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
Print Length 页数: 460 pages
ISBN-10: 0367818892
ISBN-13: 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.
