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

The current state of Big Data analytics with R

This section will serve as a critical evaluation and summary of the R language's ability to process very large, out-of memory data and its connectivity with a variety of existing Big Data platforms and tools.

Out-of-memory data on a single machine

We began the book with a brief revision of the most common techniques used to analyze data with the R language (Chapter 2, Introduction to R Programming Language and Statistical Environment). We guided you from importing the data into R, through data management and processing methods, cross-tabulations, aggregations, hypothesis testing, and visualizations. We then explained major limitations of the R language in terms of its requirement of memory resources for data storage and its speed of processing. We said that the data must fit within the available RAM installed on your computer if you were to use only a single machine for data processing in  the R language. However, as a system runs other processes...