Here you’ll find workshop slides and similar. They are roughly in order of how recently they’ve been given (or will be). Some are not so much workshops as talks without any expectation of hands-on exercises or similar. Check out the schedule to see times/dates for these as well as other CSCAR events (we give a lot of workshops!).
If you’re looking for the notes for a recent workshop I’ve given, you’ll find it here.
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.
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.
Data Processing and Visualization in R
Focus is on common data processing and exploration techniques, especially as a prelude to visualization. Part of the focus will be on dplyr and tidyverse, which enhance and facilitate the sorts of operations that typically arise when dealing with data, including faster I/O and grouped operations. For visualization, the focus will be on using ggplot2 and other packages that allow for interactivity. Exercises may be found in the document as well (including Jupyter notebooks of the same demonstrations in Python).
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 in at least a year or so. The content is still likely useful, but until 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.
Possible future topics
An afternoon workshop I hope to develop further at some point.