R for Political Data Science

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A01=Andres Cruz
A01=Francisco Urdinez
applied political data modeling in R
ARG
Author_Andres Cruz
Author_Francisco Urdinez
Bra
Category=JHBC
Category=PBT
causal inference techniques
Comprehensive Nuclear Test Ban Treaty
Data Frame
Direct Democracy
Dummy Variables
Econometrics
Education Expenditures
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
FALSE FALSE
FALSE FALSE FALSE
Follow
GDP Growth
Gini Index
Independent Variable
Int
Inverse Probability Weighting
Low Propensity Scores
Network analysis
Ordinary Least Squares
panel data analysis
PCSE
Political Analysis
Political data
Propensity Scores
Proportional Hazard Test
quantitative political methodology
Queen Criterion
R package
Raton
Regression Models
Sage
spatial cluster analysis
Spatial Data Repository
Statistical inference
survival analysis methods
text mining for social science
Tidyverse
UN

Product details

  • ISBN 9780367818890
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 18 Nov 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

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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