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

Applied data science with R

Applied data science covers all the activities and processes data analysts must typically undertake to deliver evidence-based results of their analyses. This includes data collection, preprocessing data that may contain some basic but frequently time-consuming data transformations, and manipulations, EDA to describe the data under investigation, research methods, and statistical models applicable to the data and related to the research questions, and finally, data visualizations and reporting the insights. Data science is an enormous field, covering a great number of specific disciplines, techniques, and tools, and there are hundreds of very good printed and online resources explaining the particulars of each method or application.

In this section, we will merely focus on a small fraction of selected topics in data science using the R language. From this moment on, we will also be using real data sets from socio-economic domains. These data sets, however, will...