Название: Introduction to R for Social Scientists Автор: Ryan Kennedy, Philip D. Waggoner Издательство: CRC Press Год: 2021 Страниц: 209 Язык: английский Формат: pdf (true) Размер: 10.1 MB
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.
1. Introduction Why R? Why This Book? Why the Tidyverse? What tools are needed? How This Book Can be Used in a Class Plan for the Book
2. Foundations Scripting with R Understanding R Working directories Setting Up an R Project Loading and Using Packages and Libraries Where to Get Help Moving Forward
3. Data Management and Manipulation Loading Our Data Data Wrangling Grouping and Summarizing Your Data (and Using “the Pipe”) Creating New Variables Combining Data sets Basic Descriptive Analysis Tidying a Data Set Saving Your Data Set for Later Use Saving Your Data Set Details for Presentation
4. Visualizing Your Data The Global Data Set The Data and Preliminaries Histograms Bar Plots Scatterplots Combining Multiple Plots Saving Your Plots Advanced Visualizations Parting Thoughts More Resources
5. Essential Programming Data Classes Data Structures Operators Conditional Logic User-Defined Functions Making your Code Modular Loops The map_*() Family from purrr Concluding Remarks
6. Exploratory Data Analysis Visual Exploration Numeric Exploration Putting it All Together: Skimming Data Concluding Remarks
7. Essential Statistical Modeling Loading and Inspecting the Data t-statistics Chi-square Test for Contingency Tables Correlation Ordinary Least Squares Regression Binary Response Models Parting Thoughts
8. Parting Thoughts Continuing to Learn with R Where To Go From Here Final Thought
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