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Easy R: Access, Prepare, Visualize, Explore Data, and Write Papers [Pehme köide]

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  • Formaat: Paperback / softback, 192 pages, kõrgus x laius: 231x187 mm, kaal: 330 g
  • Ilmumisaeg: 01-May-2020
  • Kirjastus: SAGE Publications Inc
  • ISBN-10: 1544379412
  • ISBN-13: 9781544379418
Teised raamatud teemal:
Easy R: Access, Prepare, Visualize, Explore Data, and Write PapersSuurem pilt
  • Formaat: Paperback / softback, 192 pages, kõrgus x laius: 231x187 mm, kaal: 330 g
  • Ilmumisaeg: 01-May-2020
  • Kirjastus: SAGE Publications Inc
  • ISBN-10: 1544379412
  • ISBN-13: 9781544379418
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781544379418.html
Märksõnad:

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.
Preface vii
Why Should You Learn R Too? vii
Who Should Read This Book? What Does This Book Hope to Achieve? vii
What Is in This Book? How Can You Use It? viii
What Is Unique About This Book? ix
Acknowledgments xi
About the Authors xiii
Chapter 1 Making Preparations: Software Installation and Project Setup
1(10)
1.1 Introduction
1(1)
1.2 How to Download and Install R for Windows
1(2)
1.3 How to Download and Install R for Mac
3(1)
1.4 Downloading and Installing RStudio
4(1)
1.5 Setting Up a Project in RStudio
5(3)
1.6 Creating Folders Under a Project
8(1)
1.7 Summary
9(1)
1.8 References
10(1)
Chapter 2 Writing Your Essay Using R Markdown: Something for Everyone
11(22)
2.1 Introduction
11(1)
2.2 The Pros of Using R Markdown
12(1)
2.3 How to Create an R Markdown File
12(3)
2.4 How to Write and Format Text in R Markdown
15(1)
2.5 A Simple Example of an R Markdown Document
16(2)
2.6 Other Useful Formatting Tricks
18(2)
2.7 How to Use R Markdown for a Writing Assignment: A Bare-Bones Example
20(4)
2.8 How to Revise and Improve Your Bare-Bones Essay
24(3)
2.9 For More Ambitious Readers
27(2)
2.10 Exercise: Turning Knowledge Into Results
29(1)
2.11 Summary
30(1)
2.12 References
31(2)
Chapter 3 Creating Data and Graphs in Your Essays
33(26)
3.1 Introduction
33(2)
3.2 Bar Plot I: Graphing the Winners of a Hot Dog Eating Contest
35(4)
3.3 Bar Plot II: Graphing Winning Lottery Numbers in Texas Pick-3
39(3)
3.4 Pie Chart: Graphing the Composition of Daily Plays Among Top 5 Songs on Spotify
42(3)
3.5 Histogram: Graphing the Distribution of LSAT Scores in a Review Class
45(3)
3.6 Scatter Plot: Graphing the Relationship Between Two Variables--Gas Mileages in the City and on the Highway
48(4)
3.7 Time Series Plot: Graphing the Changing Pattern of YouTube Video Views
52(4)
3.8 Useful Tips: Polishing and Exporting Graphs
56(1)
3.9 Summary
57(1)
3.10 References
58(1)
Chapter 4 Preparing Your Data
59(24)
4.1 Introduction
59(2)
4.2 Writing and Running a Program in R
61(2)
4.3 Creating Variables and Forming a Dataset
63(1)
4.4 Manipulating Data Using the dplyr Package
64(11)
4.5 Chaining Different Data Manipulation Operations: pipe (% > %)
75(2)
4.6 Missing Values in R: NA
77(3)
4.7 Summary
80(1)
4.8 References
81(2)
Chapter 5 Accessing Datasets
83(34)
5.1 Introduction
83(1)
5.2 Setting Up an RStudio Project
84(1)
5.3 Downloading a Dataset
85(1)
5.4 Installing R Packages for Data Importing
86(1)
5.5 Importing a Downloaded Dataset in RStudio
86(9)
5.6 Using R Data Packages: A Simple Example With gapminder
95(4)
5.7 Using R Data Packages: A More Advanced Example With wbstats
99(11)
5.8 Using R Data Packages: Finding More R Data Packages
110(4)
5.9 Where Can You Find More Data?
114(1)
5.10 Summary
114(1)
5.11 References
115(2)
Chapter 6 Exploratory Data Analysis: Three Exercises
117(46)
6.1 Introduction
117(1)
6.2 Exercise 1: Reporting Results of the 2016 Presidential Primary in King County, Washington
118(9)
6.3 Exercise 2: Human Use of Natural Resources: Consumption and Biocapacity
127(11)
6.4 Exercise 3: Exploring the Impact of GDP per Capita on Life Expectancy
138(20)
6.5 Summary
158(2)
6.6 References
160(3)
Chapter 7 Writing Your Research Paper Using R: Analyzing the Effect of Economic Development on Life Expectancy
163
Elizabeth Gohmert graduated from Texas A&M University in 2018 with a Bachelor of Arts in Political Science and a minor in Cybersecuritya 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.