Tom Houslay
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tomhouslay.bsky.social
Tom Houslay
@tomhouslay.bsky.social
120 followers 330 following 7 posts
Former academic researcher in evolution & animal behaviour.
Now: data & biology stuff for industry [human milk, infant nutrition, microbiome, healthy ageing...]
Also: dad, wildlife pond enthusiast, ND
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Reposted by Tom Houslay
ivelasq3.bsky.social
I wrote a lil post on the amazing work that
@ginareynolds.bsky.social does championing ggplot2 extension developers and teaching others to build their own!

The post features the Scrollytelling Quarto extension and the group's cute #RStats hex 🐱:

rworks.dev/posts/ggplot...
An Introduction to Writing Your Own ggplot2 Geoms – R Works
The ggextenders club provides inspiration and resources for those venturing into the exciting world of creating custom ggplot2 extensions.
rworks.dev
November 3, 2025 at 3:22 PM
Reposted by Tom Houslay
andrew.heiss.phd
Wrote up a little intervention post/explanation for my class about why using LLMs for trying to learn programming (as first time learners!) is bad and detrimental datavizf25.classes.andrewheiss.com/news/2025-11...
In the first week of class, I had you read an article about how ChatGPT is philosophical “bullshit”, as well as this other post about AI and LLMs in this class. I’ve seen a surprising increase in the amount of ChatGPT-based weekly check-ins and graph interpretations in the exercises. (In the past, I’ve even had students tell me they PDFs of the slides into ChatGPT to generate interesting and confusing things.) Don’t do this! LLMs cannot find three things that you personally found interesting from the readings. LLMs are not you. It’s a huge waste of time for all of us to for me to spend time evaluating and grading output from ChatGPT. I want to read what you think and do, not what a computer invents. NoteMy personal stance on AI and LLMs Many of you have asked about how I think about and use LLMs in my own work (and in the class materials). Please check this short little page where I explain what I do with text, images, code, research, and learning. In short, I do not use LLMs to create any of the materials for this class, and I do not use them to grade or evaluate any of your work. I’ve even had several students submit completely made up stuff for mini projects and final projects. In your final project, your job is to take a dataset, make three different plots with it, and combine the plots into one graphic that tells some sort of story. I’ve (really truly) received projects like this: (a ChatGPT-generated plot of completely made up data from gapminder) At a very quick glance, that looks like a plausible image. But holy crap it falls apart fast, both comically and tragically. Comically because, like, look at the y-axis in the scatterplot; or the x-axis ticks in the population grown chart that are sometimes spaced every 20 years, sometimes every 10 years, and has 1980 there twice; or the weird blobs in the map. Tragically—and most importantly—literally nothing in this image is real and none of the annotations make sense and none of the colors make sense and everything here is completely meaningless and devoid of any information. It’s pure philosophical bullshit—an image “produced without concern for its truth.” That’s what I mean when I say that it’s a forgetful and unreliable resource. Lots of that code is helpful as an illustration of what’s going on. Lots of the explanation is good and fine. But it subtly changed its mind halfway through and didn’t realize it (because it can’t realize it! it’s just a chain of statistically likely text!) Hopefully you can see where this is dangerous for learning. It can often “teach” you wrong, and you can only recognize that if you know stuff already. It’s extra dangerous if you then take that example output, paste it into your exercise, and submit it. I’m not even concerned about the plagiarism aspect of that—code is code and there are only a few ways to write {dplyr} code that will do this, and it’s easy to adapt from real human-based examples online, so I’m less worried about stealing other people’s work here. I’m concerned that it’s not real data. It made up values of 1, 2, 3, and 4 for some countries to illustrate grouping and summarizing. That’s good and normal. None of that is real. Don’t treat it as real.
November 2, 2025 at 10:17 PM
Reposted by Tom Houslay
absolutely-not.bsky.social
i looked at the methodology for this and it is
a. sex addiction counseling group in texas did a surveymonkey and extrapolated the results to the entire us population which is the sort of research design that earns you an ff on an intro methods class (the extra f is for extra effort), and
b. p-hacked
the-independent.com The Independent @the-independent.com · Oct 2
Nearly a third of Americans have had a ‘romantic relationship’ with an AI bot, new survey says
1 in 3 Americans have had a ‘romantic relationship’ with an AI bot, new survey says
www.independent.co.uk
October 3, 2025 at 2:15 AM
Reposted by Tom Houslay
cghlewis.bsky.social
If colleagues or students share a dataset with you and you make this face, consider sharing these resources with them. ☺️

datamgmtinedresearch.com

1. Organizing data (Ch. 3)
2. Naming variables and files (Ch. 9)
3. Documenting data (Ch. 8)
4. Cleaning data (Ch. 14)
a man is covering his mouth with his hands and the word schitts creek is on the bottom right
ALT: a man is covering his mouth with his hands and the word schitts creek is on the bottom right
media.tenor.com
October 1, 2025 at 1:20 PM
Reposted by Tom Houslay
vincentab.bsky.social
{tinytable} 0.14.0 for #RStats makes it super easy to draw tables in html, tex, docx, typ, md & png.

There are only a few functions to learn, but don't be fooled! Small 📦s can still be powerful.

Check out the new gallery page for fun case studies.

vincentarelbundock.github.io/tinytable/vi...
a table about lemurs a table about students and schools a table about wines
September 29, 2025 at 12:44 PM
tomhouslay.bsky.social
Useful resource! Good to see ordered beta regression in the mix as I've found that useful recently, although the note on it is linked to a Ben Bolker twitter comment that is now gone as his profile is deleted...
tomjwebb.bsky.social tomjwebb @tomjwebb.bsky.social · Oct 1
Useful overview of R packages / functions for modelling different kinds of response variables (continuous, proportional, counts, bounded, zero-inflated, etc.) in a regression framework, with mixed models and Bayesian options too: strengejacke.github.io/regressionmo...
Overview of R Modelling Packages
Overview of modelling strategies and packages. Which model do I need for my data?
strengejacke.github.io
October 1, 2025 at 4:05 PM
Reposted by Tom Houslay
jbengler.de
Thank you for citing #tidyplots 🙏

Jakub Idkowiak et al. Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data. Nature Communications (2025).

doi.org/10.1038/s414...

#rstats #dataviz #phd
Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data - Nature Communications
Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.
doi.org
October 1, 2025 at 3:05 PM
Reposted by Tom Houslay
dingdingpeng.the100.ci
Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models). Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve. A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
August 25, 2025 at 11:49 AM
Reposted by Tom Houslay
jsmartin.bsky.social
My first solo author paper is now available in early view at Methods in Ecology and Evolution! I develop a covariance reaction norm (CRN) model for estimating continuous, multivariate, and nonlinear environmental effects on G and P matrices.

besjournals.onlinelibrary.wiley.com/doi/10.1111/...
Covariance reaction norms: A flexible method for estimating complex environmental effects on trait (co)variances
Estimating quantitative genetic and phenotypic (co)variances is crucial for investigating evolutionary ecological phenomena such as developmental integration, life history trade-offs and niche spe...
besjournals.onlinelibrary.wiley.com
August 11, 2025 at 1:25 PM
tomhouslay.bsky.social
I got some good answers from my question about how to get back up to date with the world of #rstats
ivelasq3.bsky.social Isabella Velásquez @ivelasq3.bsky.social · Jul 14
There are some great #RStats starter packs. You can follow all or some of the folks in them.

- bsky.app/starter-pack...
- bsky.app/starter-pack...
- bsky.app/starter-pack...
- bsky.app/starter-pack...
- bsky.app/starter-pack...

I work at Posit, here is our starter pack:

- bsky.app/starter-pack...
July 15, 2025 at 9:58 AM
tomhouslay.bsky.social
Stupid question alert but here we go anyway: I long-ago deleted my twitter account, I cannot cope with linkedin, but I need to get back into knowing what's going on in #rstats world. Anyone recommend specific feeds / people / other stuff to drag myself back into it? (or do I just trawl rstats)
July 14, 2025 at 1:54 PM