R for Political Data Science

<p><strong>R for Political Data Science: A Practical Guide</strong> 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.</p><p>Key features:</p><ul> <p> </p> <li>Each chapter has the most up-to-date and simple option available for each task assuming minimal prerequisites and no previous experience in R</li> <p> </p> <li>Makes extensive use of the Tidyverse the group of packages that has revolutionized the use of R</li> <p> </p> <li>Provides a step-by-step guide that you can replicate using your own data</li> <p> </p> <li>Includes exercises in every chapter for course use or self-study</li> <p> </p> <li>Focuses on practical-based approaches to statistical inference rather than mathematical formulae</li> <p> </p> <li>Supplemented by an R package including all data</li> </ul><p>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. </p>