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 2. Introduction to R Programming Language and Statistical Environment

In Chapter 1, The era of "Big Data", you have become familiar with the most useful Big Data terminology, and a small selection of typical tools applied to unusually large or complex data sets. You have also gained essential insights into how R was developed and how it became the leading statistical computing environment and programming language favored by technology giants and the best universities in the world. In this chapter you will have the opportunity to learn some most important R functions from base R installation and well-known third party packages used for data crunching, transformation, and analysis. More specifically in this chapter you will:

  • Understand the landscape of available R data structures

  • Be guided through a number of R operations allowing you to import data from standard and proprietary data formats

  • Carry out essential data cleaning and processing activities such as subsetting, aggregating, creating...