图书简介
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 RMakes extensive use of the Tidyverse, the group of packages that has revolutionized the use of RProvides a step-by-step guide that you can replicate using your own dataIncludes exercises in every chapter for course use or self-studyFocuses on practical-based approaches to statistical inference rather than mathematical formulaeSupplemented by an R package, including all dataAs 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 conclu
I Introduction to R1. Basic RAndrés Cruz
2. Data ManagementAndrés Cruz
3. Data VisualizationSoledad Araya
4. Data LoadingSoledad Araya and Andrés Cruz
II Models5. Linear ModelsInés Fynn and Lihuen Nocetto
6. Case Selection Based on RegressionsInés Fynn and Lihuen Nocetto
7. Panel DataFrancisco Urdinez
8. Logistic ModelsFrancisco Urdinez
9. Survival ModelsFrancisco Urdinez
10. Causal InferenceAndrew Heiss
III Applications11. Advanced Political Data ManagementAndrés Cruz and Francisco Urdinez
12. Web MiningGonzalo Barría
13. Quantitaive Text AnalysisSebastián Huneeus
14. NetworksAndrés Cruz
15. Principal Component AnalysisCaterina Labrín and Francisco Urdinez
16. Maps and Spatial DataAndrea Escobar and Gabriel Ortiz
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