Workshop series schedule

Topics: Introduction to R and R Studio. Packages. Working Directory. Dataframes and functions.

Registration: Sign-up Link.

Reference material

• Anderson, B. (2017). Week 1 Slides: Preliminaries in R. R Programming for Research Course.

• R Studio (2017). R Studio IDE Cheatsheet.

Code

• Anderson, B. (2017). Week 1: Preliminaries in R. R Programming for Research Course.

• Grolemund G., & Wickham, H. (2017). Workflow Basics. R for Data Science.

• Grolemund G., & Wickham, H. (2017). Workflow Scripts. R for Data Science.

• Intro Twitter R Script. Zip File

• R Radiant Package for Statistics & Machine Learning.

Topics: Importing data (csv, Stata, SAS). Data wrangling, aggregation and filtering (dplyr).

Registration: Sign-up Link.

Reference material

• Anderson, B. (2017). Week 2 Slides: Entering & Cleaning Data. R Programming for Research Course.

• R Studio (2017). R Studio Data Import Cheatsheet.

• Zip File (Data, Code).

Code

• Grolemund G., & Wickham, H. (2017). Data Import. R for Data Science.

• Grolemund G., & Wickham, H. (2017). Data Transformation. R for Data Science.

Optional Readings

• R Studio (2017). R Studio Importing Data into R Webinar.

• Wickham, H. (2017). Data Structures. Advanced R.

• Beer in Charlotte (Twitter Tutorial) Blog Post

Topics: Overview of Tidyverse: tidyr, dplyr, ggplot2, readr, purrr

Registration: Sign-up Link.

Reference material

• R Studio (2017). Tidyverse.

• Soltoff, B. (2017). Data transformation and exploratory data analysis slides. Computing for the Social Sciences

• R Studio (2017). R Studio Data Transformation Cheatsheet.

Code

• Grolemund G., & Wickham, H. (2017). Tidy Data. R for Data Science.

• Soltoff, B. (2017). dplyr in brief. Computing for the Social Sciences.

Optional Readings

• Bryan, J. (2017). purrr tutorial.

• Gillespie G., & Lovelace, R. (2017). Efficient data carpentry.. Efficient R programming.

Topics: R Markdown and Bookdown. Introduction to Git

Registration: Sign-up Link.

Reference material

• Anderson, B. (2017). Week 5 Slides: Reproducible Research. R Programming for Research Course.

• Zip File (Data, Code).

• Bryan, J. (2017). STAT545 Course 1 Slides.

• Bryan, J. (2017). Happy with Git and GitHub for the useR.

Optional Readings

• Grolemund G., & Wickham, H. (2017). RMarkdown. R for Data Science.

• Grolemund G., & Wickham, H. (2017). RMarkdown Formats. R for Data Science.

• R Studio (2016). RMarkdown Cheatsheet.

• RStudio RMarkdown Lessons.

• Wickham, H. (2015). Git and Github. R Packages.

Topics: Introduction to visualizations. ggplot2. Interactive visualizations using HTMLWidgets.

Reference material

• Zip File (Data, Code).

• R Studio. (2017) R Studio HTML Widgets.

• R Studio. (2017) R Studio Shiny.

• R Studio. (2017) R Studio Data Visualization Cheatsheet.

Code

• Grolemund G., & Wickham, H. (2017). Data Visualization (ggplot2). R for Data Science.

• Grolemund G., & Wickham, H. (2017). Exploratory Data Analysis. R for Data Science.

• HTML Widgets: plotly, leaflet, streamgraph, visNetwork.

Topics: Define API’s. Twitter’s Public API (REST and Stream). Facebook API.

Reference material

• Kearney, M. (2017). rtweet . GitHub repository.

• Barberá, P. (2017). Rfacebook . GitHub repository.

• Zip File (Data, Code). Slides.

Optional Readings

• Barberá, P. (2017). Scraping the Web . POIR 613: Measurement Models and Statistical Computing.

• Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. arXiv preprint arXiv:1403.7400.

• Morstatter, F., & Liu, H. (2017). Discovering, assessing, and mitigating data bias in social media. Online Social Networks and Media, 1, 1-13.

Topics: Basic concepts in social network analysis. Centrality measures. Network visualization.

Reference material

• Barberá, P. (2017). Intro to Social Network . POIR 613: Measurement Models and Statistical Computing.

• Denny, M. (2014). Social Network Analysis . Institute for Social Science Research .

• Ognyanova, K. (2017). Network visualization with R. POLNET 2017 Workshop, Columbus, OH.

• Zip File (Data, Code).

Optional

• Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … & Jebara, T. (2009). Life in the network: the coming age of computational social science. Science, 323(5915), 721-3.

• McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415-444.

Code

• Barberá, P. (2017). Introduction to social network analysis with R. POIR 613: Measurement Models and Statistical Computing.

• Barberá, P. (2017). Social network analysis with R: Descriptive analysis. POIR 613: Measurement Models and Statistical Computing.

• Interactive Networks (visNetwork).

• Retweet & Reply Interactive Networks with visNetwork.

Topics: Predictive versus explanatory analysis. Evaluation: Holdout/Cross Validation. Supervised and unsupervised machine learning. Regularization.

Reference material

• Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87-106.

• Grimmer, J. (2015). We are all social scientists now: how big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80-83.

• Athey, S., & Imbens, G. (2015). Machine Learning Methods for Causal Effects.

• Barberá, P. (2017). Supervised Machine Learning . POIR 613: Measurement Models and Statistical Computing.

Code

• Supervised Machine Learning: caret tutorial

• Amunategui, M. (2014). Modeling 101 - Predicting Binary Outcomes with R, gbm, glmnet, and {caret}

• Zip File (Data, Code).

Topics: tidytext. Bag of words. Word frequencies, TF-IDF and Zipf’s Law. Dictionary-based methods.

Reference material

• Rothenberg, M. (2017) Emoji Tracker.

• University of Vermont Complex Systems Center (2017) Hedonometer.

• Silge, J. & Robinson, D. (2017) Text Mining with R.(Zip File).

Optional Readings

• Young, L., & Soroka, S. (2012). Affective news: The automated coding of sentiment in political texts. Political Communication, 29(2), 205-231.

• Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology, 29(1), 24-54.

Topics: Text as Data. Supervised (Text Classification) and Unsupervised Models (Topic Models, Word Scaling and Word Embedding)

Reference material

• Benoit, K. et al. (2017) Getting Started with quanteda.

• Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.

• Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), 267-297.

Optional Readings

• Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder‐Luis, J., Gadarian, S. K., … & Rand, D. G. (2014). Structural Topic Models for Open‐Ended Survey Responses. American Journal of Political Science, 58(4), 1064-1082.

• Lowe, W. (2008). Understanding wordscores. Political Analysis, 16(4), 356-371.

• Gentzkow, M., Kelly, B. T., & Taddy, M. (2017). Text as Data, Working paper.

• Denny, M. J., & Spirling, A. (2017). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Working paper, New York University.

• Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Code

• Topic models (Zip File)

• Text Classification (Zip File)