While I probably won’t be giving a workshop for a while, here you’ll find past workshop slides and content. They are roughly in order of how recently they’ve been given. Some were not so much workshops as talks without any expectation of hands-on exercises or similar, so may have a bit less content or won’t be as useful without the context.

I did an R Series covering various topics, the content of which is all found here: Practical Data Science (more details about this document below). The intention was to cover five key topics: basic information processing, programming, modeling, visualization, and publication/presentation.

Other workshops include:

- Distill for R Markdown
- Exploratory Data Analysis Tools
- Mixed Models with R
- More Mixed Models
- Patchwork and gganimate
- Library Learning Analytics Workshop
- Getting More from RStudio
- Latent Variable Models
- Generalized Additive Models
- Mixed Models

These are the texts that serve as the basis for the workshops.

Practical Data Science

Focus is on common data science tools and techniques in R, including data processing, programming, modeling, visualization, and presentation of results. Exercises may be found in the document, and demonstrations of most content in Python is available via Jupyter notebooks.

Easy Bayes with rstanarm and brms

This workshop provides an overview of the rstanarm and brms packages. Basic modeling syntax is provided, as well as diagnostic checking, model comparison (posterior predictive checks , WAIC/LOO ), and how to get more from the models (marginal effects , posterior probabilities posterior probabilities, etc.).

Introduction to R Markdown

This workshop will introduce participants to the basics of R Markdown. After an introduction to concepts related to reproducible programming and research, demonstrations of standard markdown as well as overviews of different formats will be provided, including exercises. This document has been superseded by Practical Data Science, and will no longer be updated.

Factor Analysis and Related Methods

This workshop will expose participants to a variety of related techniques that might fall under the heading of ‘factor analysis’, latent variable modeling, dimension reduction and similar, such as principal components analysis, factor analysis, and measurement models, with possible exposure to and demonstration of latent Dirichlet allocation, mixture models, item response theory, and others. Brief overviews with examples of the more common techniques will be provided.

Text Analysis with R

This document covers a wide range of topics, including how to process text generally, and demonstrations of sentiment analysis, parts-of-speech tagging, and topic modeling. Exercises are provided for some topics. Some Python examples will also be added at some point.

Mixed Models with R

This workshop focuses on mixed effects models using R, covering basic random effects models (random intercepts and slopes) as well as extensions into generalized mixed models and discussion of realms beyond.

Structural Equation Modeling

This document regards a recent workshop given on structural equation modeling. It is conceptually based, and tries to generalize beyond the standard SEM treatment. The document should be useful to anyone interested in the techniques covered, though it is R-based, with special emphasis on the lavaan package.

These haven’t been given recently, but the content is still likely useful. Until they are updated or given again, you may also find a more fully fleshed out related work on the documents page.

My God, it’s full of STARs! Using astrology to get more from your data.

Talk on structured additive regression models, and generalized additive models in particular.

Become a Bayesian in 10 Minutes

This document regards a talk aimed at giving an introduction Bayesian modeling in R via the Stan programming language. It doesn’t assume too much statistically or any prior Bayesian experience. For those with such experience, they can quickly work with the code or packages discussed. I post them here because they exist and provide a quick overview, but you’d get more from the more extensive document.

Engaging the Web with R

Document regarding the use of R for web scraping, extracting data via an API, interactive web-based visualizations, and producing web-ready documents. It serves as an overview of ways one might start to use R for web-based activities as opposed to a hand-on approach.

Ceci n’est pas une %>%

Exploring your data with R. A workshop that introduces some newer modes of data wrangling within R, with an eye toward visualization. Focus on dplyr and magrittr packages.

Getting More from RStudio

An afternoon talk on how to use RStudio for more than just coding.

If you see mistakes or want to suggest changes, please create an issue on the source repository.

Text and figures are licensed under Creative Commons Attribution CC BY-SA 4.0. Source code is available at https://github.com//m-clark/m-clark.github.io, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".