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Jon Harmon (he/him/his)

@jonthegeek.com
6.4K followers 1.1K following 914 posts

🗣️#RStats #DataScience #Dogs @dslc.io Executive Director #TidyTuesday poster 🔗http://linkedin.com/in/jonthegeek 🔗http://github.com/jonthegeek

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Jon Harmon (he/him/his) @jonthegeek.com · 05/10/2026
@dslc.io welcomes you to week 40 of #TidyTuesday! We're exploring Avocado Oil Authenticity! 📂 tidytues.day/2026/2026-10-06 📰 www.ucdavis.edu/food/news/avocado-o… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.PCA biplot of fatty acid profiles from oils extracted from processed foods. Samples classified as consistent with authentic avocado oil cluster together on the right, clearly separated from inconsistent samples that cluster near vegetable oil comparators on the left, demonstrating that chemical fingerprinting can reliably distinguish authentic avocado oil from adulterated products.
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Jon Harmon (he/him/his) @jonthegeek.com · 02/10/2026
I haven't figured out how this fits the metaphor quite yet, but BTW... You can use agentic ai to help prepare you to use agentic ai! But you have to guide it carefully and review, and it will probably often make sense to just do it yourself. Oh, hey, there it is!
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Jon Harmon (he/him/his) @jonthegeek.com · 02/10/2026
If you're adding agentic AI to a project, do some work up front to make sure it's ready for AI. Make sure the existing code has the style and design that you want. And please please please make sure your code is covered by tests (so you and the agent can tell if it's turning into the Red Skull).
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Jon Harmon (he/him/his) @jonthegeek.com · 02/10/2026
Seeing the difference in how the same agentic AI models in the same IDE perform differently on different projects, it hit me that AI is like the super soldier serum in the MCU: "The serum amplifies everything that is inside, so good becomes great; bad becomes worse."
Stanely Tucci as Dr. Erskine, inventor of the super soldier serum, from Captain America: The First Avenger. He is in a barracks, preparing Steve Rogers mentally for the procedure.
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Jon Harmon (he/him/his) @jonthegeek.com · 28/09/2026
@dslc.io welcomes you to week 39 of #TidyTuesday! We're exploring Health metrics in urban centres worldwide! 📂 tidytues.day/2026/2026-09-29 📰 human-settlement.emergency.copernic… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Exterior of a modern hospital building at dusk, with a brick and glass facade lit from within, a colorful sculpture of a child holding balloons on the wall, young trees and a landscaped walkway in front, and a U.S. flag near the entrance. Photo by Acton Crawford on Unsplash, https://unsplash.com/photos/brown-and-white-concrete-building-near-green-trees-under-blue-sky-during-daytime-8PB_TFEy2XQ
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Jon Harmon (he/him/his) @jonthegeek.com · 28/09/2026
I have 5 728-day-old hydroponic tomato plants which I use more for meditation than cultivation. 1 of them (closest in the photo) is barely holding on. I'm trimming the one next to it fairly hard to get the poor little guy some light, but it only has a few small leaves left to try to absorb it 😢
An Aerogarden hydroponic system with 5 tomato plants. The plant closest to the viewer is mostly bare of leaves.
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Jon Harmon (he/him/his) @jonthegeek.com · 23/09/2026
@dslc.io welcomes you to week 38 of #TidyTuesday! We're exploring Average share of green areas across cities! 📁 tidytues.day/2026/2026-09-22 📰 data.unhabitat.org/pages/open-space… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A map of the world, with each country colored in shades of yellow-green by the average share of urban area that is green, by country in 2020. Portugal, Belize, and Fiji appear to be the most green at about 25-30% green in urban areas.
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Jon Harmon (he/him/his) @jonthegeek.com · 16/09/2026
Ahhh, no more looping through The Design of Everyday Things at 2x to prep for my talk! It's a good book, but it definitely getting to be a bit annoying 🙃 #PositConf2026
The audio book "the design of everyday things" in the audible player, set at 2x playback speed. Currently 39 minutes into the book, with 5 hours remaining at 2x.
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Jon Harmon (he/him/his) @jonthegeek.com · 16/09/2026
See ya at #PositConf2027, back in Seattle! #PositConf2026
Posit conf 2027 in Seattle September 13-15
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Jon Harmon (he/him/his) @jonthegeek.com · 16/09/2026
Dammit my stbl hex stickers that I meant to mention in my talk and make available in the room are still in my pocket. Grab me after the closing if you want one! They look like this #PositConf2026 #RShiny
A stack of hex stickers for the stbl R package. They look like the name of the package carved into stone.
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Jon Harmon (he/him/his) @jonthegeek.com · 14/09/2026
@dslc.io welcomes you to week 37 of #TidyTuesday! We're exploring Dead Sea Scrolls Manuscripts! 📂 tidytues.day/2026/2026-09-15 📰 www.deadseascrolls.org.il #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Bar chart showing manuscript copy counts by biblical book, with deuterocanonical books highlighted in orange alongside protocanonical books in blue.
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Jon Harmon (he/him/his) @jonthegeek.com · 11/09/2026
My old leaky dog is off to the boarder this week so my spouse can go to the office while I'm @ #PositConf2026, and Dante is very upset that he didn't get to go to "camp". He's whining and watching the door for the boarder to come back for him. I love that they enjoy running around at her place!
A great dane watching something off screen with his ears "at attention" as he listens for someone to come to the door for him.
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Jon Harmon (he/him/his) @jonthegeek.com · 08/09/2026
Date yourself with a snap of your first ever computer. en.wikipedia.org/wiki/BASIC_P...
A tv with strange characters and an Atari 2600 with the Basic Programming cartridge and weird controller.
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Jon Harmon (he/him/his) @jonthegeek.com · 07/09/2026
@dslc.io welcomes you to week 36 of #TidyTuesday! We're exploring The Cappuccino Index! 📁 tidytues.day/2026/2026-09-08 🗞️ www.youtube.com/watch?v=WtlE3BW9Nqs #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Coffee beans.
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Jon Harmon (he/him/his) @jonthegeek.com · 31/08/2026
@dslc.io welcomes you to week 35 of #TidyTuesday! We're exploring World Castles, Fortresses and Palaces! 📁 tidytues.day/2026/2026-09-01 📰 thecastlemap.com/data #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Photograph of the Alhambra in Granada, Spain, seen from across the valley in low evening sunlight. The long sandstone complex runs across the frame on a wooded ridge, its walls and square crenellated towers lit warm orange against a pale blue sky. A tall keep with battlements stands left of centre and the boxy Renaissance facade of the Palace of Charles the Fifth sits to the right, with dark green woodland and cypresses filling the slope below. The Alhambra is ranked seventh of the 5,793 landmarks in this dataset, with articles in 95 Wikipedia language editions.
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Jon Harmon (he/him/his) @jonthegeek.com · 30/08/2026
An airplane with red dots indicating where returning planes had bullet holes
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Jon Harmon (he/him/his) @jonthegeek.com · 24/08/2026
@dslc.io welcomes you to week 34 of #TidyTuesday! We're exploring Country Music Lyrics! 📂 tidytues.day/2026/2026-08-25 📰 www.youtube.com/watch?v=48ZxNFGJTo8 #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A multi-series line chart titled 'Comparison of Popular Terms', created by Dana Gibbon, tracks the usage frequency of 20 lyrical words across songs from 2014 through 2019, measured as a percentage of songs on the y-axis ranging from 0% to over 60%. The word 'wanna' sits at the top of the chart for nearly the entire period, peaking sharply around 70% in 2018, followed closely by high-frequency terms like 'girl' and 'yeah' which hover between 35% and 50%. In contrast, terms like 'drink', 'drinkin', and 'lovin' stay consistently near the bottom of the chart below 15%, while middle-tier terms such as 'tonight', 'night', and 'little' show moderate year-to-year shifts within the 20% to 50% range.
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Jon Harmon (he/him/his) @jonthegeek.com · 17/08/2026
@dslc.io welcomes you to week 33 of #TidyTuesday! We're exploring IELTS exam results! 📂 tidytues.day/2026/2026-08-18 📰 sojourningscholar.com/ielts-score-s… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Plot showing distribution of IELTS scores by native language ordered by median score. German, Marathi, Russian and Afrikaans have a higher median score than English. The distributions are also different. The most frequent score for Germans and Russians is 8, while the most frequent score for English-speakers is 6.
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Jon Harmon (he/him/his) @jonthegeek.com · 10/08/2026
When @jameshwade.bsky.social mentioned hex stickers at our last #PositConf2026 speaker training, I realized I didn't have any for stbl.wrangle.zone, the #RStats 📦 I'm speaking about! Thankfully I had time to order a small batch. The url is unreadable, I'll have to fix that if I order another batch.
Hex logo stickers for the R package stbl, featuring the letters stbl seemingly engraved in stone.
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Jon Harmon (he/him/his) @jonthegeek.com · 10/08/2026
@dslc.io welcomes you to week 32 of #TidyTuesday! We're exploring the Palomar Spectroscopic Survey of Nearby Galaxies! 📁 tidytues.day/2026/2026-08-11 📰 arxiv.org/abs/astro-ph/9704108 #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A scatter plot showing the BPT diagnostic diagram for 408 nearby galaxies observed with the Hale Telescope. The x-axis is the log ratio of nitrogen to hydrogen emission lines and the y-axis is the log ratio of oxygen to hydrogen lines. Points are colored by nuclear activity type: blue for star-forming H II regions clustered at lower left, red for Seyfert nuclei at upper right, orange for LINERs at right, and green for transition objects between the groups.
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Jon Harmon (he/him/his) @jonthegeek.com · 03/08/2026
@dslc.io welcomes you to week 31 of #TidyTuesday! We're exploring Basotho Wool! 📁 tidytues.day/2026/2026-08-04 📰 groundup.org.za/article/the-mountai… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A horizontal bar chart titled 'Global Basotho Wool Imports (2023)' displaying the total trade primary value in USD across four importing countries from Lesotho. The countries are listed vertically on the y-axis, ordered from largest value at the top to smallest at the bottom: South Africa ($38M), China ($24M), Uruguay ($215K), and India ($141K). Each country is colored with a unique solid bar, and the exact formatted dollar amounts are labeled in bold next to the right edge of each bar. The x-axis representing total import value is clean with no line or tick marks, letting the data labels communicate the values. A descriptive text caption below notes that the visualization utilizes mirror trade statistics based on UN Comtrade primary value reporting.
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Jon Harmon (he/him/his) @jonthegeek.com · 27/07/2026
@dslc.io welcomes you to week 30 of #TidyTuesday! We're exploring Ecotourism! 📁 tidytues.day/2026/2026-07-28 📰 vahdatjavad.github.io/ecotourism/ar… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Simple map of Australia with red dots indicating sightings of Manta rays. Most of the dots are close to the Eastern coast, with a few dots along the North West coast and some further out into the ocean.
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Jon Harmon (he/him/his) @jonthegeek.com · 20/07/2026
@dslc.io welcomes you to week 29 of #TidyTuesday! We're exploring Near-Death Experiences (NDERF)! 📂 tidytues.day/2026/2026-07-21 📰 nderf.org #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A dumbbell chart titled 'Changed Forever' comparing life changes 8 years after cardiac arrest between NDE experiencers (purple dots) and controls who did not have an NDE (gray dots). NDE experiencers show dramatically higher rates of becoming more loving/empathetic (73% vs 41%), decreased fear of death (47% vs 16%), and increased belief in afterlife (42% vs 16%).
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Jon Harmon (he/him/his) @jonthegeek.com · 13/07/2026
@dslc.io welcomes you to week 28 of #TidyTuesday! We're exploring Many penguins! 📂 tidytues.day/2026/2026-07-14 📰 dx.doi.org/10.1111/ele.13898 #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A montage of the 18 penguin species included in the data set, arranged alphabetically from A. forsteri (top left) to S. mendiculus (bottom right). The images vary; most show a single individual in a terrestrial habitat, but some show multiple individuals or family groups, or show birds at sea.A phylogenetic layout of images of the 18 penguin species included in the data set, arranged according to a radial phylogenetic layout. The images vary; most show a single individual in a terrestrial habitat, but some show multiple individuals or family groups, or show birds at sea.
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Jon Harmon (he/him/his) @jonthegeek.com · 06/07/2026
@dslc.io welcomes you to week 27 of #TidyTuesday! We're exploring UFC Athletes and Fight Data! 📂 tidytues.day/2026/2026-07-07 📰 github.com/benyamindsmith/fightr #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Banner for the 'fightr' R package, featuring a hexagonal logo with a fighter silhouette and the R logo, next to the stylized text 'fightr' against a dark chain-link background.
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Jon Harmon (he/him/his) @jonthegeek.com · 29/06/2026
@dslc.io welcomes you to week 26 of #TidyTuesday! We're exploring Wreck Inventory of Ireland! 📁 tidytues.day/2026/2026-06-30 🗞️ www.archaeology.ie/advice-and-suppo… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Map of Ireland showing the locations of recorded shipwrecks. Each grey circle represents a wreck site, with a very high concentration around the Irish coastline and relatively few points in the open Atlantic to the west.
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Jon Harmon (he/him/his) @jonthegeek.com · 22/06/2026
@dslc.io welcomes you to week 25 of #TidyTuesday! We're exploring Papal Encyclicals: Industrial Revolution vs. AI Revolution! 📂 tidytues.day/2026/2026-06-23 #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A two-panel visualization comparing Pope Leo XIII's Rerum Novarum (1891) with Pope Leo XIV's Magnifica Humanitas (2026). The top panel shows thematic density (mentions per 1,000 words) across six categories including Dignity & Rights, Common Good, and Technology & Power. The bottom panel shows a sentiment arc tracking net positive/negative tone through each document. Rerum Novarum is shown in brown and Magnifica Humanitas in navy blue.
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Jon Harmon (he/him/his) @jonthegeek.com · 15/06/2026
@dslc.io welcomes you to week 24 of #TidyTuesday! We're exploring UK Baby Names! 📁 tidytues.day/2026/2026-06-16 🗞️ www.nrscotland.gov.uk/publications/… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Line chart showing the rise of girls' names in the Scotland top 100 rankings over time. Freya climbs steadily from around 90th place in the early 2000s to become the top-ranked name by 2025, overtaking other popular names including Isla, Olivia, Amelia and Grace.
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Jon Harmon (he/him/his) @jonthegeek.com · 08/06/2026
@dslc.io welcomes you to week 23 of #TidyTuesday! We're exploring Films Based on Video Games! 📂 tidytues.day/2026/2026-06-09 🗞️ en.wikipedia.org/wiki/List_of_films… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A ranked list of the top 10 highest-grossing video game film adaptations, with The Super Mario Bros. Movie at #1 with $1.36B, followed by Detective Pikachu at $450M and Warcraft at $439M.
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Jon Harmon (he/him/his) @jonthegeek.com · 05/06/2026
The logo for the httr2 package, with a batter (or "hitter") swinging at a baseball, thus revealing the package is pronounced "hitter 2".
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Jon Harmon (he/him/his) @jonthegeek.com · 01/06/2026
@dslc.io welcomes you to week 22 of #TidyTuesday! We're exploring European Parenting Leave Policies! 📁 tidytues.day/2026/2026-06-02 📰 eplp-dataset.org #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.An area chart with lines showing changes in average parenting leave regulations across 21 European countries between 1970 and 2024. A general increase in total paid leave is seen until around 1990 where it then flattens out. However, after 1990 an increasing proportion of the leave is protected leave for co-parents. Total protected leave increased until around 2000 before flattening off.
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Jon Harmon (he/him/his) @jonthegeek.com · 25/05/2026
@dslc.io welcomes you to week 21 of #TidyTuesday! We're exploring Sustainable Energy for All! 📁 tidytues.day/2026/2026-05-26 📰 energydata.info/dataset/dashboard/s… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A shaded area chart titled 'Wind Power Consumed by Population and Industry (%)' comparing wind energy consumption in Denmark, Ireland, and Norway from 1990 to 2010. The visualization uses an overlapping 'identity' position to show each country’s individual trajectory. Denmark (teal) shows an early and aggressive lead, peaking near 4% by 2010, while Ireland (light blue) demonstrates a significant surge starting around 2003. Norway (burnt orange) shows a more modest, steady climb that begins to level out after 2007. The plot illustrates the 'momentum' of the energy transition, inviting participants to explore similar shifts in renewables, energy access, and efficiency metrics across the full global dataset.
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Jon Harmon (he/him/his) @jonthegeek.com · 18/05/2026
@dslc.io welcomes you to week 20 of #TidyTuesday! We're exploring State of Crossref metadata by member country! 📂 tidytues.day/2026/2026-05-19 🗞️ doi.org/10.64000/7s70g-drz77 #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.An infographic depicting the continuous lifecycle of research outputs. The center consists of various entities including books, blogs, conference materials, articles, organizations, preprints, videos, images, theses, software, data, protocols, contributors, and other objects. Surrounding this core are five interconnected stages: Fund (fund, provide facilities, give award, fund again); Create (propose, generate data, author, revise, and write/produce); Post (edit, publish, deposit, check similarity, share, link, maintain metadata, and archive); Respond (comment, cite, mention, review, correct, and retract); and Use (verify, reuse, reproduce, translate, and refute).
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Jon Harmon (he/him/his) @jonthegeek.com · 11/05/2026
@dslc.io welcomes you to week 19 of #TidyTuesday! We're exploring Twinned Cities! 📂 tidytues.day/2026/2026-05-12 🗞️ bothness.github.io/twin-cities #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A screenshot of the twin cities explorer showing a minimalist world map. On the map points represent some cities, with lines connecting the different cities indicating a link between them. The highlighted cities include Hamburg, Marseille, Tunis, Paris, and Dubai. Links from those cities are largely concentrated within Europe.
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Jon Harmon (he/him/his) @jonthegeek.com · 07/05/2026
I love that the definitions in this new MW all include a date of first known use (as defined in this dictionary). I knew it was in even the very old English dictionaries online, but now I want to also collect actual usage. 1613, eh? 😁
computer \kəm-ˈpyü-tər\ n : one that computes; specif: a programmable usu. electronic device that can store, retrieve, and process data <using a ~ to design 3-D models> -- computerdom \kəm-ˈpyü-tər-dəm\ n -- computerless \kəm-ˈpyü-tər-ləs\ adj -- computerlike \kəm-ˈpyü-tər-ˌlīk\ adj (1613)
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Jon Harmon (he/him/his) @jonthegeek.com · 07/05/2026
The old definitions all talk about a person, usually defined somewhere in the vicinity of an accountant. I like that here "calculator" is similarly used to obviously refer to "one who calculates". The definitions in the 60s-80s are probably my faves, as we tried to figure things out.
com-pu-ter, s. [Eng. comput(e); - er.] One who computes or reckons; a calculator, accountant, or reckoner. (Brown.)
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Jon Harmon (he/him/his) @jonthegeek.com · 07/05/2026
I collect definitions of the word "computer", bound together with definitions of other words. My current oldest is from the 4-volume Universal Dictionary of the English Language (1897), newest came today in @merriam-webster.com 's 12th Collegiate Dictionary (2025)
2 dictionaries, one old, one brand new. The old is the Universal Dictionary of the English Language, Volume 1, A to CRE. The new is Merriam-Webster's Collegiate Dictionary, 12th edition.
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Jon Harmon (he/him/his) @jonthegeek.com · 07/05/2026
@merriam-webster.com the podcast worked! Somehow I missed that this came out. Can we get more new episodes???
A brand new copy of Merriam-Webster's Collegiate Dictionary, 12th edition. A red cloth cover with gold and white lettering. Since 1828.
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Jon Harmon (he/him/his) @jonthegeek.com · 04/05/2026
@dslc.io welcomes you to week 18 of #TidyTuesday! We're exploring Italian industrial production! 📂 tidytues.day/2026/2026-05-05 🗞️ seriestoriche.istat.it/fileadmin/do… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A line chart titled Italian beer production increased dramatically between 1950 and 1980, showing Italian beer production between 1871 and 1985 in million hectolitres per year. The line is quite flat initially around 1 million hectolitres per year, until around 1950 onwards where it increases dramatically, reaching about 10 million hectolitres per year in 1985.
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Jon Harmon (he/him/his) @jonthegeek.com · 27/04/2026
@dslc.io welcomes you to week 17 of #TidyTuesday! We're exploring US Agricultural Tariffs! 📂 tidytues.day/2026/2026-04-28 🗞️ ers.usda.gov/sites/default/files/_l… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A four-panel line chart showing Most Favored Nation tariff rates by HTS section from 2000 to 2024. Each panel displays all four sections as thin grey background lines for comparison, with the focal section highlighted in a bold colored line matching its panel title. Panel I (orange, top-left) shows Live Animals & Animal Products with the highest rates, starting around 11% in 2000, dropping to about 9% in 2012, and stabilizing near 9.5% through 2024. Panel II (green, top-right) shows Vegetable Products declining from 8.5% in 2000 to about 6.5% in 2012, then climbing back to 7.5% by 2024. Panel III (pink, bottom-left) shows Animal/Vegetable Fats & Oils with the lowest rates, around 6-6.5%, also dropping in 2012 to about 5.5% before increasing back near 6% in 2020. Panel IV (blue, bottom-right) shows Prepared Foodstuffs remaining relatively stable around 7.5-8% throughout the period, with a similar 2012 drop. The title reads MFN tariff rates are highest for Live Animals & Animal Products with the section name in matching orange color. All panels share a common y-axis scale showing average tariff rates as percentages between 5% and 11.5%, and x-axis marks every four years for 2000 through 2024.
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Jon Harmon (he/him/his) @jonthegeek.com · 20/04/2026
@dslc.io welcomes you to week 16 of #TidyTuesday! We're exploring Global Health Spending! 📂 tidytues.day/2026/2026-04-21 📰 data.one.org/analysis/out-of-pocket… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A collage of four charts exploring global health expenditure using data from the Global Health Expenditure Database (GHED). Top left: a Voronoi treemap of total global health care spending broken down by country, dominated by high income countries. Top right: a small-multiples line chart showing out-of-pocket payments as a share of health spending are declining across all income groups, but remain high in many countries. Bottom left: a bubble chart showing many African countries fall short of the $86 per capita and 5% of GDP health spending targets. Bottom right: a scatter plot with LOESS curves showing curative spending rises with income while preventive spending gets squeezed.
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Jon Harmon (he/him/his) @jonthegeek.com · 13/04/2026
@dslc.io welcomes you to week 15 of #TidyTuesday! We're exploring Bird Sightings at Sea! 📁 tidytues.day/2026/2026-04-14 🗞️ obis.org/dataset/29ea15ed-8f76-40ca… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Screenshot of an interactive visualization summarizing bird sightings. At top, a zoomed out world map displays record density with thousands of observations intensely clustered in specific grid cells within the Southern Ocean. At bottom left, a temporal histogram starts with about 1000 records in 1969, no data for 1970 to 1974, then counts that peak at over 3000 records from 1984 to 1987, quickly trailing off and ending in 1990. At bottom right, a nested taxonomic sunburst chart shows that all records are within the Phylum Chordata and Class Aves. The vast majority of records are within Order Procellariiformes and Family Procellariidae, with representation from other Procellariiformes Families (primarily Diomedeidae) and other Orders: Charadriiformes (Family Laridae) and Pelecaniformes (Family Sulidae).
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Jon Harmon (he/him/his) @jonthegeek.com · 06/04/2026
@dslc.io welcomes you to week 14 of #TidyTuesday! We're exploring Repair Cafes Worldwide! 📂 tidytues.day/2026/2026-04-07 🗞️ insideclimatenews.org/news/11112025… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Infographic titled 'Can you fix it?' showing repair success rates (0–100%) for the 100 most common items brought to Repair Cafés between 2015 and 2025. A horizontal dot plot grouped by product category. Non-electric items (like knives, scissors, and bikes) have the highest success rates, with knives and scissors at 98%. Electric appliances, tools, TVs, printers, and phones have lower and more varied success rates. Overall success rate: 62% across 136,000 repairs. Plot credit: Yanika Borg (https://www.linkedin.com/in/yanikaborg/)
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Jon Harmon (he/him/his) @jonthegeek.com · 30/03/2026
@dslc.io welcomes you to week 13 of #TidyTuesday! We're exploring Coastal Ocean Temperature by Depth! 📂 tidytues.day/2026/2026-03-31 📰 data.novascotia.ca/stories/s/a25g-p… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A photo of the ocean from the shore. Small waves are breaking on a rocky beach in the foreground. The sun is shining from behind a few clouds on the horizon in an otherwise clear blue sky.
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Jon Harmon (he/him/his) @jonthegeek.com · 23/03/2026
@dslc.io welcomes you to week 12 of #TidyTuesday! We're exploring One Million Digits of Pi! 📂 tidytues.day/2026/2026-03-24 📰 www.jpl.nasa.gov/edu/news/how-many-… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A collection of gauge charts each representing one digit from 0 to 9. Each gauge shows the probability that a digit is immediately followed by itself in the first million digits of pi. Arc length encodes the self-transition rate, with a white line marking the expected 10.00% baseline. The arc appears to range from 9% to 11%. All ten digits fall within 0.20 percentage points of the expected rate, supporting the conjecture that pi is a normal number.
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Jon Harmon (he/him/his) @jonthegeek.com · 16/03/2026
@dslc.io welcomes you to week 11 of #TidyTuesday! We're exploring Salmonid Mortality Data! 📁 tidytues.day/2026/2026-03-17 📰 www.vetinst.no/arrangementer/lanser… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Dozens of salmon swim in netted cage. Photo: "Rudolf Svensen"
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Jon Harmon (he/him/his) @jonthegeek.com · 09/03/2026
@dslc.io welcomes you to week 10 of #TidyTuesday! We're exploring How likely is 'likely'?! 📂 tidytues.day/2026/2026-03-10 📰 adamkucharski.github.io/CAPphrase #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A chart from github.com/adamkucharski showing the distribution of estimated probabilities for different phrases, ranked by mean. The y-axis is labeled with the individual phrases, and the x-axis shows the probabily percent from 0 to 100%. Each point is an individual response, with the mean for each phrase shown as a hollow red circle, and the median for each phrase shown as a red diamond. 'Will Happen' is top with a median 100% and mean 98% probability, and 'Almost No Chance' is bottom with a median 2% and mean about 3.5% probability. 'Realistic Possibility', 'May Happen', 'Might Happen', and 'Could Happen' each have points spanning roughly the entire range from 0% to 100%.
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Jon Harmon (he/him/his) @jonthegeek.com · 02/03/2026
@dslc.io welcomes you to week 9 of #TidyTuesday! We're exploring Golem Grad Tortoise Data! 📁 tidytues.day/2026/2026-03-03 📰 onlinelibrary.wiley.com/doi/10.1111… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A photograph of a Hermann's tortoise, featuring a severe injury in her shell after a fall from 20 to 30 m high cliffs. She stands on rocky cliffs, with treetops in the background.
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Jon Harmon (he/him/his) @jonthegeek.com · 23/02/2026
@dslc.io welcomes you to week 8 of #TidyTuesday! We're exploring Science Foundation Ireland Grants Commitments! 📁 tidytues.day/2026/2026-02-24 📰 data.gov.ie/dataset/science-foundat… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.A line plot showing the amount of funding (in € millions) Science Foundation Ireland contributed to Higher Education Authorities in Ireland from 2000 to 2025. A map of Ireland is overlayed on the plot as the background. Funding starts a little below €100m in 2000, peaks above €400m in 2020, and drops to €0 in 2025 after Science Foundation Ireland was dissolved and merged with the Irish Research Council to form Taighde Éireann - Research Ireland.
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Jon Harmon (he/him/his) @jonthegeek.com · 16/02/2026
@dslc.io welcomes you to week 7 of #TidyTuesday! We're exploring Agricultural Production Statistics in New Zealand! 📁 tidytues.day/2026/2026-02-17 🗞️ www.rnz.co.nz/news/country/560252/g… #RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

 How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Line chart of New Zealand's sheep-to-people ratio from 1950 to 2023. The ratio peaks at about 22 sheep per person in 1981 ("peak sheep") and then steadily declines, falling below 5 in 2022 and reaching 4.6 in 2023. Image credit: "https://sherwood.news/world/new-zealands-sheep-to-people-ratio-fell-again-in-2023/"
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