Easy R: Access, Prepare, Visualize, Explore Data, and Write Papers / Edition 1

by Elizabeth A. Gohmert, Quan L. Li, Douglas R. Wise

Overview

Do you want to learn R? This book is built on the premise that anyone with a bit of free time and a healthy curiosity can learn to use R in their studies or at work. The authors focus on using R to do useful things like writing reports, creating data and graphs, accessing datasets collected by others, preparing data, and conducting simple data analysis.

In this book you’ll learn how to: install R and RStudio®, and set up an RStudio® project and folders; write an essay with graphs based on simple real-world data using R Markdown; create variables from everyday numeric information and visualize data through ­five types of charts—bar plot, histogram, pie chart, scatter plot, and time series line plot—to identify patterns in the data; write and run R programs, and prepare your data following the tidyverse approach; import external datasets into R, install R data packages, and carry out initial data validity checks; conduct exploratory data analysis through three exercises involving data on voting outcomes, natural resource consumption, and gross domestic product (GDP) via data visualization, correlation coeffi­cient, and simple regression; and write a research paper on the impact of GDP per capita on life expectancy using R Markdown.

Student-friendly language and examples (such as binge-watched shows on Netflix, and the top 5 songs on Spotify), cumulative learning, and practice exercises make this a must-have guide for a variety of courses where data are used and reports need to be written.

Code and datasets used to carry out the examples in the book are available on an accompanying website.

Product Details

ISBN-13:	9781544379418
Publisher:	SAGE Publications
Publication date:	02/10/2020
Edition description:	New Edition
Pages:	192
Product dimensions:	7.40(w) x 9.00(h) x 0.50(d)

About the Author

Elizabeth Gohmert graduated from Texas A&M University in 2018 with a Bachelor of Arts in Political Science
and a minor in Cybersecurity—a year early. She then went on to graduate school at Southern Methodist
University, from which she graduated with a Master of Science in Business Analytics in 2019. While she
had previously helped to edit the book, “Using R for Data Analysis in Social Sciences”, this will be her first
authored publication. Aside from her love of data science, she enjoys travelling, learning new languages,
white water rafting, and the joy of finding great new rib places. She currently lives and works in Washington,
D.C. as a data science consultant.

Dr. Quan Li is Professor of Political Science and Cornerstone Faculty Fellow at Texas A&M University and
a Fulbright U.S. Senior Scholar in Spain during the 2019-2020 academic year. His research on economic
globalization, democratic governance, political violence, and environmental degradation has appeared in
various journals in international relations, international business, political science, and public policy. He
coauthored Democracy and Economic Openness in an Interconnected System: Complex Transformations
(Cambridge University Press, 2009) and Politics and Foreign Direct Investment (University of Michigan Press,
2012). He is also the author of Using R for Data Analysis in Social Sciences: A Research Project-Oriented
Approach (Oxford University Press, 2018). Dr. Li has served on the editorial boards of American Journal of
Political Science, Journal of Politics, International Studies Quarterly, and International Interactions. He
is the co-recipient of the 2003 Best Article on Democratization Award from the American Political Science
Association.

Douglas Wise is a Solution Engineer at a Silicon Valley based technology company where he specializes
in helping organizations transform their digital customer experiences. Douglas previously served as an
intelligence analyst in the United States Air Force. Douglas was also a member of the Air Force Honor Guard
and is an Afghanistan war veteran. Douglas received his Bachelor of Science in Political Science from Texas
A&M University and currently resides in Santa Clara, California with his family.

Table of Contents

Preface
Why Should You Learn R Too?
Who Should Read This Book? What Does This Book Hope to Achieve?
What Is in This Book? How Can You Use It?
What Is Unique About This Book?
Acknowledgments
About the Authors
CHAPTER 1 • Making Preparations: Software Installation and Project Setup
1.1 Introduction
1.2 How to Download and Install R for Windows
1.3 How to Download and Install R for Mac
1.4 Downloading and Installing RStudio
1.5 Setting Up a Project in RStudio
1.6 Creating Folders Under a Project
1.7 Summary
1.8 References
CHAPTER 2 • Writing Your Essay Using R Markdown: Something for Everyone
2.1 Introduction
2.2 The Pros of Using R Markdown
2.3 How to Create an R Markdown File
2.4 How to Write and Format Text in R Markdown
2.5 A Simple Example of an R Markdown Document
2.6 Other Useful Formatting Tricks
2.7 How to Use R Markdown for a Writing Assignment: A Bare-Bones Example
2.8 How to Revise and Improve Your Bare-Bones Essay
2.9 For More Ambitious Readers
2.10 Exercise: Turning Knowledge Into Results
2.11 Summary
2.12 References
CHAPTER 3 • Creating Data and Graphs in Your Essays
3.1 Introduction
3.2 Bar Plot I: Graphing the Winners of a Hot Dog Eating Contest
3.3 Bar Plot II: Graphing Winning Lottery Numbers in Texas Pick-3
3.4 Pie Chart: Graphing the Composition of Daily Plays Among Top 5 Songs on Spotify
3.5 Histogram: Graphing the Distribution of LSAT Scores in a Review Class
3.6 Scatter Plot: Graphing the Relationship Between Two Variables— Gas Mileages in the City and on the Highway
3.7 Time Series Plot: Graphing the Changing Pattern of You Tube Video Views
3.8 Useful Tips: Polishing and Exporting Graphs
3.9 Summary
3.10 References
CHAPTER 4 • Preparing Your Data
4.1 Introduction
4.2 Writing and Running a Program in R
4.3 Creating Variables and Forming a Dataset
4.4 Manipulating Data Using the dplyr Package
4.5 Chaining Different Data Manipulation Operations: pipe (%>%)
4.6 Missing Values in R: NA
4.7 Summary
4.8 References
CHAPTER 5 • Accessing Datasets
5.1 Introduction
5.2 Setting Up an RStudio Project
5.3 Downloading a Dataset
5.4 Installing R Packages for Data Importing
5.5 Importing a Downloaded Dataset in RStudio
5.6 Using R Data Packages: A Simple Example With gapminder
5.7 Using R Data Packages: A More Advanced Example With wbstats
5.8 Using R Data Packages: Finding More R Data Packages
5.9 Where Can You Find More Data?
5.10 Summary
5.11 References
CHAPTER 6 • Exploratory Data Analysis: Three Exercises
6.1 Introduction
6.2 Exercise 1: Reporting Results of the 2016 Presidential Primary in King County, Washington
6.3 Exercise 2: Human Use of Natural Resources: Consumption and Biocapacity
6.4 Exercise 3: Exploring the Impact of GDP per Capita on Life Expectancy
6.5 Summary
6.6 References
CHAPTER 7 • Writing Your Research Paper Using R: Analyzing the Effect of Economic Development on Life Expectancy