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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Setting Up Data Analysis Environment

Section 1: Setting Up Data Analysis Environment

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

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

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

Visualizing Data Graphically with ggplot2

Visualizing Data Graphically with ggplot2

Technical requirements

Advanced graphics grammar of ggplot2

Installing ggplot2

Histogram plots

Probability plots

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

Section 2: Univariate, Time Series, and Multivariate Data

Section 2: Univariate, Time Series, and Multivariate Data

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

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

Multivariate Datasets

Multivariate Datasets

Technical requirements

Introducing and reading a dataset

Cleaning the data

Mapping and understanding the structure

Hypothesis test

Parsimonious model

Data visualization

Section 3: Multifactor, Optimization, and Regression Data Problems

Section 3: Multifactor, Optimization, and Regression Data Problems

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

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

Section 4: Conclusions

Section 4: Conclusions

Next Steps

Technical requirements

What to learn next

The data analysis workflow

Building a data science portfolio

Getting help with exploratory data analysis

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Building a data science portfolio

Now as you get accustomed to a particular language, whether it is R or Python, it is mandatory that you create your own portfolio. Kindly refer to the following steps, which include an approach to creating your own data science portfolio:

Be visible: Always keep your profile updated with your required skillsets to keep yourself visible in the market.
Articulate your ability: Try out different features and experiments, and check on the output you get. This will help to articulate your ability and skills for any area of data science.
Be visual: Always have a look at the trending technologies and the programming languages that are being used. Keep up to date with the algorithms, as algorithms form the base of any implementation.
Showcase process: Include your own algorithms and experiments. Try to showcase them as and when needed.
Stand out from...