explore | Grilled Cheese

@nrennie.bsky.social Oct 14, 2026, 8:23 PM

A little bit of #Rtistry 🎨

#RStats #ggplot2 #GenArtClub #CreativeCoding

Generative art piece consisting of a 5x8 grid of squares with lines coming from a random, central point.

@nrennie.bsky.social Oct 13, 2026, 3:34 PM

One of the great things about #TidyTuesday is that one day you can be playing with data on avocado oil authenticity, and the next learning all about frogs and toads 🐸

So here's a quick map of the presence of frogs and toads across European countries 🗺️

#RStats #DataViz #ggplot2

Small multiples choropleth map series of Europe showing where 14 frog and toad species are present across European countries. Each mini-map highlights countries in green where a species occurs and gray where it does not. The main message is that frogs and toads are widespread across Europe, but species distributions vary considerably: the Eastern Tree Frog is found in only 10 countries, while the Common Toad is the most widespread, present in 36 countries.

@sponce1.bsky.social Oct 12, 2026, 6:51 PM

📊 #TidyTuesday – 2026 W41 | AmphibiaWeb
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

AmphibiaWeb has written accounts for 233 of the 1,170 native frog species it lists for Brazil (20%). A 15-square grid, one per country with at least 250 native species, runs from the largest list to the smallest; each square's area shows the number of native species, and a burgundy fill rising from the bottom shows the share with an account. In 13 of the 15 countries, the share sits between 18% and 30%; Madagascar (274 of 441, 62%) and Australia (221 of 258, 86%) stand apart. An account means a description, life-history, or distribution text on AmphibiaWeb; a missing account does not mean a species lacks published research. Species found in several countries count in each list, so lists cannot be added together. Introduced occurrences and 31 species with ambiguous country codes are excluded. Source: AmphibiaWeb via TidyTuesday 2026 week 41.

@nrennie.bsky.social Oct 12, 2026, 1:38 PM

Catching up on last week's #TidyTuesday data about avocado oils 🥑

Obviously I had to make an avocado (pie?) chart 📊

#RStats #DataViz #ggplot2

Pictogram chart titled "Holy Guacamole! Your avocado oil probably isn’t real." highlighting widespread fraud in the avocado oil market, where 89% of tested products failed authenticity testing according to a 2026 UC Davis study. The chart uses three avocado illustrations to represent passing rates, where the size of the brown avocado pit corresponds to the percentage of products that passed testing. Only 29% of tested mayonnaise passed authenticity testing, shown by a large pit; only 7% of tested chips passed, represented by a small pit; and none of the tested salad dressings passed, depicted by an avocado with no pit.

@sponce1.bsky.social Oct 11, 2026, 11:07 PM

📊 #MakeoverMonday – 2026 W41 | Tourism Nights Booked Through Platforms
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

European NUTS 2 regions with data. Each row is one region and each column a month; color shows that month's share of the region's annual nights, from blue (below an even 1/12) through neutral to burgundy (26% or more). Rows run from most to least summer-heavy. A dark July–August band runs down the chart and fades toward the bottom: 220 of the 251 regions peaked in July or August, but those two months hold between 15.5% and 65.9% of a region's nights. Yugoiztochen (Bulgaria) is the most summer-heavy region. Adriatic Croatia ranks second for the year among these regions but falls outside the top 25 in October–December. Alsace, Sud-Vest Oltenia and Northern & Eastern Finland stand out with December peaks, and Northern & Eastern Finland is the least summer-heavy. Every region gets equal space regardless of size. Regional data are missing for parts of Portugal (including Lisbon), the Netherlands, and Iceland. Source: Eurostat, tour_ce_omn12

@novica.bsky.social Oct 11, 2026, 7:15 PM

{ggindex}: numbered points that explain themselves #rstats #ggplot2

new extension that I think should be useful. based on a long forgotten demo, that I decided to revisit. see more here: discindo.github.io/ggindex/ or discindo.org/posts/2026-1...

the first release is submitted to CRAN.

@instats.bsky.social Oct 9, 2026, 9:19 PM

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

@ginareynolds.bsky.social Oct 9, 2026, 5:24 PM

#ggplot2 #rstats

@sponce1.bsky.social Oct 6, 2026, 8:49 PM

📊 #MakeoverMonday – 2026 W40 | Songs with Cowbell
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Horizontal bar chart titled "Most cowbell comes one song at a time." It groups the 654 songs on cowbellsongs.com's reader-submitted list by how many songs each artist has there, and each bar counts the songs that group contributes. The highlighted bottom bar shows that artists listed only once supply 337 songs, just over half the list, one per artist. Above it, gray bars show 9 songs from Kiss, 8 from The Beatles, 14 from The Allman Brothers Band and Rush, 24 from Guns N' Roses, Led Zeppelin, Queen and Van Halen, and 15 from The Donnas, Jimi Hendrix and Prince, then 64 songs from 16 artists with four each, 63 from 21 artists with three, and 120 from 60 artists with two. A note says all seven Allman Brothers songs were added in one July 2026 update. Source: cowbellsongs.com.

@sponce1.bsky.social Oct 5, 2026, 9:02 PM

📊 #TidyTuesday – 2026 W40 | Avocado Oil Authenticity
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

@joachimschork.bsky.social Oct 5, 2026, 7:39 AM

Overplotting is a common challenge when visualizing data.

One interesting way to address this issue is by using blending techniques with the ggblend package in R.

I recently explored this in a Statistics Globe Hub module: statisticsglobe.com/hub

#RStats #ggplot2 #ggblend #DataScience #Statistics

@ansgarw.bsky.social Oct 3, 2026, 8:18 PM

Parliamentary seat share in the German Bundestag since 1949

Inspired by @economist.com's chart showing how the factions and parties evolved in Israel's Knesset.

#ggplot2 📊

A ribbon chart which shows the share of parliamentary seats of political factions in the German Bundestag. Each election year is marked on the vertical axis. Each party's seat share is connect with the next election year. The name of the chancellors are marked at the first legislative term of their time in office.
The chart shows a lot of stability, esp. between 1961 to 1980, with only 3 factions in the Bundestag. The Greens joined in 1983, the PDF/Leftist Party in 1990 after the reunification. The right-wing extremist AfD entered in 2017. Since 2025, the Bundestag consists of 5 factions + 1 MP from the SSW.

@dataviz.ar Oct 3, 2026, 7:44 PM

I recently became a dad, which is the most wonderful experience I've... experienced!

But it's been no bed of roses 🎵 No pleasure cruise 🎶👑

It is no news that a baby disrupts parents' sleep. Well, I share my evidence of that with a #dataviz

Still worth it!!!💙👶

#ggplot2 #rstats

@daxkellie.bsky.social Oct 2, 2026, 4:25 AM

This thread is so great! 🤩

To avoid annoying resizing, figured I'd mention an alternative to {ggview}: {showtext} can resize all text to a specific dpi

Text is the part that I find I have to fiddle with most when saving #ggplot2 plots, so this usually works for me 😀

#rstats #dataviz

library(showtext)
showtext_opts(dpi = 300)
ggsave(
plot = my_plot,
file = "file_name.png",
width = 6,
height = 4,
dpi = 300
)

@nrennie.bsky.social Oct 1, 2026, 10:17 AM

If you missed today's @rinpharma.bsky.social workshop, here are a few of my favourite "quick fixes" for some common #ggplot2 issues 📊

1. Use {ggview} to preview plots at the same size and resolution as you want to save them with ggsave() to avoid the annoying resizing

#RStats #DataViz

library(ggview)
ggplot(mtcars, aes(wt, mpg)) +
geom_point() +
canvas(
width = 6, height = 4,
units = "in",
dpi = 300
)

@simongreenhill.bsky.social Sep 30, 2026, 8:10 PM

why are there 100s of #Rstats packages providing colors for #ggplot2, and only 2 or 3 broken ones providing themes?

I'm getting sick of theme_classic() and theme_bw() and want something new.

@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.