Book Image

Big Data Analytics with R

By : Simon Walkowiak
Book Image

Big Data Analytics with R

By: Simon Walkowiak

Overview of this book

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 non-relational 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.
Table of Contents (16 chapters)
Big Data Analytics with R
About the Author
About the Reviewers

Chapter 3. Unleashing the Power of R from Within

In the first chapter we introduced you to a number of general terms and concepts related to Big Data. In Chapter 2, Introduction to R Programming Language and Statistical Environment, we presented you with several frequently used methods for data management, processing, and analysis using the R language and its statistical environment. In this chapter we will merge both topics and attempt to explain how you can use powerful mathematical and data modeling R packages in large datasets, without the need for distributed computing. After reading this chapter you should be able to:

  • Understand R's traditional limitations for Big Data analytics and how they can be resolved

  • Use R packages such as ff, ffbase, ffbase2, and bigmemory to enhance out-of-memory performance

  • Apply statistical methods to large R objects through the biglm and ffbase packages

  • Enhance the speed of data processing with R libraries supporting parallel computing

  • Benefit from faster data...