Book Image

R Data Analysis Cookbook - Second Edition

By : Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan
Book Image

R Data Analysis Cookbook - Second Edition

By: Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan

Overview of this book

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.
Table of Contents (14 chapters)


R is a popular statistical programming language with wide range of extensions/packages that support data processing and machine learning tasks. However, data analysis in R is often limited by two main factors:

  • Single threaded runtime environment: This often increasing the processing and makes your data analysis slow.
  • Limitation of single machine’s memory: When accessing data stored in a data.frame or CSV file or any other format in R, the entire data must all fit in memory and this becomes a bottleneck when using a large dataset.

In this chapter, we will discuss how to overcome the two major pain points of R stated earlier by accessing data from datastore (MySql and MongoDB) or running analysis on distributed system with Apache Spark or even leveraging other multi threaded environment such as Java.

Datastore such as MongoDB or MySql are more efficient at...