R For Political Data Science

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R For Political Data Science

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Author : Francisco Urdinez
language : en
Publisher: CRC Press
Release Date : 2020-11-17

R For Political Data Science written by Francisco Urdinez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-17 with Political Science categories.

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.

R For Political Data Science

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Author : Francisco Urdinez
language : en
Publisher: CRC Press
Release Date : 2020-11-18

R For Political Data Science written by Francisco Urdinez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-18 with Political Science categories.

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.

Introduction To Political Analysis In R

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Author : H. Whitt Kilburn
language : en
Publisher: CRC Press
Release Date : 2025-08-15

Introduction To Political Analysis In R written by H. Whitt Kilburn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-15 with Mathematics categories.

Introduction to Political Analysis in R is a comprehensive guide for students and researchers eager to delve into the intersection of data science, statistics, and political science. Aimed at equipping readers with the essential quantitative skills to analyze political data, the book bridges practical coding techniques in R with foundational statistical concepts, emphasizing real-world applications in politics. The text adopts a progressive structure, beginning with the basics of R and data manipulation before advancing to more complex topics such as data visualization, spatial analysis, text analysis, and modeling. Through accessible language and engaging examples—ranging from U.S. election forecasting to global development trends—it demystifies complex analytical methods. Each chapter integrates coding exercises and real-world datasets to reinforce learning, fostering independent data analysis skills. Designed for undergraduate political science majors, this book is also a valuable resource for anyone seeking to understand data-driven political analysis, whether for academic research, professional development, or personal curiosity. Key features include: Integrates data science and statistics with a political science focus, offering hands-on coding practice using the R programming language. Provides real-world datasets and step-by-step exercises, enabling students to directly apply concepts to political phenomena such as gerrymandering. Features a progressive chapter structure, progressing from foundational data handling to advanced methods like text analysis, spatial mapping, and linear modeling. Emphasizes accessible coding for beginners, fostering self-sufficiency in data analysis without requiring prior statistical expertise. Bridges theory and application with examples that engage students’ interest in politics while developing transferable analytical skills. H. Whitt Kilburn is Associate Professor of Political Science, Grand Valley State University, Allendale, Michigan.

Learning Microeconometrics With R

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Author : Christopher P. Adams
language : en
Publisher: CRC Press
Release Date : 2020-12-29

Learning Microeconometrics With R written by Christopher P. Adams and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-29 with Business & Economics categories.

This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor’s degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis. Key Features: Focuses on the assumptions underlying the algorithms rather than their statistical properties. Presents cutting-edge analysis of factor models and finite mixture models. Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately. Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems. Introduces R programming concepts throughout the book. Includes appendices that discuss some of the standard statistical concepts and R programming used in the book.

Javascript For R

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Author : John Coene
language : en
Publisher: CRC Press
Release Date : 2021-07-15

Javascript For R written by John Coene and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with Business & Economics categories.

Little known to many, R works just as well with JavaScript—this book delves into the various ways both languages can work together. The ultimate aim of this work is to put the reader at ease with inviting JavaScript in their data science workflow. In that respect the book is not teaching one JavaScript but rather we show how little JavaScript can greatly support and enhance R code. Therefore, the focus is on integrating external JavaScript libraries and no prior knowledge of JavaScript is required. Key Features: ● Easy to pick up. ● An entry way to learning JavaScript for R. ● Covers topics not covered anywhere else. ● Easy to follow along.

Behavior Analysis With Machine Learning Using R

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Author : Enrique Garcia Ceja
language : en
Publisher: CRC Press
Release Date : 2021-11-25

Behavior Analysis With Machine Learning Using R written by Enrique Garcia Ceja and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-25 with Psychology categories.

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

R For Conservation And Development Projects

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Author : Nathan Whitmore
language : en
Publisher: CRC Press
Release Date : 2020-12-21

R For Conservation And Development Projects written by Nathan Whitmore and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-21 with Computers categories.

This book is aimed at conservation and development practitioners who need to learn and use R in a part-time professional context. It gives people with a non-technical background a set of skills to graph, map, and model in R. It also provides background on data integration in project management and covers fundamental statistical concepts. The book aims to demystify R and give practitioners the confidence to use it. Key Features: • Viewing data science as part of a greater knowledge and decision making system • Foundation sections on inference, evidence, and data integration • Plain English explanations of R functions • Relatable examples which are typical of activities undertaken by conservation and development organisations in the developing world • Worked examples showing how data analysis can be incorporated into project reports

Data Driven Campaigning And Political Parties

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Author : Katharine Dommett
language : en
Publisher: Oxford University Press
Release Date : 2024

Data Driven Campaigning And Political Parties written by Katharine Dommett and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Political Science categories.

Challenging the often-hyperbolic claims that have been made around the use of data in election campaigns for voter manipulation and suppression, this book provides unrivalled evidence of how parties actually behave. It shows that data-driven campaigning practice is not inherently problematic or new, but neither is it uniform, rather systemic, regulatory and party level factors affecting the nature of campaigning. Providing detailed empirical examples from Australia, Canada, Germany, the UK and US, this book shows how parties campaign and explains why parties differ, thereby resetting prevailing understanding of the role of data in campaigns.

R Markdown Cookbook

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Author : Yihui Xie
language : en
Publisher: CRC Press
Release Date : 2020-10-21

R Markdown Cookbook written by Yihui Xie and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-21 with Business & Economics categories.

This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. Those new to R Markdown will appreciate the short, practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose ‘hidden’ features, support customization and demonstrate the many new and varied applications of the software. After reading this book users will learn how to: Enhance your R Markdown content with diagrams, citations, and dynamically generated text Streamline your workflow with child documents, code chunk references, and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python, D3, and more into your analysis

Modern Data Science With R

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Author : Benjamin S. Baumer
language : en
Publisher: CRC Press
Release Date : 2021-03-31

Modern Data Science With R written by Benjamin S. Baumer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Business & Economics categories.

From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.