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


We began this chapter by introducing you gently to the rich and abundant world of machine learning algorithms and open-source tools which facilitate their application of large datasets.

We then moved on to practical tutorials during which we presented you with three different machine learning methods run on a multi-node Microsoft Azure HDInsight cluster with Hadoop, Spark, and RStudio Server installed. In the first example you learnt how to perform a logistic regression through the Spark MLlib module using the SparkR package for R with HDFS as a data source.

In two further tutorials, we explored the powerful capabilities of H2O-an open-source, highly-optimized platform for Big Data machine learning models run through the h2o package for R. We applied the Naive Bayes algorithm to predict the classes of the outcome variable and then we compared the achieved performance and accuracy metrics with two models generated by the Neural Networks and Deep Learning techniques.

In the final chapter...