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Jesse Onland

@jdonland.bsky.social
160 followers 324 following 225 posts

dataviz and statistics | jdonland.github.io | views are my own

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Jesse Onland @jdonland.bsky.social · 22/09/2026
Posit is an LLM propaganda outlet that happens to maintain some #rstats packages.
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Jesse Onland @jdonland.bsky.social · 22/09/2026
Maybe I'm missing something but isn't the result here a bit obvious? If the cost of search effort doesn't depend on location and your prior is non-degenerate, you could always have picked a prior that was denser around the true target location and found it at less cost. arxiv.org/html/2311.18...
arxiv.org
On the true detection probability of the uniformly optimal search plan
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Jesse Onland @jdonland.bsky.social · 16/09/2026
Mercator's projection was very useful in its time: a straight line on the map corresponds to a path of constant bearing on the globe. When navigation was done with a compass and astrolabe, this was crucial. He didn't make Africa and Greenland look the same size just for fun!
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Jesse Onland @jdonland.bsky.social · 16/09/2026
It's remarkable how quickly LLM token (slopbot store credit) usage efficiency has become the primary selling point of software packages.
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Jesse Onland @jdonland.bsky.social · 16/09/2026
Seeing Hadley suggest using LLMs to maintain the #TidyTuesday repo is a little depressing. It's an unpaid community project. If it's not worth doing by hand, why do it? Why not automate the whole thing, then? Let the bots make up datasets, maintain the repos, make the charts, and post about them.
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Jesse Onland @jdonland.bsky.social · 10/09/2026
300-character book review: "The Theory That Would Not Die" (2011) Brings its heroes and villains to life with charming short biographies, but mangles the statistical ideas. "Fisher considered a hypothesis significant if it was unlikely to have occurred by chance"? Hypotheses don't "occur" at all!
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Minnesota Aquatic Invasive Species Research Center @maisrc.bsky.social · 19/08/2026
MAISRC’s Quantitative Ecologist Alex Bajcz has created ggplotplus, an accessibility component for ggplot2, to produce graphs for posters, presentations, papers, and exploration. Especially helpful for ggplot2 users with limited time, energy, or experience. github.com/MAISRC/ggplo...
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Jesse Onland @jdonland.bsky.social · 11/08/2026
One day I'll remember that I want `fct_relevel()` and not `fct_reorder()`, but today is not that day. #tidyverse
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Jesse Onland @jdonland.bsky.social · 04/08/2026
Herbert Robbins (1915-2001) on the start of his career as a statistician: observing a physical Monte Carlo estimation of the number of bombs required to disable an airstrip.
Screenshot of text reading: "My real entry into statistics came during World War II. I was in the Navy, and I was not a researcher. I wasn't working on any mathematical problems. But I happened to overhear the conversation of some people concerned with bombing airstrips and military installations. They were having trouble figuring out how much bombing was needed to put an airstrip out of commission. You might drop lots of bombs, but they overlap, and the total area covered is not the sum of the areas of the individual bombs. They wanted to know what the effect of the overlapping was so as to determine the number of bombs to be dropped in order to have a high probability of knocking out say 75% of the target. They had not found anybody who could solve that probable mathematically, so they had taken the expedient of simply dropping poker chips on the floor, photographing the patterns, and then measuring the total area covered by the overlapping poker chips. It was what we would now call a simulation or Monte Carlo study, but it was physically Monte Carlo, not mathematical Monte Carlo. Anyway, this got me to thinking, so I wrote a little note and submitted it for publication in the Annals of Mathematical Statistics. It came to the attention of Harold Hotelling, and that started my career as a statistician."
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Jesse Onland @jdonland.bsky.social · 03/08/2026
Clever Hans and Koko would like a word.
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Jesse Onland @jdonland.bsky.social · 28/07/2026
I wondered what infrastructure and guidelines exist for #dataviz 📊 on @wikipedia.org, took a quick look, and now I regret it. A big jumble of half-baked, semi-official tools missing basic functionality, endless debate about whether pie charts are ever okay, no unified style guide.
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Jesse Onland @jdonland.bsky.social · 24/07/2026
Does anyone else's copy of @statmodeling.bsky.social, @avehtari.bsky.social and @rmcelreath.bsky.social's Bayesian Workflows have terrible colour registration? CRC is sending me a replacement copy, apparently.
A close-up photo of a bar chart showing misaligned colour printing.
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Jesse Onland @jdonland.bsky.social · 16/07/2026
The @ropensci.org website appears to be down. Alarming!
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Xan Gregg @xangregg.bsky.social · 05/07/2026
This fit didn't look like p=0.020 to me, so I extracted the data and checked. It looks like the Line+CI is for all the data, but the stats exclude the low outlier points. #dataviz jov.arvojournals.org/article.aspx...
Chart with regression line and 18 data points. The confidence interval looks wider than it should for the given p-value of 0.020.Chart with regression line and 18 data points. Same as fit as before but showing a p-value of 0.0677Chart with regression line and 18 data points, with one outlier point marked with an X and excluded from the fit which has a p-value of 0.020.
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Jesse Onland @jdonland.bsky.social · 06/07/2026
This bicycle gear ratio visualizer reminds me of nomograms, and of the train timetable visualizations attributed to E. J. Marey. 📊 sheldonbrown.com/gear-graph.h...
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Jesse Onland @jdonland.bsky.social · 02/07/2026
What a bizarre choice of visualization for a sequence of six values.
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Jesse Onland @jdonland.bsky.social · 24/06/2026
Can Posit please hand over the tidyverse to some other organization that doesn't put 95% of its effort into LLM stuff?
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Jesse Onland @jdonland.bsky.social · 17/06/2026
I never realized how full of heresy Casella & Berger is. They think non-differentiable cumulative distribution functions represent pathological random variables not worthy of consideration!
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Jesse Onland @jdonland.bsky.social · 08/06/2026
Is looping LLM 📊 output through a prompt asking it to do a better job really better (cheaper? faster?) than just doing a good job oneself? www.goodeyelabs.com/insights/the...
goodeyelabs.com
The Tufte Test: Teaching an AI Agent to Make Better Data Visualizations
I encoded Tufte's data visualization principles as a quality standard in Truesight, deployed it as an API, and pointed an AI agent at a real dataset. One instruction: keep improving until you pass.
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Jesse Onland @jdonland.bsky.social · 07/06/2026
Say what you will about Stephen Wolfram and his research programme, but you have to admit that the visualizations are cool. writings.stephenwolfram.com/2026/06/game...
writings.stephenwolfram.com
Games between Programs: The Ruliology of Competition
Stephen Wolfram applies ruliological methods to competition, considering all possibilities in looking for a winning strategy. Does competition lead to complexity or simplicity?
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Jesse Onland @jdonland.bsky.social · 16/05/2026
@flowingdata.com ought not to characterize keeping one's money under a mattress as having "favourable odds [of a positive return]". On the contrary, one is virtually certain to lose money, in real terms, by doing this. flowingdata.com/2026/05/14/m...
flowingdata.com
More people losing money in prediction markets
For the Washington Post, Jeremy B. Merrill and Leslie Shapiro visualized users who won and lost money on Polymarket. The odds aren’t great. A lot of users aren’t that good at predicting…
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Voilà: Francis Gagnon @chezvoila.com · 21/04/2026
Nice reminder and visual representation of the limitations of radar charts, by Flourish 📊 Source: flourish.studio/blog/create-...
Two radar charts side by side. On the left, the nine values are in descending order, forming a type of snail shell figure. On the right, the nine values are in random order, forming an explosion-like shape.

These charts have the same values – just in a different order


Two radar charts side by side, illustrating how the ordering of axes in a radar chart dramatically changes the perceived shape — even when the underlying data is identical. Both charts show scores for Jane Doe (blue) and John Doe (pink) across nine dimensions labelled A through I, with values ranging from 0 to 100.
Jane Doe's chart shows a compact, roughly rounded shape sitting mostly in the upper half of the radar, suggesting her high scores are clustered around adjacent axes in this arrangement.
John Doe's chart shows a jagged, star-like shape with sharp spikes extending outward in several directions and deep indentations between them, creating a visually fragmented appearance.
The key insight of this chart is that both shapes represent exactly the same set of values — only the order of the axes differs. This demonstrates a well-known limitation of radar charts: the visual shape is highly sensitive to axis arrangement, which means two identical datasets can look completely different depending on how the axes are ordered. Readers should focus on individual axis values rather than overall shape when interpreting radar charts.
Left: Jane Doe
Right: John Doe
Created with the Radar chart template
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Michael Friendly @datavisfriendly.bsky.social · 28/03/2026
📊 #OTD 🎂🎂🎂 Happy Minard Day! Charles Joseph Minard's, b. Mar 27, 1781, his big 245th. #OTD #dataviz One way to celebrate is to visit @infowetrust.com wonderful Visual Catalog of the Work of Charles Joseph Minard, bit.ly/3lOXsbR Another is my celebration of Minard Day, 2021 ed, bit.ly/49f7k1t
bit.ly
Minard Day 2021: Resources, Research and Inspirations
Michael Friendly
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Voilà: Francis Gagnon @chezvoila.com · 28/03/2026
A dozen year ago, I was alone at home and decided to treat myself to a beer and a detailed exploration of the famous Minard map. I wrote this blog post, which is still one of the most popular on our website all these years later. 📊 chezvoila.com/blog/minard-...
chezvoila.com
13 facts you didn't know about the Minard map - Voilà:
Source: Wikipedia. The map representing the Russian campaign of the French army in 1812 is a true celebrity of the data visualization world, mostly known for its spectacular features. Lines show the n...
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Jesse Onland @jdonland.bsky.social · 25/03/2026
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Per Engzell @pengzell.bsky.social · 11/03/2026
How far can you get in 60 minutes? European cities offer vastly larger areas reachable by public transit than US cities of comparable population size. Source: lconwell.github.io/lucasconwell...
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Jesse Onland @jdonland.bsky.social · 14/03/2026
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Jesse Onland @jdonland.bsky.social · 10/03/2026
See also: Blank Space by David W. Marx, which argues that aesthetic fragmentation and other forces have resulted in 21st century cultural stagnation.
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Jesse Onland @jdonland.bsky.social · 08/03/2026
Why do people mention which slop bot made something for them? Brand loyalty? Why not say nothing and let people think you used your own knowledge and abilities?
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Jesse Onland @jdonland.bsky.social · 06/03/2026
Posit is so embarrassing.
posit.co
Introducing AI in RStudio
Today we’re introducing AI in RStudio. We’ve embedded a specialized agent directly into RStudio so it can read your live session context and provide more accurate assistance for data science and analy...
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Jesse Onland @jdonland.bsky.social · 05/03/2026
What is with all the "📊 for kids" books lately? Is it just slop bots driving the cost of illustration to zero? Do parents really buy these to read to their kids? Is it a résumé padding exercise for the authors?
Thumbnail from a review of a "dataviz for kids" book.
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Jesse Onland @jdonland.bsky.social · 24/02/2026
"Nothing is lost by adding the second graph." Sure, except for a consistent scale. 📊
Juxtaposed horizontal bar graphs using slightly different scales.
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Jesse Onland @jdonland.bsky.social · 23/02/2026
The StatsCan website is baffling. Every dataviz product is meticulously documented with authorship, update cadence, underlying datasets, keywords, related outputs... everything but a link to the actual viz! 📊
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Jesse Onland @jdonland.bsky.social · 14/02/2026
I wonder whether the categorization of farmers into "large" and "small" in the data used for this visualization 📊 of income inequality in 19th c. France amounts to conditioning on the outcome. Or maybe it's by acreage? freerangestats.info/blog/2026/02...
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Jesse Onland @jdonland.bsky.social · 13/02/2026
From the first and second editions of @rmcelreath.bsky.social's Statistical Rethinking. 📊
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Jesse Onland @jdonland.bsky.social · 07/02/2026
Am I wrong to think that overlapping bars is just straightforwardly an error? Now area (and thus visual impact) is no longer proportional to the data. Maybe I'm too VDQI-pilled? #dataviz 📊 (Chart from SWD: Before & After)
A chart with overlapped bars.
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Jesse Onland @jdonland.bsky.social · 07/02/2026
Well, @newyorker.com... is this "re-signed" or "resigned"? Why leave this at a line break? You'll use the dieresis but not the double hyphen?
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Jesse Onland @jdonland.bsky.social · 21/01/2026
Chapter 6 of Casella & Berger is a real page-turner! It's a bit funny to read them introduce the likelihood principle and credible intervals while carefully avoiding the term "Bayesian".
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Jesse Onland @jdonland.bsky.social · 02/01/2026
"There is a strong relationship, but it isn't statistically significant." This frequentism stuff has absolutely cooked people's brains.
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Jesse Onland @jdonland.bsky.social · 30/12/2025
At first I thought I'd like this better with filled, triangular arrowheads, but upon further reflection I think this style makes it more obvious where the data points are. Is there an unconscious tendency to imagine the data point in the centre of a filled arrowhead instead of at the tip?
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Jesse Onland @jdonland.bsky.social · 29/12/2025
@allendowney.bsky.social on Hempel's raven paradox. I like the heatmaps visualizing the joint posterior distributions in each scenario. www.allendowney.com/blog/2025/12...
allendowney.com
The Raven Paradox - Probably Overthinking It
Suppose you are not sure whether all ravens are black. If you see a white raven, that clearly refutes the hypothesis. And if you see a black raven, that supports the hypothesis in the sense that it in...
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Jesse Onland @jdonland.bsky.social · 20/12/2025
The archives of the journal Visible Language, which has published interesting articles on #dataviz 📊 in addition to typography, calligraphy, spelling reform, and many other interesting things, are available for free online: journals.uc.edu/index.php/vl...
journals.uc.edu
Archives | Visible Language
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Jesse Onland @jdonland.bsky.social · 03/12/2025
Can you guess which basic #dataviz errors this graphic features? Why in the world wouldn't the colour mapping be the same across the four donuts? And why not a 100% stacked bar chart instead of donuts? Just like movies about making movies, data graphics about making data graphics are never good.
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Evan Peck @peck.phd · 11/11/2025
I'm not proud to tell you that I clicked this Wirecutter article fully believing it was about the 📊 Best Bar Charts.
Screen-shot of wirecutter page with a big titel that says "The Best Bar Carts"
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Vinni Ott @vlott.bsky.social · 29/08/2025
New paper for anyone working with data: Better data viz - for free, in few clicks. Below, all N = 111, M = 0.04, SD = 0.27. One-sided t-tests vs. 0 yield: t(110) = 1.67, p = .049. Use raincloud plots. Or risk wrong conclusions! Plot w/ @jaspstats.bsky.social today! 🧪 📊 #PsychSciSky #StatsSky 🧵👇
Figure 2 from the linked article.Abstract of the linked article
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Jesse Onland @jdonland.bsky.social · 29/08/2025
Will Posit's recent transition to hyping slop generation endanger its B Corporation status? Surely you can't be serious about climate targets while shilling this stuff, right?
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Cédric Scherer @cedricscherer.com · 25/07/2025
Find the data 🧐🤓 #dataviz #datatoinkratio
One of many line graphs in the NASA Mariner-Venus Report from 1962. Due to the rather black and bold and many grid lines, the straight lines  that show the results (which are also black) are almost invisible.
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Jesse Onland @jdonland.bsky.social · 17/07/2025
Embarrassing. Why not just train a slop generator on all the conference submissions and then skip actually running it?
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Jesse Onland @jdonland.bsky.social · 17/07/2025
My struggles with Tableau 📊 seem to boil down to two issues: 1. Too little control over the aesthetic mapping. (E.g. cannot map to position.) 2. Dimensions and measures are treated radically differently (e.g by filtering) in ways that are unintuitive and/or just never what one would want.
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EarthScope Consortium @earthscope.org · 17/07/2025
Watch the seismic waves from today's magnitude 7.3 Alaska earthquake ripple across seismic stations in North America. More ➡️ loom.ly/kaJp5I0
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