explore | Grilled Cheese

@sponce1.bsky.social Sep 29, 2026, 10:02 AM

📊 #MakeoverMonday – 2026 W39 | World Population
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Waffle chart titled "Growth Slowed. The Yearly Additions Didn't. Where They Happen Did." From 1981 to 2020, the world added roughly 83–90 million people a year even as the growth rate fell from 1.8% to 1.1%, while Sub-Saharan Africa's contribution rose from 12 million to 29 million a year and East Asia & Pacific's fell from 25 million to 14 million. Thirteen grids show five-year windows from 1961–65 to 2021–25; each square is 1 million people of net population growth per year, ochre for Sub-Saharan Africa, teal for East Asia & Pacific, gray for the rest of the world. Gaps separate Before (1961–80), The Plateau (1981–2020), and Possible Break (2021–25). Sub-Saharan Africa overtook East Asia & Pacific in 2001–05. The final grid was smaller: 72 million a year, with East Asia & Pacific contributing 4 million. Source: World Bank, World Development Indicators.

@nrennie.bsky.social Sep 29, 2026, 8:11 AM

This week's #TidyTuesday data looks at health metrics in cities across the world 🏥 I created a dot density plot of the population density around hospitals 📊

Thanks to @gabspalomo.bsky.social for curating this week's dataset!

#RStats #DataViz #ggplot2

Four-panel dot density chart comparing the median population living within 1 km of a hospital in 2025 across World Bank income groups. Each dot represents approximately 200 people. Low-income countries have 50,000 people, lower-middle-income countries 53,000, upper-middle-income countries 44,000, and high-income countries 28,000. The chart’s main message is that higher-income countries tend to have lower population density around hospitals.

@sponce1.bsky.social Sep 28, 2026, 2:52 PM

📊 #TidyTuesday – 2026 W39 | Health metrics in urban centres worldwide
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

Faceted scatter plot titled "Income looks like the divide. City density explains most of the gap." Four panels, one per World Bank income group, plot the share of residents within 1 km of a hospital against population density (log scale) for 6,427 urban centers. A large dot marks each group's typical city: 23% within 1 km in high-income countries, 33% in upper-middle, 36% in lower-middle, and 36% in low-income, even though high-income cities have about twice as many hospitals per person as low-income cities. The typical cities sit at rising densities, from about 3,000 people per km² (high income) to about 7,300 (low income), and all four lie close to one dashed curve showing the access–density relationship for all cities. Density strips above each panel show where cities fall on the density axis; cities without hospital data lean denser in low-income countries. Source: GHS Urban Centre Database R2024A, European Commission JRC.

@rajodm.bsky.social Sep 24, 2026, 6:52 PM

re-up because the previous version was not easy to read (or misleading? check the gh repo)

#TidyTuesday 2026 week 38

Overall, African cities have seen their green space quietly disappear

code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2

Histogram showing African cities' green space share falling across four decades (1990–2020); nearly all cities cluster under 5% green space, and by 2020 the median was just 4.12%, the lowest of the four decades.

@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.

@nrennie.bsky.social Sep 22, 2026, 3:04 PM

A question for the #RStats and #ggplot2 hivemind:

What things do you find most tricky or frustrating in ggplot2?

It might be things like specific chart types, aspects of styling, your workflow, saving charts etc.

@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

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

📊 #TidyTuesday – 2026 W3 | Average share of green areas across cities (UN-Habitat Urban Indicators)
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🔗: 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.

@sponce1.bsky.social Sep 21, 2026, 8:11 PM

📊 #MakeoverMonday – 2026 W38 | CFB Roster Spending 2026
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Range chart in four panels, titled SEC Roster Budgets Run Deeper Than Any Other Power Conference's. 14 of 16 SEC programs' estimated 2026 roster-budget ranges sit entirely above the $23.5M median, versus 9 of 18 in the Big Ten, 3 of 17 in the ACC, and 1 of 16 in the Big 12. Each bar spans one program's low-to-high estimate: burgundy if entirely above the median line, dark gray if it crosses the line, light gray if entirely below. Texas Tech is the only Big 12 program above the line. Source: The Athletic.

@lcolladotor.bsky.social Sep 18, 2026, 7:05 PM

140.776 - Statistical Computing at @jhubiostat.bsky.social @johnshopkinssph.bsky.social

Lecture 06 summary

lcolladotor.github.io/jhustatcompu...

#RStats #DataScience #ggplot2 #DataViz

@instats.bsky.social Sep 17, 2026, 11:42 PM

New Instats livestreaming seminar: Introduction to Longitudinal Analysis & Multilevel Modeling #Business #Economics #DataScience #Statistics #ggplot2 #R #Tidyverse #Research #ResearchTraining #Instats

@instats.bsky.social Sep 16, 2026, 1:00 PM

Last chance to register for Tidy Data, Visualisation, and AI #ComputationalBiology #InformationScience #SoftwareEngineering #Statistics #DataScience #ggplot2 #R #tidyverse #Research #ResearchTraining #Instats

@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

@sponce1.bsky.social Sep 15, 2026, 12:53 AM

📊 #TidyTuesday – 2026 W37 | Dead Sea Scrolls Manuscripts
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

Two-panel dot plot of Dead Sea Scrolls manuscript counts (works with ≥16 manuscripts). Left panel: six familiar biblical books, Genesis at 24. Right panel adds other catalog works — 1 Enoch also totals 24, tying Genesis and exceeding Exodus, Isaiah, and Leviticus, showing the biblical canon captures only part of this collection's textual world.

@sponce1.bsky.social Sep 13, 2026, 9:13 PM

📊 #MakeoverMonday – 2026 W37 | What are the world's deadliest animals?
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Log-scale dot plot: envenomation (~100,000 deaths yearly, mostly snakes) sits far closer to disease transmission (~830,000) than to animal attacks (~1,500). Each step represents 10x more deaths. Source: Our World in Data.

@nrennie.bsky.social Sep 11, 2026, 7:28 PM

Join me on October 1 for a #ggplot2 workshop as part of the @rinpharma.bsky.social conference! 📊

#RStats #DataViz

R in Pharma flyer. Date October 1 and time 3-5pm GMT+8

@nrennie.bsky.social Sep 9, 2026, 12:50 PM

A very quick pictogram for #TidyTuesday this week where we're looking at The Cappuccino Index ☕

#RStats #DataViz #ggplot2

Pictogram chart titled "Italy is one cup ahead," showing how many small cappuccinos a barista can buy with one hour of pay across various European countries. Italy leads at 5 cups, followed by countries like Switzerland, Netherlands, and Germany at 4 cups, down to nations like Romania and Belarus at 1 cup.

@cborstell.bsky.social Sep 8, 2026, 6:11 PM

Me wanting to make the entire workflow reproducible ends up with me writing #ggplot2 code to draw a cow for a figure 🐮

@sponce1.bsky.social Sep 7, 2026, 6:40 PM

📊 #TidyTuesday – 2026 W36 | The Cappuccino Index
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Scatter plot titled "Cheap coffee isn't necessarily affordable coffee," plotting mean hourly barista wage against mean cappuccino price across 87 countries on log-log axes, with dashed diagonal lines marking equal work-time cost at 30, 60, and 120 minutes. Pakistan (£1.87 cappuccino, 277 minutes of work) and India (£1.96, 172 minutes) sit far left with cheap coffee but extreme work-time burdens driven by very low wages. Switzerland and Denmark, both averaging £5.41 per cappuccino, require only 14 and 19 minutes, respectively, reflecting high wages. The remaining gray points cluster loosely along the diagonal. Source: James Hoffmann via Filip Reierson.