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.

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.

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.

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).

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.

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.

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.

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.

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.

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

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.