Learn R for Applied Statistics: With Data Visualizations, Regressions, and Statistics

218 Pages·2019·14.46 MB·English

by  Eric Goh Ming Hui

Eric Goh Ming Hui
Learn R for Applied Statistics
With Data Visualizations, Regressions, and Statistics
Eric Goh Ming Hui
Singapore, Singapore
Any source code or other supplementary material referenced by the author in this
book is available to readers on GitHub via the book’s product page, located at
www.apress.com/978-1-4842-4199-8 . For more detailed information, please
visit www.apress.com/source-code .
ISBN 978-1-4842-4199-8 e-ISBN 978-1-4842-4200-1
https://doi.org/10.1007/978-1-4842-4200-1
Library of Congress Control Number: 2018965216
© Eric Goh Ming Hui 2019
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Introduction
Who is this book for?
This book is primarily targeted to programmers or learners who want to learn R
programming for statistics. This book will cover using R programming for
descriptive statistics, inferential statistics, regression analysis, and data
visualizations.
How is this book structured?
The structure of the book is determined by following two requirements:
This book is useful for beginners to learn R programming for statistics.
This book is useful for experts who want to use this book as a reference.
Topic Chapters
Introduction to R and R programming fundamentals 1 to 3
Descriptive statistics, data visualizations, inferential statistics, and regression analysis 4 to 6
Contacting the Author
More information about Eric Goh can be found at www.svbook.com . He can
be reached at [email protected] .
Acknowledgments
Let me begin by thanking Celestin Suresh John, the Acquisition Editor and
Manager, for the LinkedIn message that triggered this project. Thanks to Amrita
Stanley, project manager of this book, for her professionalism.
It took a team to make this book, and it is my great pleasure to acknowledge
the hard work and smart work of Apress team. The following are a few names to
mention: Matthew Moodie, the Development Editor; Divya Modi, the
Coordinating Editor; Mary Behr for copy editing; Kannan Chakravarthy for
proofreading; Irfanullah for indexing; eStudioCalamar and Freepik for image
editing; Krishnan Sathyamurthy for managing the production process; and
Parameswari Sitrambalam for composing. I am also thankful to Preeti Pandhu,
the technical reviewer, for thoroughly reviewing this book and offering valuable
feedback.
Table of Contents
Chapter 1: Introduction
What Is R?
High-Level and Low-Level Languages
What Is Statistics?
What Is Data Science?
What Is Data Mining?
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
What Is Text Mining?
Data Acquisition
Text Preprocessing
Modeling
Evaluation/Validation
Applications
Natural Language Processing
Three Types of Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Big Data
Volume
Velocity
Variety
Why R?
Conclusion
References
Chapter 2: Getting Started
What Is R?
The Integrated Development Environment
RStudio: The IDE for R
Installation of R and RStudio
Writing Scripts in R and RStudio
Conclusion
References
Chapter 3: Basic Syntax
Writing in R Console
Using the Code Editor
Adding Comments to the Code
Variables
Data Types
Vectors
Lists
Matrix
Data Frame
Logical Statements
Loops
For Loop
While Loop
Break and Next Keywords