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.
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Book Overview & Buying
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Table Of Contents
Hands-On Exploratory Data Analysis with R
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Hands-On Exploratory Data Analysis with R
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Overview of this book
Hands-On Exploratory Data Analysis with R will help you build a strong foundation in data analysis and get well-versed with elementary ways to analyze data. You will learn how to understand your data and summarize its characteristics. You'll also study the structure of your data, and you'll explore 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 set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using 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, uncover hidden insights, and present your results in a business context.
Table of Contents (17 chapters)
Preface
Free Chapter
Section 1: Setting Up Data Analysis Environment
Setting Up Our Data Analysis Environment
Importing Diverse Datasets
Examining, Cleaning, and Filtering
Visualizing Data Graphically with ggplot2
Creating Aesthetically Pleasing Reports with knitr and R Markdown
Section 2: Univariate, Time Series, and Multivariate Data
Univariate and Control Datasets
Time Series Datasets
Multivariate Datasets
Section 3: Multifactor, Optimization, and Regression Data Problems
Multi-Factor Datasets
Handling Optimization and Regression Data Problems
Section 4: Conclusions
Next Steps
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