R for Political Data Science:A Practical Guide


R for Political Data Science:A Practical Guide (Chapman & Hall/CRC The R Series)
Part of:Chapman & Hall/CRC The R (53 Books) | by:Francisco Urdinez and Andres Cruz
Publisher Finelybook 出版社:Chapman and Hall/CRC; 1st edition (November 18,2020)
Language 语言:English
pages 页数:460 pages
ISBN-10 书号:0367818892
ISBN-13 书号:9780367818890

Book Description
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.

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