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

Boosting R performance with the data.table package and other tools

The following two sections present several methods of enhancing the speed of data processing in R. The larger part is devoted to the excellent data.table package, which allows convenient and fast data transformations. At the very end of this section we also direct you to other sources, that elaborate, in more detail, on the particulars of faster and better optimized R code.

Fast data import and manipulation with the data.table package

In a chapter devoted to optimized and faster data processing in the R environment, we simply must spare a few pages for one, extremely efficient and flexible package called data.table. The package, developed by Dowle, Srinivasan, Short, and Lianoglou with further contributions from Saporta and Antonyan, took the primitive R data.frame concept one (huge) step forward and has made the lives of many R users so much easier since its release to the community.

The data.table library offers (very) fast...