing the same data activities.

Understanding different types of files

There are commonly four different types of data files used with R for data storage operations. They are as follows:

  • CSV (Comma Separated Values)

  • Txt (with Tab Separated Values)

  • .RDATA (R's native data format)

  • .rda (R's native data format)

Installing R packages

To use the data file with the format specified earlier, we don't need to install extra R packages. We just need to use the built-in functions available with R.

Importing the data into R

To perform analytics-related activities, we need to use the following functions to get the data into R:

  • CSV: read.csv() is intended for reading the comma separated value (CSV) files, where the decimal...