Book Image

Hands-On Exploratory Data Analysis with R

By : Radhika Datar, Harish Garg
Book Image

Hands-On Exploratory Data Analysis with R

By: Radhika Datar, Harish Garg

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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Code in Action
Get in touch
Free Chapter
1
Section 1: Setting Up Data Analysis Environment
Section 1: Setting Up Data Analysis Environment
2
Setting Up Our Data Analysis Environment
Setting Up Our Data Analysis Environment
Technical requirements
The benefits of EDA across vertical markets
Manipulating data
Installing the required R packages and tools
Summary
3
Importing Diverse Datasets
Importing Diverse Datasets
Technical requirements
Converting rectangular data into R with the readr R package
Reading in Excel data with the readxl R package
Reading in JSON data with the jsonlite R package
Getting data into R from web APIs using the httr R package
Getting data into R by scraping the web using the rvest package
Importing data into R from relational databases using the DBI R package
Summary
4
Examining, Cleaning, and Filtering
Examining, Cleaning, and Filtering
Technical requirements
About the dataset
Reshaping and tidying up erroneous data
Manipulating and mutating data
Selecting and filtering data
Cleaning and manipulating time series data
Summary
5
Visualizing Data Graphically with ggplot2
Visualizing Data Graphically with ggplot2
Technical requirements
Advanced graphics grammar of ggplot2
Installing ggplot2
Scatter plots
Histogram plots
Density plots
Probability plots
Box plots
Residual plots
Summary
6
Creating Aesthetically Pleasing Reports with knitr and R Markdown
Creating Aesthetically Pleasing Reports with knitr and R Markdown
Technical requirements
Installing R Markdown
Reproducible data analysis reports with knitr
Exporting and customizing reports
Summary
7
Section 2: Univariate, Time Series, and Multivariate Data
Section 2: Univariate, Time Series, and Multivariate Data
8
Univariate and Control Datasets
Univariate and Control Datasets
Technical requirements
Reading the dataset
Cleaning and tidying up the data
Understanding the structure of the data
Hypothesis tests
Tietjen-Moore test
Parsimonious models
Probability plots
The Shapiro-Wilk test
Summary
9
Time Series Datasets
Time Series Datasets
Technical requirements
Introducing and reading the dataset
Cleaning the dataset
Mapping and understanding structure
Hypothesis test
Grubbs' test and checking outliers
Parsimonious models
Bartlett's test
Data visualization
Summary
10
Multivariate Datasets
Multivariate Datasets
Technical requirements
Introducing and reading a dataset
Cleaning the data
Mapping and understanding the structure
Hypothesis test
Parsimonious model
Levene's test
Data visualization
Summary
11
Section 3: Multifactor, Optimization, and Regression Data Problems
Section 3: Multifactor, Optimization, and Regression Data Problems
12
Multi-Factor Datasets
Multi-Factor Datasets
Technical requirements
Introducing and reading the dataset
Cleaning the dataset
Mapping and understanding data structure
Hypothesis test
Grubbs test and checking outliers
Parsimonious model
Multi-factor variance analysis
Exploring graphically the dataset
Summary
13
Handling Optimization and Regression Data Problems
Handling Optimization and Regression Data Problems
Technical requirements
Introducing and reading a dataset
Cleaning the dataset
Mapping and understanding the data structure
Hypothesis test
Grubbs' test and checking outliers
Parsimonious model
Exploration using graphics
Summary
14
Section 4: Conclusions
Section 4: Conclusions
15
Next Steps
Next Steps
Technical requirements
What to learn next
Why R?
The data analysis workflow
Building a data science portfolio
Datasets in R
Getting help with exploratory data analysis
Summary
16
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Cleaning and tidying up the data

Data cleaning, or rather tidying up the data, is the process of transforming the raw data into a specific form of consistent data that includes a simpler form of analysis. Cleaning the attributes of the bank dataset is considered quite critical and should be performed carefully. The R workspace includes a set of comprehensive tools that are specifically designed to clean the data in an effective manner. The following steps are implemented to this end:

  1. Initial explanatory analysis
  2. Data visualization
  3. Error cleaning

Here, we will focus on various aspects of understanding the data summary and also getting a feel for the data. We will also implement the libraries required to clean and tidy up the data by observing the following steps:

  1. Include the requisite libraries (as discussed in Chapter 3, Examining, Cleaning, and Filtering) that assist in cleaning...
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