Exploratory analysis techniques are one part of the larger process of collecting data, learning from data, acting on data, and exploring data to uncover a meaningful pattern. The **Exploratory Data Analysis** (**EDA**) is a crucial step to take before diving into advanced analytics and machine learning, as it provides the context needed to develop an appropriate model for the problem at hand and to correctly interpret its results through visualization techniques to tease apart hidden patterns. In this chapter, we will discuss some of EDA's most common and essential practices, in order to summarize and visualize data so that the task of finding trends and patterns becomes causally easier.

#### R Data Analysis Cookbook - Second Edition

##### By :

#### R Data Analysis Cookbook - Second Edition

##### By:

#### 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)

Preface

Free Chapter

Acquire and Prepare the Ingredients - Your Data

What's in There - Exploratory Data Analysis

Where Does It Belong? Classification

Give Me a Number - Regression

Can you Simplify That? Data Reduction Techniques

Lessons from History - Time Series Analysis

How does it look? - Advanced data visualization

This may also interest you - Building Recommendations

It's All About Your Connections - Social Network Analysis

Put Your Best Foot Forward - Document and Present Your Analysis

Work Smarter, Not Harder - Efficient and Elegant R Code

Where in the World? Geospatial Analysis

Playing Nice - Connecting to Other Systems

Customer Reviews