<i>Introduction to R for Social Scientists: A Tidy Programming Approach</i> introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stack
Introduction to R for Social Scientists: A Tidy Programming Approach (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
โ Scribed by Ryan Kennedy, Philip D. Waggoner
- Publisher
- Chapman and Hall/CRC
- Year
- 2021
- Tongue
- English
- Leaves
- 209
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology.
Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.
ย
โฆ Table of Contents
Contents
Preface
1 Introduction
2 Foundations
3 Data Management and Manipulation
4 Visualizing Your Data
5 Essential Programming
6 Exploratory Data Analysis
7 Essential Statistical Modeling
8 Parting Thoughts
Bibliography
Index
๐ SIMILAR VOLUMES
<p><span>An Introduction to the Rasch Model with Examples in R</span><span> offers a clear, comprehensive introduction to the Rasch model along with practical examples in the free, open-source software R.</span></p><p><span>It is accessible for readers without a background in psychometrics or statis
<p>Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. <i>Linear Regression Models: Applications in R</i> provides you with a comprehensive
<span><p>Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. <i>Linear Regression Models: Applications in R</i> provides you with a comprehe
<p><strong>Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach</strong> is aimed at statisticians and quantitative social, economic and public health students and researchers who work with small-area spatial and spatial-temporal data. It assumes a grounding in statistical theory up to t
Dr. Scott Lynch has made a great job for those (like me) who want a clear introduction to the methods of bayesian data analysis. I hold a Ph.D. in plant breeding, and as many others, I was trained in the traditional frequentist approach for the analysis of experiments: linear regression, ANOVA and u