"Researchers found that walkable city design—not personal motivation—was the key factor behind people taking 1,100 more steps per day."
www.scientificamerican.com/article/movi...
📣 NEW! I’ve just released the BIGGEST and perhaps most creative project I’ve ever worked on!
“Searching for Birds” searchingforbirds.visualcinnamon.com 🐤
A project, an article, an exploration that dives into the data that connects humans with birds, by looking at how we search for birds.
New Year, New Colour Tool
for you data visualizers and maybe the odd designer
obumbratta.com/colour
R code and output, showing the creation of a methods narrative from a log that was created behind the scenes. R code: bigrams_joyce |> narrativize(format = "text", person = "we", return = "html") Output: First we retrieved a corpus from Project Gutenberg using ID numbers 2814, 4217, and 4300. Then we moved column `subsection` into body text. Next, we loaded texts by tokenizing words, converting to lowercase, and preserving paragraph breaks. We identified documents using the column `title`. Finally, we constructed bigram sequences from the text.
Screenshot of RStudio’s data viewer, with tidytext-style data showing one word per row and descriptive labels under key variable names. The example data shows James Joyce’s story “The Sisters.” The columns `doc_id` and `word` are unlabeled. `new_word` is labeled “new use of word.” `hapax_doc` is labeled “document singleton.” `hapax_corpus` is labeled “corpus singleton.” `vocabulary` is labeled “count of new words.” `ttr` is labeled “text-token ratio.”
Super excited to share the 0.6 update to my R package for text mining. Designed for students new to #RStats (and text mining), it’s nevertheless what I use in my own work. This update packs in a ton, but my favorite new features are methods logging and labels.
jmclawson.github.io/tmtyro/news/...
🚨🚨 🪶📊🎨 🚨🚨
Good news for all of you bird + data + art enthusiasts:
I've opened registration for THREE cohorts of Binoculars to Binomials starting in March.
One contemplative monthly cohort & two bi-weekly ones.
Guaranteed to change your 🧠!
www.jerthorp.me/learning
Map of water availability and water stress in South America. The map uses a bivariate color palette with dark blue indicating high water availability and demand, light blue/cyan indicating high water availability and low demand, gray showing low water availability and demand, and orange indicating low water availability with high water demand. Most of the Amazon is low water stress. Atlantic Brazil, the west coast bordering the Andes, and the pampas region have the most water stress.
Map of water availability and water demand in North America. The map uses a bivariate color palette with dark blue indicating high water availability and demand, light blue/cyan indicating high water availability and low demand, gray showing low water availability and demand, and orange indicating low water availability with high water demand. Most of the Amazon is low water stress. Mexico and the U.S. northern plains and southwest have the most water stress. The Pacific Northwest, Great Lakes region, and northeast/Atlantic regions have high demand and availability. Alaska and northern Canada have high availability and low demand, except for the Alaskan North slope with lower availability and higher demand.
Brilliant choice of bivariate color palette in this map of water stress from @wriclimate.bsky.social & ESRI.
I love how it draws attention to areas with high demand for water with saturated colors, & distinguishes low & high availability with a striking change in hue.
via @dantoearth.bsky.social
We’re excited to announce the winners of the 2025 Pudding Cup! Our entry pool was the strongest ever with close to 100 submissions.
Show our 3 winners some love 🫶 on this thread, and check out the full details and honorable mentions at the link:
pudding.cool/pudding-cup/
This is a timeline compares the dates and time when Donald Trump posted about various public officials with the dates those individuals reported receiving threats, bomb threats, or swatting attacks: Rep. Marjorie Taylor Greene (R-Ga.) — Trump post: Nov. 14; threats reported: Nov. 15. State Sen. Greg Goode (R-Ind.) — Post: Nov. 16; swatting and threats hours after Trump posted. Rep. Chris Deluzio (D-Pa.) — Post: Nov. 20; bomb threat: Nov. 21. Rep. Chrissy Houlahan (D-Pa.) — Post: Nov. 16; bomb threat: Nov. 21. Sen. Elissa Slotkin (D-Mich.) — Post: Nov. 20; bomb threat: same day, Nov. 20. Chuck Schumer — Post: Nov. 20; bomb threat: Dec. 1. There are more.
“In recent weeks, nearly two dozen elected officials on both sides of the aisle said they were targeted after getting caught in the president's crosshairs.”
I got to contribute to this @NBCNews.com piece by Dareh Gregorian and Jiachuan Wu:
www.nbcnews.com/politics/don...
I’m thrilled to share that my new book (Spatial Data Management with DuckDB) is now published! 🎉
At 430 pages, this book provides a practical, hands-on guide to scalable geospatial analytics and visualization using DuckDB. All code examples are open-source and freely available on GitHub.
SveltePlot website featuring a grid of example plots
SveltePlot 0.7 is out! Lots of new features since the last time I posted here: 🎉 jitter transform, 🐝 dodge transform (for beeswarm plots), 🎉 stackMarimekko, 📸 image mark, and most recently the 🧇 waffle mark! And the website now features a ton of examples.
➡️ svelteplot.dev
Quick thread on the BBC and the political and societal significance of recent developments:
One of the main reasons the UK has historically been so much less polarised than the US, is that Britain has a shared source of information, consumed and trusted by most people regardless of their politics.
Hey Bluesky! You can follow the @economist.com data team here if you want to see more of our analysis and charts in your feed go.bsky.app/BJSsLHw
Blue and red stripes showing temperature anomalies since 1850
Have you ever wanted to make your own climate warming stripes plot? It's super easy with #rstats! (code here gist.github.com/andrewheiss/... )
Example of using `filter_out()` on the `penguins` dataset, showing how it is much easier than `filter()`, especially with `NA`s
We are looking for #rstats community feedback on 3 new dplyr functions!
We're aiming to expand the `filter()` family:
- `filter()` to keep rows
- `filter_out()` to drop rows
- `when_any()` and `when_all()` as modifiers
Read more and leave feedback here:
github.com/tidyverse/ti...
Do you teach #rstats? Do your students complain about how lame and old-fashioned dplyr is? Don't worry: I have the solution for you: github.com/hadley/genzp....
genzplyr is dplyr, but bussin fr fr no cap.
😂😂
The Open Visualization Academy's newsletter is expanding! New contributor: Melissa Strong, who is also co-designing the OVA's website. Her first article in a series of 6: 'How Open Source Fuels the Future of Data Visualization (Part 1)
' openvisualizationacademy.beehiiv.com/p/how-open-s...
A scatter plot titled "Fair and square? Russian federal elections, 2000-21" which visualizes the results of Russian federal elections. The x-axis represents voter turnout in percentage, ranging from 0 to 100%. The y-axis represents the percentage of votes for Putin, Medvedev, or the United Russia party, also ranging from 0 to 100%. Each dot on the plot represents a polling station. A cluster of dots appears in the upper right corner, indicating high voter turnout and a high percentage of votes for the specified candidates. A dashed rectangle highlights a grid-like pattern of dots at numbers ending in zero and five, with an annotation suggesting this pattern indicates potential foul play. The source is cited as Kobak and Shpilkin (2021).
Now this is how you detect whether an election was stolen. Humans choose rounder numbers.
by @TheEconomist
#30DayMapChallenge - November’s 30DayMapChallenge is fast approaching - and the daily themes for this year’s event have been released! googlemapsmania.blogspot.com/2025/10/30-d...
Woot, just submitted another dataset to #TidyTuesday and now there are 5 PRs waiting for Jon to review.
I screenrecorded the process, so if you want to see how EASY it is to contribute a dataset to this AMAZING #rstats community, check out this video.
youtu.be/Kp7pyYwLcwc
I ruptured my achilles in January. Couch-bound for most of the winter, I read over a dozen books about walking journeys. I was inspired. Obsessed? I decided to walk across Massachusetts.
You decide how to experience my story here: pudding.cool/2025/10/walk/
Big news out of @climatecentral.org today! We've brought back NOAA's billion dollar disaster dataset!
Check out our interactive website and bookmark it because there's more to come!
www.climatecentral.org/climate-serv...
A treemap showing the world’s population by country, grouped by continent. Each rectangle’s area represents a country’s estimated 2023 population, with labels on the 10% most populous countries. Asia dominates with over 60% of the global population, followed by Africa, the Americas, Europe, and Oceania.
🌏 A compact snapshot on where people live around the globe (2023 data) — over 60% of #humans live in #Asia!
I originally created a more basic version for our new #ggplot2 [un]charted lesson on 🎨 "Color Choice" to discuss color accessibility:
👉 www.ggplot2-uncharted.com/module2/colo...
If you're trying to learn the birds in a place (either where you live or a travel spot), eBird has a quiz!
ebird.org/quiz/
You're also helping rate the quality of images.
I'm using this right now to brush up on the birds of Raja Ampat, where I'll be leading a trip in December.
New visual story: Bird migration is one of nature’s greatest spectacles — and scientists are uncovering extraordinary insights into how new threats are reshaping these epic journeys. Follow the remarkable travels of three birds as they fly across the planet. www.theguardian.com/environment/...
New R package: gridmappr by Roger Beecham
It automates creating small multiple gridmap layouts by optimally placing geographic points into grid cells (inspired by Jo Wood’s Observable notebooks).
👉 github.com/rogerbeecham...
#RStats #RSpatial #DataViz #GISchat
A graphic with the following text: “The correct way to scale up or down dimensions when using areas to show values: use a scale factor to multiply the square root of the value.” Below the text is an equation that reads: width equals square root of value times scale factor. Further below are two circles of varying sizes. One is labeled: ”Value: 10, Area: 1” and below it a line segment, corresponding to the circle’s diameter is labeled ”3.16 times 3.16 equals 10 millimeters”. The other is labeled: ”Value: 50, Area: 5 times 1” and the line segment below it is labeled ”7.07 times 3.16 equals 22.34 millimeters”.
📊 #Dataviz PSA: When using areas to depict numbers, make sure you are indeed scaling the *area*, and not the width/length/diameter of the circles (or other shapes). Because software such as Illustrator don’t let you specify the area directly, you need do a small calculation to get it right. 1/2
Thanks! Loved featuring your project on the site 🙌
Wondering if you can outsource your data viz work to ChatGPT? 📊
I tested out a few different generative AI tools, giving them prompts to visualise two different data sets. If you're interested in the results, you can read them here: nrennie.rbind.io/blog/gen-ai-...
#RStats #Python #DataViz #GenAI
BIG NEWS! We've updated the website of the Open Visualization Academy, where you can see all its contributors: openvisualizationacademy.org
This is the announcement in our newsletter: openvisualizationacademy.beehiiv.com/p/we-re-back...
#dataViz #infographics #dataJournalism #dataVis 📊
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