Hands-on exploratory data analysis with R : become an expert in exploratory data analysis using R packages / Radhika Datar, Harish Garg.

Format:

Book

Author/Creator:

Datar, Radhika, author.

Garg, Harish, author.

Language:

English

Subjects (All):

Databases.

R (Computer program language).

Physical Description:

1 online resource (254 pages)

Edition:

1st edition

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2019.

System Details:

Mode of access: World Wide Web.

text file

Biography/History:

Datar Radhika: Radhika Datar has more than 5 years' experience in software development and content writing. She is well versed in frameworks such as Python, PHP, and Java, and regularly provides training on them. She has been working with Educba and Eduonix as a training consultant since June 2016, while also working as a freelance academic writer in data science and data analytics. She obtained her master's degree from the Symbiosis Institute of Computer Studies and Research and her bachelor's degree from K. J. Somaiya College of Science and Commerce. Garg Harish: BignumWorks Software LLP is an India-based software consultancy that provides consultancy services in the area of software development and technical training. Our domain expertise includes web, mobile, cloud app development, data science projects, in-house software training services, and up-skilling services

Summary:

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 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. What you will learn Learn effective R techniques that can accelerate 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 in data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete exploratory data analysis workflow. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R . If you require support please email: customercare@packt.com

Contents:

Cover

Title Page

Copyright and Credits

Dedication

About Packt

Contributors

Table of Contents

Preface

Section 1: Setting Up Data Analysis Environment

Chapter 1: Setting Up Our Data Analysis Environment

Technical requirements

The benefits of EDA across vertical markets

Manipulating data

Examining, cleaning, and filtering data

Visualizing data

Creating data reports

Installing the required R packages and tools

Installing R packages from the Terminal

Installing R packages from inside RStudio

Summary

Chapter 2: Importing Diverse Datasets

Converting rectangular data into R with the readr R package

readr read functions

read_tsv method

read_delim method

read_fwf method

read_table method

read_log method

Reading in Excel data with the readxl R package

Reading in JSON data with the jsonlite R package

Loading the jsonlite 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

Chapter 3: Examining, Cleaning, and Filtering

About the dataset

Reshaping and tidying up erroneous data

The gather() function

The unite() function

The separate() function

The spread() function

Manipulating and mutating data

The mutate() function

The group_by() function

The summarize() function

The arrange() function

The glimpse() function

Selecting and filtering data

The select() function

The filter() function

Cleaning and manipulating time series data

Chapter 4: Visualizing Data Graphically with ggplot2

Advanced graphics grammar of ggplot2

Data

Layers

Scales.

The coordinate system

Faceting

Theme

Installing ggplot2

Scatter plots

Histogram plots

Density plots

Probability plots

dnorm()

pnorm()

rnorm()

Box plots

Residual plots

Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown

Installing R Markdown

Working with R Markdown

Reproducible data analysis reports with knitr

Exporting and customizing reports

Section 2: Univariate, Time Series, and Multivariate Data

Chapter 6: Univariate and Control Datasets

Reading the dataset

Cleaning and tidying up the data

Understanding the structure of the data

Hypothesis tests

Statistical hypothesis in R

The t-test in R

Directional hypothesis in R

Correlation in R

Tietjen-Moore test

Parsimonious models

The Shapiro-Wilk test

Chapter 7: Time Series Datasets

Introducing and reading the dataset

Cleaning the dataset

Mapping and understanding structure

Hypothesis test

t-test in R

Grubbs' test and checking outliers

Bartlett's test

Data visualization

Autocorrelation plots

Spectrum plots

Phase plots

Chapter 8: Multivariate Datasets

Introducing and reading a dataset

Cleaning the data

Mapping and understanding the structure

Parsimonious model

Levene's test

Principal Component Regression

Partial Least Squares Regression

Section 3: Multifactor, Optimization, and Regression Data Problems

Chapter 9: Multi-Factor Datasets

Introducing and reading the dataset.

Cleaning the dataset

Mapping and understanding data structure

Grubbs test and checking outliers

Multi-factor variance analysis

Exploring graphically the dataset

Chapter 10: Handling Optimization and Regression Data Problems

Mapping and understanding the data structure

Exploration using graphics

Section 4: Conclusions

Chapter 11: Next Steps

What to learn next

Why R?

Environmental setup

R syntax

R packages

Understanding the help system

The data analysis workflow

Data import

Reporting results

Standout as R wizard

Building a data science portfolio

Datasets in R

Getting help with exploratory data analysis

Other Books You May Enjoy

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781789802085

1789802083

OCLC:

1463580318