University of Kentucky

I’ve been teaching phonetics for the first time in a while, and I wanted to make some animations illustrating how sounds are pressure waves in propagating through the air. I worked how how to do this in a fairly tidy way, again leveraging the new stat_manual() from ggplot2, and gganimate.

Animating a Sound Wave

I had a lot of fun working with the XKCD color survey data, and I think I’ll keep messing around with it here and there in the future. But I also think I’ll want access to the full data. The tidytuesday data set was necessarily boiled down. The answers data frame contained just one hex code associated with one color label, not every color label given to every hex code in the survey.

Getting the xkcd color survey sqlite database

Note This is a version of the AI course policy I’m introducing this semester.

 🚫 When in doubt: No.

There is no aspect of this course for which it is recommended, appropriate, or acceptable to turn to a Large Language Model (more commonly called an AI) such as ChatGPT. This includes: Working on graded assignments, quizzes, or discussion prompts. As a supplement to the readings or course notes.

AI Policy

When I saw that the TidyTuesday dataset was the the XKCD color survey this week, I had to jump in! source(here::here("_defaults.R")) library(tidyverse) library(tidytuesdayR) library(tinytable) library(mgcv) library(marginaleffects) library(ggdist) library(ggdensity) library(geomtextpath) set.seed(2025-07-08) # eval: false # downloading &amp;

tidytuesday color survey

Yesterday I posted about the normalization functions in the tidynorm R package. In order to implement formant track normalization, I had to also put together code for working with the Discrete Cosine Transform (DCT), which in and of itself can be handy to work with.

Doing cool things with the Discrete Cosine Transform in tidynorm

Weinreich, Labov, and Herzog (1968) is a foundational text in my subfield of linguistics. Entitled “Empirical foundations for a theory of language change,” it’s both a comprehensive review of the field at the time, and a programmatic outlook for the future, laying down problems that researchers are still grappling with today.

In Remembrance

This might be a “everyone else already knew about this” thing, but I’ve finally gotten to a place of understanding about setting default colors scales for ggplot2, so I thought I’d share.

Setting default ggplot2 colors

As I scroll through my feeds, I often come across a really cool looking package, or a new feature of a package, that I think looks really cool, and then I forget to go back to really kick the tires to see how it works. So I’ve decided to try to set up a workflow where I send the docs or pkgdown pages for the package to a Trello board, and then come back maybe once a month and experiment with them in a blog post.

R Package Exploration (Jan 2023)

I’ve recently started playing Dungeons and Dragons, and have been really enjoying the campaign my sibling runs.

Simulating DND Rolls

This is part 2 of my blog posts to accompany my ADS2023 Poster. Saying Wooder As I said in the previous post, people seem to be very aware

 now

that pronouncing “water” as [wʊɾɚ] is a feature of Philadelphia English, but the surge in written recognition from 2000 onwards isn’t an accurate indicator of when people

 started

pronouncing it that way.

Rising Wooders: Part 2

In the Spring 2023 semester, I’m going to be teaching two R intensive courses: a statistics for linguists course, and an R for the Arts and Sciences course. For both, I’m going to have to do a “What is R” discussion during week 1, and given the breadth of tools I hope students come away with, I’ve been rethinking my usual answers.

What is R?

Following on from my post about darkmode in ggplot2 4.0, I wanted to also mess around with the new stat_manual() that’s available. And folks, it’s good! source(here::here("_defaults.R")) library(tidyverse) library(tidynorm) library(scales)  The announcement blog post says  Let’s put it to the test! Plotting sine waves   I’m teaching Phonetics this semester, so I’ve got sine waves on the mind.

Working with stat_manual()

Back in April, Quarto v1.7 introduced the ability to display multiple outputs from a single code chunk depending on whether or not the side is in dark mode. This was really exciting for me, because even though I prefer to keep sites in dark mode for personal use, I’ve found that in some presentation and teaching settings, the projector and lighting conditions make dark mode illegible.

ggplot2 4.0 and Dark Mode

I’m currently running a D&amp;D campaign, and in a recent session we ended with the plan for the party to interrogate a character they’d captured. We play every other week, which meant I had two whole weeks to figure out what exactly this character knew. I was worried about a few things though.

D&amp;D and Making Typst Figures

By default in a quarto document, the code and output look something like this: set.seed(2025) rnorm(10) [1] 0.62075674 0.03564140 0.77315448 1.27248909 0.37097543 -0.16285434 [7] 0.39711189 -0.07998932 -0.34496518 0.70215136 Maybe this is just me not wanting my peas to touch my mashed potatos, but I don’t like how

 close

the output is to the text of the document.

Custom Code Chunk css

I’ve been doing a

 lot

of python development recently, and really leaning into object oriented programming for my projects. For example, in the aligned-textgrid package, I started off by defining a class to represent intervals, which at their core have start and end times, and a label.

Experimenting with Object Oriented Programming in R

I’ve been working a lot with the Discrete Cosine Transform in python, specifically as it’s implemented in scipy. But, I really prefer doing my data munging and stats in R.

 1

What to do! I knew the answer rested in using the reticulate package, which lets you communicate back and forth between python and R, but I hadn’t appreciated

 how

cool reticulate was, which is why I’m making this blog post.

Working with the Discrete Cosine Transform in R

Upshot   I’ve moved a bunch of R defaults that I want for each post from .Rprofile into _defaults.R, and now run source(here::here("_defaults.R")) in each post where I want them. That looks like more work, but it actually makes things run a bit faster with the way Quarto runs R and freezes outputs.

Changing Project Defaults

Today in my stats class, my students saw me realize, in real-time, that you

 can

include random intercepts in poisson models that you couldn’t in ordinary gaussian models,

 and

this might be a nicer way to deal with overdispersion than moving to a negative binomial model.

Random Effects and Overdispersion

I’ve been playing a

 lot

of Baldur’s Gate 3 lately. My party has gotten to max level, and we’re running around, casting spells and taking names. One of the spells my wizard has is called

 Disintegrate

, which gets summarized like this


ImportantDisintegrate Shoot a thin green ray from your finger. If the spell reduces the target to 0 hit points, it disintegrates into a crumbly ash.

Rolling for Damage with the Central Limit Theorem

This summer, I’ve been spending some time getting more familiar with the nitty-gritty of Bayesian models, and I’m working on some modelling in Stan for logistic regression. Setting up the basic model formula in Stan for logistic regression is pretty straightforward: mu = intercept + slope * x; y ~ bernoulli_logit(mu);  And then you “just” need to set a prior for for the intercept and slope terms.

Getting a sense of priors for logistic regression

This semester I was teaching two R intensive courses: Quantitative Investigations in the Social Sciences and Quantitative Methods in Linguistics. For both courses, I had students maintain Quarto blogs that they pushed to Github Classroom and I thought I’d write up my thoughts on what worked, what didn’t work, and what I wish I’d planned out in advance.

Using Quarto Blogs for Teaching

One of the new functions in dplyr v1.1.0 is dplyr::consecutive_id(), which strikes me as having a few good use cases for linguistic data. The one I’ll illustrate here is for processing transcriptions.

A handy dplyr function for linguistics

I might flesh this out as a more detailed tutorial for LingMethodsHub, but for now this is going to be a rough-around-the-edges post about making spectrograms in R. My goal will be to get as close as possible to recreating a spectrogram like you might get from Praat. Pre-processing.

Making a spectrogram in R

The target audience   I’m writing this primarily for students I’ll be teaching in Spring 2023 who I want to use Git/Github with Posit Workbench. This tutorial is appropriate for:  Anyone using RStudio/RStudio Server/Posit Workbench/Posit Cloud I will assume: Git is already installed and available. You have not already configured Git locally. You cannot access the terminal.

Github Onboarding with RStudio

I’m a bit nervous about investing time into an interactive plotting framework after getting burned by Google Motion Charts.

 1

But, plotly seems to work even offline, which I think means once I’ve installed it, it doesn’t depend on a service or code hosted by the plotly company. That makes me feel a

 little

more confident. I’d like to build some animations in it, but that means learning how it works, so here I go!

Making a Plotly Plot

One of the courses I taught in the Fall 2022 semester was Natural Language Processing (NLP). It was a fun course to teach, and I learned a lot since it’s not a topic in my core areas of research. At the same time, the amount of work I put into it has made me really start to rethink how I prepare for teaching.

A lesson in over-preparing

This tidytuesday dataset of colors labels is like the perfect confluence of interests for me! I’ve started learning how to do digital art to illustrate characters for a D&amp;D campaign: Which means I’ve been looking a

 lot

at a color picker that uses Hue, Saturation and Lightness sliders (even though they’re not labelled that way). But I’ve had an interest in colors and color theory for a while.

Visualizing the Color Sphere

Recently, for class notes on probability/the central limit theorem, I wanted to recreate the table of 2d6 values that I made here. A really cool thing I found between that blog post and now is that gt has afmt_icon() operation that will replace text with its fontawesome icon.

Building up a complex {gt} table.

setupsource(here::here("_defaults.R")) library(tidyverse) library(brms) library(broom.mixed) library(ggdist) library(tidybayes) library(marginaleffects) library(tinytable) library(patchwork) library(faux) set.seed(2025-12-8) options(tinytable_html_mathjax = TRUE)  I’ve been thinking about Causal Inference, and have run up against a kind of confusing issue when it comes to “Composite Variables”, or variables deterministically created from the

Causal Confusion with Composite Variables

I’m not sure when I first came across his stuff, but I’ve really admired Andrew Heiss’ public scholarship, including his blog, which is full of handy stuff. At some point I noticed he had post-level DOIs minted. I knew you could mint repository-level DOIs with GitHub and Zenodo, but maintaining each post as a repository and adding them as submodules to a blog is… a lot.

Setting Up Rogue Scholar

I’m teaching a class on quantitative methods in linguistics, and after getting tired, over the years, of debugging everyone’s computer before we could get to the

 content

of learning R (much less quantitative methods), I decided to seize control. I’m running the course through GitHub Classroom which comes with an educational allowance for using GitHub codespaces.

Troubleshooting RStudio for GitHub Classrom and Codespaces

So now I can finally get to visualizing the effect of “light” and other modifiers on colors! When I eventually get to the plotly code, there’s nothing tidy going on, so I’ll be code-folding most of this stuff.

Light vs Dark 

I just saw a really interesting paper by Scott Nelson and Jeffrey Heinz (Nelson and Heinz 2025) that proposes a model of phonology and phonetics as complex function application that maintains a discrete phonology while also alowing for things like incomplete neutralization. I myself am always

 sort of

able to follow formal notation, but get a better understanding if I try rewriting it in a programming language of some sort.

Blueprint Model of Production, Pythonically

I’m still thinking about priors, distributions, and logistic regressions. The fact that a fairly broad normal distribution in logit space turns into a bimodal distribution in probability space has got me thinking about the standard deviation of random effects in logistic regression.

Thinking About Hierarchical Variance Parameters

I’m currently teaching a computational linguistics course, and for just the second time, I’m using Github Classroom to manage assignments. I might make a few posts about what I’ve learned in the process, but for now, I think I’ll focus on what’s gone right, and what’s been more challenging in using Github Codespaces. Github Codespaces   A Github Codespace is basically a virtual machine hosted by Github with VS Code as the default interface.

Some lessons in using Github Classroom and Codespaces

For me, teaching stats this semester has turned into a journey of discovering what the distributional and ggdist packages can do for me. The way I make illustrative figures will never be the same. So I thought I’d revisit my post about hierarchical variance priors, this time implementing the figures using these two packages.

Random effect priors, redo

Last semester, I spent time co-developing some python packages:  aligned-textgrid with Christian Brickhouse  fasttrackpy with Santiago Barreda  So, I thought I’d share a little walkthrough of a cool way to use them. They can both be installed with pip. bash pip install fastttrackpy pip install aligned-textgrid FastTrackPy   fasttrackpy (Fruehwald and Barreda 2023) is a python implementation of Santiago Barreda’s Praat plugin (Barreda 2021).

Using FastTrackPy and aligned-textgrid

The upshot The tidynorm package has convenience functions for normalizing Point measurements norm_barkz() norm_deltaF() norm_lobanov() norm_nearey() norm_wattfab() Formant Tracks norm_track_barkz() norm_track_deltaF() norm_track_lobanov() norm_track_nearey() norm_track_wattfab() DCT coefficients norm_dct_barkz() norm_dct_deltaF() norm_dct_lobanov() norm_dct_nearey() norm_dct_wattfab() As well as generic functions to implement your

Introducing tidynorm

I recently gave a talk about analyzing vowel formant trajectories using the Discrete Cosine Transform, and people have been asking me about when they could/should use a DCT based model versus a gamm. So, I’ll work through that question here!

Væl Space