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E-grāmata: R for Political Data Science: A Practical Guide

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R for Political Data Science: A Practical GuidePalielināt
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R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis.

Key features:











Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R





Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R





Provides a step-by-step guide that you can replicate using your own data





Includes exercises in every chapter for course use or self-study





Focuses on practical-based approaches to statistical inference rather than mathematical formulae





Supplemented by an R package, including all data

As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.

Recenzijas

"Urdinez and Cruz provide a thorough and pedagogically sound introduction to working with political science data in R, complete with modern R code to reproduce every figure and analysis presented. The breadth of statistics and data science methods presented in the book is impressive. The datasets used in examples are real, contemporary, and engaging, which makes the book accessible to anyone interested in quantitative approaches in political science." - Mine Ēetinkaya-Rundel, University of Edinburgh, Duke University, and RStudio.

"This book is a great resource for students learning methods as well as for researchers migrating to R. The volume introduces a wide range of topics, including foundations of R, conventional statistical models, text analysis, networks, maps, and web mining. And there is more! The examples based on Latin America make the book substantively interesting and enjoyable." - Anķbal Pérez-Lińįn, University of Notre Dame

"As others who lacked the capacity to work in R, I was lagging behind regarding my capacity to produce cutting edge empirical analyses for my research. This textbook and its applied pedagogy and examples, significantly reduced the costs of catching up. I highly recommend it, both as a textbook and as a guideline for anyone interested in learning R on their own." - Juan Pablo Luna, Pontificia Universidad Católica de Chile"With its tutorial approach, R for Political Data Science builds readers R literacy without assuming any prior experience with the language. By the end, your practical political data science toolkit will be well-stocked, you will be more motivated to take the next step and study the mathematical underpinnings of the methods discussed throughout, and using R professionally will no longer feel like a pipe dream (pun intended!)." - Santiago Olivella, University of North Carolina Chapel Hill

"If you have a background in Political Science, this is THE BOOK you need to start your journey into R. Using up-to-date tools, this book guides you step-by-step through the process of translating data analysis into political questions. R for Political Data Science not only covers a wide range of techniques and R packages, but also uses Latin American datasets that make the topics covered interesting for a broader audience." - Riva Quiroga, co-founder of R-Ladies Santiago and R-Ladies Valparaķso, editor of The Programming Historian and chair of the Latin-R Conference

"The monograph belongs to The R Series, and presents a reference textbook on R language with a semester course on statistics with application to estimations on real political data...Each chapter suggests references on the recent sources, exercises, and links to numerous websites with data, packages and other R facilities. The book is convenient as a textbook for students, and is equally helpful for researcher and practitioners. The main material in the book consists of R codes, that supplies the readers with amazingly useful tools of modeling not only in political but in a wider area of applied social and other sciences, wherever the statistical analysis is required." - Stan Lipovetsky, Technometrics, April 2021

Preface ix
Who will find this book useful?
ix
About the book
x
What to expect from the book
xii
Book structure
xii
Prerequisites
xiv
How to use the textbook in a methods course?
xiv
Contributors xix
I Introduction to R 1(86)
1 Basic R
3(12)
1.1 Installation
3(1)
1.2 Console
4(1)
1.3 Script
5(1)
1.4 Objects (and functions)
6(9)
2 Data Management
15(22)
2.1 Introduction to data management
15(2)
2.2 Describing a dataset
17(2)
2.3 Basic operations
19(9)
2.4 Chain commands
28(2)
2.5 Recode values
30(7)
3 Data Visualization
37(34)
3.1 Why visualize my data?
37(3)
3.2 First steps
40(8)
3.3 Applied example: Local elections and data visualization
48(18)
3.4 To continue learning
66(5)
4 Data Loading
71(16)
4.1 Introduction
71(2)
4.2 Different dataset formats
73(1)
4.3 Files separated by delimiters (.csv and .tsv)
73(11)
4.4 Large tabular datasets
84(3)
II Models 87(188)
5 Linear Models
89(42)
5.1 OLS in R
90(6)
5.2 Bivariate model: simple linear regression
96(7)
5.3 Multivariate model: multiple regression
103(5)
5.4 Model adjustment
108(1)
5.5 Inference in multiple linear models
109(1)
5.6 Testing OLS assumptions
110(21)
6 Case Selection Based on Regressions
131(16)
6.1 Which case study should I select for qualitative research?
133(12)
6.2 The importance of combining methods
145(2)
7 Panel Data
147(26)
7.1 Introduction
147(5)
7.2 Describing your panel dataset
152(6)
7.3 Modelling group-level variation
158(3)
7.4 Fixed vs. random effects
161(2)
7.5 Testing for unit roots
163(6)
7.6 Robust and panel-corrected standard errors
169(4)
8 Logistic Models
173(36)
8.1 Introduction
173(1)
8.2 Use of logistic models
174(2)
8.3 How are probabilities estimated?
176(6)
8.4 Model estimation
182(4)
8.5 Creating tables
186(4)
8.6 Visual representation of results
190(10)
8.7 Measures to evaluate the fit of the models
200(9)
9 Survival Models
209(26)
9.1 Introduction
209(3)
9.2 How do we interpret hazard rates?
212(1)
9.3 Cox's model of proportional hazards
213(2)
9.4 Estimating Cox Models in R
215(11)
9.5 Tools to interpret and present hazard ratios
226(9)
10 Causal inference
235(40)
10.1 Introduction
235(2)
10.2 Causation and causal graphs
237(2)
10.3 Measuring causal effects
239(2)
10.4 DAGs and statistical associations
241(2)
10.5 Backdoors and do-calculus
243(4)
10.6 Drawing and analyzing DAGs
247(9)
10.7 Making adjustments
256(16)
10.8 Caveats
272(3)
III Applications 275(150)
11 Advanced Political Data Management
277(30)
11.1 Introduction
277(2)
11.2 Merging datasets
279(5)
11.3 Fuzzy or inexact join of data
284(4)
11.4 Missing values' management
288(7)
11.5 Imputation of missing values
295(12)
12 Web Mining
307(20)
12.1 Introduction
307(2)
12.2 Ways to do web scraping
309(1)
12.3 Web scraping in R
310(7)
12.4 Using APIs and extracting data from Twitter
317(10)
13 Quantitative Analysis of Political Texts
327(30)
13.1 Analysis of political hashtags
328(12)
13.2 Wordfish
340(6)
13.3 Structural Topic Modeling
346(11)
14 Networks
357(18)
14.1 Introduction
357(1)
14.2 Basic concepts in a network
358(2)
14.3 Network datasets
360(2)
14.4 Graphic presentation of a network
362(4)
14.5 Measures of centrality
366(9)
15 Principal Component Analysis
375(20)
15.1 Introduction
376(1)
15.2 How PCA works
377(1)
15.3 Basic notions in R
378(5)
15.4 Dimensionality of the concept
383(6)
15.5 Variation of the concept
389(6)
16 Maps and Spatial Data
395(30)
16.1 Introduction
395(3)
16.2 Spatial Data in R
398(3)
16.3 Spatial Data Management
401(6)
16.4 Mapping in R
407(6)
16.5 Inference from Spatial Data
413(12)
IV Bibliography and Index 425(2)
Bibliography 427(10)
Index 437
This book is edited by Francisco Urdinez, Assistant Professor at the Institute of Political Science of the Pontifical Catholic University of Chile, and Andrés Cruz, Adjunct Instructor at the same institution. Most of the authors who contributed with chapters to this volume are political scientists affiliated to the Institute of Political Science of the Pontifical Catholic University of Chile, and many are researchers and collaborators of the Millennium Data Foundation Institute, an institution that aims at gathering, cleaning and analyzing public data to support public policy. Andrew Heiss is affiliated to Georgia State University Andrew Young School of Policy Studies and he joined this project contributing with a chapter on causal inference. Above all, all the authors are keen users of R.
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