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


In this chapter we have began our journey through the meanders of Big Data analytics with R. First, we introduced you to the structure, definition, and major limitations of the R programming language hoping that this may clarify why traditionally R was an unlikely choice for a Big Data analyst. But then we showed you how some of these concerns can be quite easily dispelled by using several powerful R packages which facilitate processing and analysis of large datasets.

We have spent a large proportion of this chapter on approaches, that allow out-of-memory data management, first through the ff and ffbase packages, and later by presenting methods contained within the bigmemory package and other libraries that support operations and analytics on big.matrix objects.

In the second part of the chapter we moved on to methods that can potentially boost the performance of your R code. We explored several applications of parallel computing through the parallel and foreach packages and you learnt...