Preface

As the name suggests, Hands-On Exploratory Data Analysis with R practically demonstrates the complete process of exploratory data analysis. In this book you will learn about the complete process of exploratory data analysis using R and some of its most popular and powerful packages. You will understand the concepts of data analysis right from data ingestion, data cleaning, and data manipulation, to applying statistical techniques and visualizing hidden patterns. By the end of this book, you will be able to expand your real-world R knowledge by means of practical real-world data analysis projects.

Who this book is for

This book is for you if you are looking to build a strong foundation in data analysis. Whether you are a data analyst, data engineer, software engineer, product manager, or anybody who is involved with data in any way, this book will sharpen your skills in the overarching workflow of exploratory data analysis.

What this book covers

Chapter 1, Setting Up Our Data Analysis Environment, introduces the overall goal of this book. This chapter stipulates how exploratory data analysis benefits business and has a significant impact across almost all verticals.

Chapter 2, Importing Diverse Datasets, demonstrates practical, hands-on code examples on reading in all kinds of data into R for exploratory data analysis. This chapter also covers how to use advanced options while importing datasets such as delimited data, Excel data, JSON data, and data from web APIs.

Chapter 3, Examining, Cleaning, and Filtering, introduces how to identify and clean missing and erroneous data formats. This chapter also covers concepts such as data manipulation, wrangling, and reshaping.

Chapter 4, Visualizing Data Graphically with ggplot2, demonstrates how to draw different kinds of plots and charts, including scatter plots, histograms, probability plots, residual plots, boxplots, and block plots.

Chapter 5, Creating Aesthetically Pleasing Reports with knitr and R Markdown, explains how to use RStudio to wrap your code, graphics, plots, and findings in a complete and informative data analysis report. The chapter will also look at how to publish these in different formats for different audiences using R Markdown and packages such as knitr.

Chapter 6, Univariate and Control Datasets, takes a real-world univariate and control dataset and runs an entire exploratory data analysis workflow on it using the R packages and techniques.

Chapter 7, Time Series Datasets, introduces a time series dataset and describes how to use exploratory data analysis techniques to analyze this data.

Chapter 8, Multivariate Datasets, introduces a dataset from the multivariate problem category. This chapter explains how to use exploratory data analysis techniques to analyze this data, as well as how to use the exploratory data analysis techniques of the star plot, the scatter plot matrix, the conditioning plot, and their principal components.

Chapter 9, Multi-Factor Datasets, introduces a multi-factor dataset and explains how to use exploratory data analysis techniques to analyze this data.

Chapter 10, Handling Optimization and Regression Data Problems, introduces a dataset from the regression problem category and describes how to use exploratory data analysis techniques to analyze this data. It also shows how to learn and apply these exploratory data analysis techniques.

Chapter 11, Next Steps, covers how to build a roadmap for yourself to consolidate the skills you have learned in this book and gain further expertise in the field of data science with R.

To get the most out of this book

In order to get the most out of this book, you should already be familiar with the basics of the R programming language and you should possess at least a rudimentary knowledge of data analysis, irrespective of the tool or programming language. If you would like to learn the basics of R, we recommend one of our excellent Packt titles.

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Code in Action

Visit the following link to check out videos of the code being run:
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Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

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html, body, #map {
 height: 100%; 
 margin: 0;
 padding: 0
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[default]
exten => s,1,Dial(Zap/1|30)
exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)

Any command-line input or output is written as follows:

$ mkdir css
$ cd css

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.

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