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@karaman.is Sep 23, 2026, 3:55 PM

This week's #TidyTuesday is about urban green space from UN Habitat. Two in three of 1,112 cities lost green space since 1990. The largest gain was only 18 percentage points, while three cities lost more than 60.

Code: github.com/gkaramanis/t...

#RStats #dataviz

Line chart titled "Cities lose green space far faster than they gain it", in six small panels, one per region: Africa, Asia, Europe, Latin America & Caribbean, Northern America and Oceania. Each panel shows the change in the share of urban area covered by green space since 1990, in percentage points, for the three cities with the largest gains (blue) and the three with the largest losses (dark red) in that region. All lines start at zero in 1990 and end in 2020 or 2025, with the city name and country code at the end. Gains stay below +18 points everywhere, led by Stoke-on-Trent in the UK. Losses are much deeper in Africa, Asia and Latin America, where the worst cities fall by 40 to 63 points. Bissau, Kenema and Bo in West Africa lose the most. Losses in Europe, Northern America and Oceania stay between 13 and 25 points.

@dslc.io Sep 23, 2026, 1:48 PM

It's (the day after, sorry!) #TidyTuesday y'all! Show us what you made on our Slack at dslc.io!
#RStats #PyData #JuliaLang #RustLang #DataViz #DataScience #DataAnalytics #data #tidyverse #DataBS

@jonthegeek.com Sep 23, 2026, 1:42 PM

@dslc.io welcomes you to week 38 of #TidyTuesday! We're exploring Average share of green areas across cities!

📁 https://tidytues.day/2026/2026-09-22
📰 https://data.unhabitat.org/pages/open-spaces-and-green-areas

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

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.

@jonthegeek.com Sep 23, 2026, 1:09 PM

Doh! I just noticed that the #TidyTuesday poster actions were disabled! Some people definitely found the datasets, but I'm getting the posts out now...

@nrennie.bsky.social Sep 23, 2026, 8:44 AM

We're exploring green spaces for #TidyTuesday this week! 🌳

A simple small multiple bar chart showing changes in (some) UK cities over time 📊 Impressive increase from Stoke!

(Obviously I felt the need to style the bars as a mowed lawn pattern)

#RStats #DataViz #ggplot2

Small-multiple bar chart showing the average share of green area in 18 UK cities in 1990, 2000, 2010 and 2020. Stoke-on-Trent had the largest increase, from 8% in 2000 to 26% in 2020, while Dundee remained lowest, falling from 7% in 1990–2010 to 6% in 2020. Overall, green-space shares increased in several cities, while others were stable or declined.

@deepalikank.in Sep 23, 2026, 6:43 AM

#TidyTuesday week 38 - Average share of green areas across cities 🌳🌳
Four Indian cities, four different paths for urban green cover.
#DataVisualization #UrbanGreening #Rstats

Leaf pictograms compare urban green cover in Bengaluru, Mumbai, Pune, and Delhi across five years. Bengaluru rises by 2020; the other cities’ latest readings are below 1990. 2025 data is missing for Bengaluru and Delhi.

@weare.rladies.org Sep 23, 2026, 1:11 AM

This is the first #TidyTuesday I've done in a while. It can feel impossible to carve out the time to make something that is just okay, when other people are making #dataviz that is really beautiful. What's stopping you from participating? And how do you get past it? #rladies

@cborstell.bsky.social Sep 22, 2026, 2:42 PM

Urban Green Areas in Scandinavian capitals for this week's #TidyTuesday
🇩🇰🇳🇴🇸🇪

Stockholm started high in 1990 but has since dropped the most. City expansion, construction, definition of city limits, something else or all of the above?
#DataViz #ggplot2

Link: github.com/borstell/tid...

A small-multiples plot of the "Proportion of green areas across Scandinavian capitals: Copenhagen, Oslo and Stockholm". Each city has four panels - 1990, 2000, 2010, 2020 - showing a square of scattered green tiles representing the proportion of green areas. Below each square is an power bar-type plot showing each year's fill compared to the maximum across the 1990-2020 span, and next to it is a text label showing the percent change from the previous decade. While Oslo has had a quite stable level, Copenhagen lost a lot of relative green space in 2010, and Stockholm has been decreasing steadily and quite dramatically from the overall highest proportion in 1990, about half the proportion of 1990 in 2020. Data: UN Habitat Urban Indicators Dataset via TidyTuesday; Packages: {tidyverse, ggh4x}; Visualization: C. Börstell

@weare.rladies.org Sep 22, 2026, 11:40 AM

It's #TidyTuesday. This week's dataset is from the UN Habitat Urban Indicators Dataset . I haven't done one in a while, but I think I will give this one a go. If you made something for Tidy Tuesday, or you get stuck please share it with us! #rladies

@mel-likes-maps.bsky.social Sep 22, 2026, 11:32 AM

Whenever a dataset has location info, I like to start with a map.

This week’s #TidyTuesday data tracks urban green space since 1990 — and it’s global. First cut looks like it is trending toward less green area per person worldwide. #rstats #dataviz #rspatial

github.com/mhegeman/…

World map in an equal-area projection showing whether a country's urban green area per capita has increased or decreased since 1990. Countries with an increase in urban green area per capita from 1990 to 2020 are shown in blue, while those with a decrease are shown in orange. Countries without data are in gray.

@mitsuoxv.bsky.social Sep 22, 2026, 8:01 AM

My submission for #TidyTuesday, Week 38 on average share of green areas across cities. I explore share changes in China, India, and Japan.

Code: github.com/mitsuoxv/tid...

Line chart faceted by country (China, India, and Japan); x-axis is year from 1990 to 2020, and y-axis is average share of green areas in each city.

@sponce1.bsky.social Sep 22, 2026, 12:40 AM

📊 #TidyTuesday – 2026 W3 | Average share of green areas across cities (UN-Habitat Urban Indicators)
.
🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

Beeswarm chart titled The High End of Estimated Urban Green Area Came Down. Four stacked strips for 1990, 2000, 2010, and 2020 place about 1,110 cities by estimated green area share on a 0 to 80% axis, with a dashed line at 40%. None of the 83 cities estimated at 40% or more in 1990, drawn in burgundy in every strip, were still at 40% or more in 2020, yet 73% stayed in the top quarter of cities. The 83 ran from 40% to 77% in 1990, 23% to 68% in 2000, 13% to 44% in 2010, and about 5% to 35% in 2020. Most cities sit below about 20% each year, and the maximum fell from about 77% to 38%. Data: UN-Habitat Urban Indicators Database.

@karaman.is Sep 20, 2026, 8:13 AM

This week's #TidyTuesday is the Dead Sea Scrolls. An Euler diagram of the 28 canonical books in the collection, by manuscript language. 18 are Hebrew only, Leviticus is the only one in all three.

Code: github.com/gkaramanis/t...

#RStats #dataviz

Euler diagram titled "Dead Sea Scrolls: Biblical books by language", showing all 28 canonical books identified among the scrolls, placed by the primary language of the manuscripts that preserve them. Three overlapping rotated squares on a near-white background, Hebrew in pale sand, Greek in terracotta and Aramaic in muted teal. The Hebrew square is much the largest and holds 18 books on its own. Four books sit in the Hebrew and Greek overlap, four in the Hebrew and Aramaic overlap, and one, Leviticus, in the small region where all three meet. One book survives in Greek alone. No book is in Aramaic alone.

@nrennie.bsky.social Sep 16, 2026, 7:43 AM

This week's #TidyTuesday data is from the Dead Sea Scrolls digital library 📖

I created an interactive Voronoi diagram showing the number of images of manuscripts found in different locations 📊

#DataViz #ggplot2 #RStats

@jdonland.bsky.social Sep 16, 2026, 2:13 AM

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.

@jonthegeek.com Sep 15, 2026, 11:08 PM

@hadley.nz do you have an order-of-magnitude estimate of when the data-dict spec will reach 1.0? I'm considering switching to it for #TidyTuesday dictionaries but that will take lots of touches, so I want to make sure it's at least stable-ish first.

@dslc.io Sep 15, 2026, 5:11 PM

It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io!
#RStats #PyData #JuliaLang #RustLang #DataViz #DataScience #DataAnalytics #data #tidyverse #DataBS

@mitsuoxv.bsky.social Sep 15, 2026, 12:27 PM

My submission for #TidyTuesday, Week 37 on Dead Sea Scrolls Manuscripts. I explore material composition by period.

Code: github.com/mitsuoxv/tid...

Bar chart filled with scroll material; x-axis is # of scrolls, and y-axis is periods from Early Hellenistic to Roman.