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:
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superseded by Practical Data Science, and will no longer be updated.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 ...".