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What this book covers

Chapter 1, Setting Up Our Data Analysis Environmentintroduces 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 Datasetsdemonstrates 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 Filteringintroduces 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 ggplot2demonstrates 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 Markdownexplains 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 Datasetstakes 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 Datasetsintroduces a time series dataset and describes how to use exploratory data analysis techniques to analyze this data.

Chapter 8, Multivariate Datasetsintroduces 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 Datasetsintroduces a multi-factor dataset and explains how to use exploratory data analysis techniques to analyze this data.

Chapter 10, Handling Optimization and Regression Data Problemsintroduces 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 Stepscovers 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.

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