What's in There - Exploratory Data Analysis

In this chapter, you will cover:

Creating standard data summaries
Extracting a subset of a dataset
Splitting a dataset
Creating random data partitions
Generating standard plots, such as histograms, boxplots, and scatterplots
Generating multiple plots on a grid
Creating plots with the lattice package
Creating charts that facilitate comparisons
Creating charts that help to visualize possible causality

Amazon Customer Oct 02, 2017

The R Data Analysis Cookbook 2nd Edition is primarily focused on real life data analysis and data science activities performed by data analyst/data scientist using R and offers succinct examples on a variety of data analysis topics such as data cleaning & munging, exploratory analysis, vectorized operations, regression, classification, advance clustering, deep learning (image recognition), geospatial analysis, social network analysis, handling large dataset in R with Spark and MongoDB. I enjoyed the section dealing with classification, image recognition and R with distrbuted system. This book does not provide introduction to R language (as it assume the readers to have basic knowledge in R as prerequisite). Although the book provide brief explanation of the machine learning algorithms used in the recipes, with equation, how it works along with its pros/cons, but it doesn't explain in details or great depth about each of the machine learning algorthim. For such information, you will have to look elsewhere such as "Beginning R Programming" and "Machine Learning: An Algorithmic Perspective". Overall it a very good book and hits the road running, if you just have basic knowledge of R programming.

Amazon Verified review

John DCousta Oct 16, 2017

This book is for data analyst and aspiring data science professionals who are familiar with basics of R and want to expand their skill set in data analysis activities (without diving too much into mathematics/statistical jargon)- data cleaning & munging, eda, machine learning such as- regression, classification, advance clustering, deep learning (image recognition), handling large dataset in R with Spark.

Leonardo Damasceno Dec 11, 2017

Did not like. Too superficial. Treat each topic as 'cake recipe'.

Dimitri Shvorob Dec 07, 2017

Looking at the five-star reviews, I notice that "John DCousta" has only reviewed, and given five-star reviews, to Ganguly's two (Packt) books, and "Alessandro Breschi" - whose profile initially had name "Sunith Shetty" - has similarly only reviewed, and given five-star reviews, to three Packt books, one of them plagiarized. In all likelihood, both reviews are fake. Another thing you should know is that Ganguly's other book, "Learning Generative Adversarial Networks", is plagiarized. Even if this one isn't - which I think is unlikely - you should not support a plagiarist by buying his books.