iiinova — where your digital self keeps evolving

Reposted by Tom Houslay

Colin Carlson @colincarlson.bsky.social · 31/01/2026

This is Nathan Wolfe. Nathan is a virus hunter. Nathan has a bit of a reputation. Through the years, I've told multiple journalists stories I've heard, like "Nathan thanks Jeffrey Epstein in his book." Somehow no one ever wrote anything up. Want to see what Nathan was up to in today's Epstein files?

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Walty @waltometry.blacksky.app · 07/02/2026

I’m not arguing with no account defending AI. My grandmother lives in an area being environmentally compromised currently by an ai data center. I remember the color of the lake before they built it and the sludge I see in it when I go to see her. Shut the fuck up about “the benefits of AI” forever.

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Reposted by Tom Houslay

James Balamuta @coatless.bsky.social · 29/12/2025

{toggle} does one thing: adds a button to hide code output in #quarto docs. Took two versions to do that one thing well. Now it works everywhere... tabsets, callouts, nested containers, you name it. 📚 quarto.thecoatlessprofessor.com/toggle/ 🐙 github.com/coatless-qua...

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Reposted by Tom Houslay

Andrew Heiss @andrew.heiss.phd · 09/12/2025

Some closing thoughts for my students this semester on LLMs and learning #rstats datavizf25.classes.andrewheiss.com/news/2025-12...

Will you incorporate LLMs and AI prompting into the course in the future?
No.

Why won’t you incorporate LLMs and AI prompting into the course?
These tools are useful for coding (see this for my personal take on this).

However, they’re only useful if you know what you’re doing first. If you skip the learning-the-process-of-writing-code step and just copy/paste output from ChatGPT, you will not learn. You cannot learn. You cannot improve. You will not understand the code.

In that post, it warns that you cannot use it as a beginner:

…to use Databot effectively and safely, you still need the skills of a data scientist: background and domain knowledge, data analysis expertise, and coding ability.

There is no LLM-based shortcut to those skills. You cannot LLM your way into domain knowledge, data analysis expertise, or coding ability.

The only way to gain domain knowledge, data analysis expertise, and coding ability is to struggle. To get errors. To google those errors. To look over the documentation. To copy/paste your own code and adapt it for different purposes. To explore messy datasets. To struggle to clean those datasets. To spend an hour looking for a missing comma.

This isn’t a form of programming hazing, like “I had to walk to school uphill both ways in the snow and now you must too.” It’s the actual process of learning and growing and developing and improving. You’ve gotta struggle.

This Tumblr post puts it well (it’s about art specifically, but it applies to coding and data analysis too):

Contrary to popular belief the biggest beginner’s roadblock to art isn’t even technical skill it’s frustration tolerance, especially in the age of social media. It hurts and the frustration is endless but you must build the frustration tolerance equivalent to a roach’s capacity to survive a nuclear explosion. That’s how you build on the technical skill. Throw that “won’t even start because I’m afraid it won’t be perfect” shit out the window. Just do it. Just start. Good luck. (The original post has disappeared, but here’s a reblog.)

It’s hard, but struggling is the only way to learn anything.

You might not enjoy code as much as Williams does (or I do), but there’s still value in maintaining codings skills as you improve and learn more. You don’t want your skills to atrophy.

As I discuss here, when I do use LLMs for coding-related tasks, I purposely throw as much friction into the process as possible:

To avoid falling into over-reliance on LLM-assisted code help, I add as much friction into my workflow as possible. I only use GitHub Copilot and Claude in the browser, not through the chat sidebar in Positron or Visual Studio Code. I treat the code it generates like random answers from StackOverflow or blog posts and generally rewrite it completely. I disable the inline LLM-based auto complete in text editors. For routine tasks like generating {roxygen2} documentation scaffolding for functions, I use the {chores} package, which requires a bunch of pointing and clicking to use.

Even though I use Positron, I purposely do not use either Positron Assistant or Databot. I have them disabled.

So in the end, for pedagogical reasons, I don’t foresee me incorporating LLMs into this class. I’m pedagogically opposed to it. I’m facing all sorts of external pressure to do it, but I’m resisting.

You’ve got to learn first.

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Reposted by Tom Houslay

Jumping Rivers @jumpingrivers.com · 09/12/2025

If your Shiny app works with a mouse and looks fine on your screen, it may still be unusable for some users. Issues like missing alt text, ARIA misuse, or loose WCAG checks often surface too late. This Thursday’s free webinar shows how to catch them earlier. 🕐 13:00 UK time Register:

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Reposted by Tom Houslay

Peter Tennant @pwgtennant.bsky.social · 08/12/2025

Thrilled to share my latest paper entitled, "Estimating Discrimination in Sentencing: Distinguishing between Good and Bad Controls" Led by @jpinasanchez.bsky.social, the paper introduces a framework for examining discrimination in criminal justice processes. 🧵 1/10 publicera.kb.se/ejels/articl...

Screenshot of title page including the following abstract:

To minimise confounding bias and disentangle warranted from unwarranted disparities, researchers examining sentencing discrimination have traditionally sought to control for as many legal factors as possible. However, over the past decade, a growing number of scholars have questioned this strategy, noting that many legal factors are themselves subject to judicial discretion and that controlling for them can introduce post-treatment bias. Here, we use directed acyclic graphs (DAGs) to provide a formal and comprehensive assessment of the different types of bias that may arise from different choices of controls. In addition, we propose a new modelling framework to facilitate the selection of controls and reflect the model uncertainty created by the trade-off inherent in judicially-defined legal factors and other factors with a similar dual causal role. We apply this framework to examine race disparities in US federal courts and gender disparities in the England and Wales magistrates’ court. We find substantial model uncertainty for gender disparities and for race disparities affecting Hispanic offenders, rendering estimates of the latter inconclusive. Disparities against black offenders are more consistent and — under specific conditions — could be interpreted as evidence of direct discrimination.

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Tom Houslay @tomhouslay.bsky.social · 04/12/2025

Here are some mammals that like to pop out from under my office at night.

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Reposted by Tom Houslay

Isabella Velásquez @ivelasq3.bsky.social · 03/11/2025

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...

rworks.dev

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.

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Reposted by Tom Houslay

Andrew Heiss @andrew.heiss.phd · 02/11/2025

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.

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Reposted by Tom Houslay

doctor eek (alt decorative ghost season) @absolutely-not.bsky.social · 03/10/2025

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

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Reposted by Tom Houslay

Crystal Lewis @cghlewis.bsky.social · 01/10/2025

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)

media.tenor.com

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

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Reposted by Tom Houslay

Vincent Arel-Bundock @vincentab.bsky.social · 29/09/2025

{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...

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Tom Houslay @tomhouslay.bsky.social · 01/10/2025

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...

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Reposted by Tom Houslay

Julia M. Rohrer @dingdingpeng.the100.ci · 25/08/2025

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.

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Reposted by Tom Houslay

Jordan S. Martin @jsmartin.bsky.social · 11/08/2025

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/...

besjournals.onlinelibrary.wiley.com

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...

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Tom Houslay @tomhouslay.bsky.social · 15/07/2025

I got some good answers from my question about how to get back up to date with the world of #rstats

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Tom Houslay @tomhouslay.bsky.social · 14/07/2025

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)

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