This repo includes R code and materials from my Computational Social Science course at Rutgers University. The course includes theoretical discussion and hands-on training using R. It is a doctoral seminar offering a gentle introduction to computational methods both for people with some previous experience in coding, and for those who are just starting to learn. The course covers a variety of topics including introduction to R, analyzing survey data, using APIs, web scraping, network analysis, natural language processing, machine learning, online experiments, and ethics.
The repository includes the syllabys, R code, and data accompanying my 2023 course lectures.
R files include:
- Introduction to R (data formats, flow control, packages)
- Analyzing survey data (descriptives, recoding, GLM, weights)
- Working with APIs (Twitter, Reddit, Internet Archive, bibliometrics)
- Web scraping (rvest, xpath, pattern matching)
- Network analysis 1 (network data, network descriptives)
- Network analysis 2 (reciprocity, transitivity, homophily)
- Network analysis 3 (communities, permutation tests, QAP & netlm)
- Network analysis 4 (exponential random graph models)
- Data visualization (introduction to ggplot2)
- Text analysis 1 (preprocessing, term frequencies, sentiment)
- Text analysis 2 (n-grams, topic models)
- Machine learning (tidymodels, classification, regression)
Some of the recommended books for the course include:
- Salganik, M. J. (2017). Bit by Bit: Social Research in the Digital Age.
Available to read online or purchase on Amazon. - Wickham, H., & Grolemund, G. (2017). R for Data Science.
Available to read online or purchase on Amazon. - Long, J. D., & Teetor, P. (2019). R Cookbook, 2nd Edition.
Available to read online or purchase on Amazon. - Silge, J., & Robinson, D. (2017). Text Mining with R.
Available to read online or purchase on Amazon.