Hands-On Exploratory Data Analysis with R

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills

Key Features

• Speed up your data analysis projects using powerful R packages and techniques
• Create multiple hands-on data analysis projects using real-world data
• Discover and practice graphical exploratory analysis techniques across domains

Book Description

Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You’ll also uncover the structure of your data, and you’ll learn graphical and numerical techniques using the R language.

This book covers the entire exploratory data analysis (EDA) process―data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems.

By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context.

What you will learn

• Learn powerful R techniques to speed up your data analysis projects
• Import, clean, and explore data using powerful R packages
• Practice graphical exploratory analysis techniques
• Create informative data analysis reports using ggplot2
• Identify and clean missing and erroneous data
• Explore data analysis techniques to analyze multi-factor datasets

Who this book is for

Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Table of Contents

1. Setting Up Our Data Analysis Environment
2. Importing Diverse Datasets
3. Examining, Cleaning, and Filtering
4. Visualizing Data Graphically with ggplot2
5. Creating Aesthetically Pleasing Reports with knitr and R Markdown
6. Univariate and Control Datasets
7. Time Series Datasets
8. Multivariate Datasets
9. Multi-Factor Datasets
10. Handling Optimization and Regression Data Problems
11. Next Steps