#30DayChartChallenge Day 21: Historical 🍺

Here are the side-by-side graphs

#30DayChartChallenge Day 15: Correlation 📈 Thanks to tylervigen.com for the fun idea. Check his website for more Spurious Correlations

#30DayChartChallenge Day 14: Trade 🍽️

#30DayChartChallenge Day 13: Ecosystems 🌳

It was interesting to see how the top 10 #IMDb movies were ranked on #Letterboxd

#30DayChartChallenge Day 10: Pop culture 🍿Ratings on #IMDb vs #Letterboxd #DataViz

screenshot of exercise 5 in our coordinate system lesson in "ggplot2 unchartred" shwoing the solution which is a ciruclar heatmap of (fictious) traffic data per hour and weekday.

Late for #Day8 of the #30DayChartChallenge, but the "Circular" prompt is the perfect excuse to share yet another #ggplot2 trick! 👀

Tried the "new" #coord_radial yet? Compared to coord_polar, it:

🌀 bends grid lines
🌀 places labels naturally
🌀 allows inner radii
🌀 handles partial circles easily

#30DayChartChallenge Day 9 : Wealth 💰 with data from the OECD Wealth Distribution Database #DataViz

#30DayChartChallenge Day 8: Circular ⭕ #dataviz

A faculty position is available at the University of Windsor, Canada. Details: efhc.fa.ca2.oraclecloud.com/hcmUI/Candid...
🧪🌐🦉🦑🦊🐻‍❄️

A three-panel chart showing the distribution of renewable electricity share across countries in 2022 at three scales. Panel 1 is a histogram of the global distribution, revealing a bimodal shape with many countries clustered near 0–20% and another group near 100%, with a median of roughly 32%. Panel 2 shows half-eye density plots by World Bank income group — Low, Lower-middle, Upper-middle, and High — all with similar medians around 28–36%, but notably different spreads and shapes. Panel 3 zooms in on Upper-middle income countries as a dot plot, where individual-country variation is stark: South Africa sits near 0%, China and Turkey near 25–30%, and Brazil and Paraguay near 80–100%.

📊 #30DayChartChallenge 2026 – day 07
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Distributions | Multiscale
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Day 7 — #30DayChartChallenge

Wine quality dataset 🍷
Built a multiscale visualization:
• Macro → score distributions (red vs white)
• Meso → feature distributions by quality tiers
• Micro → feature behavior per score

🔗https://wine-quality-distribution.vercel.app/

#DataViz #D3js #DataStorytelling

Map of Earth setting behind the moon, with Australia the lone visible landmass. The lunar surface is faded, but visible.

Earth setting behind the moon, with coastline overlain. Australia is the lone visible large landmass.

The lunar surface fills the frame in sharp detail, as seen during the Artemis II lunar flyby, while a distant Earth sets in the background. This image was captured at 6:41 p.m. EDT, on April 6, 2026, just three minutes before the Orion spacecraft and its crew went behind the Moon and lost contact with Earth for 40 minutes before emerging on the other side. In this image, the dark portion of Earth is experiencing nighttime, while on its day side, swirling clouds are visible over the Australia and Oceania region. In the foreground, Ohm crater shows terraced edges and a relatively flat floor marked by central peaks — formed when the surface rebounded upward during the impact that created the crater.

Map of Earth setting behind the Moon from the perspective of Artemis II.

I can’t decide which variant of this Artemis II Earthset map I like best. flic.kr/p/2s6et1Z

#NASA #Moon #Earth #dataviz

This is without the bottom plots

#30DayChartChallenge Day 6: Reporters Without Borders (data) 🎥 & Day 7: Multiscale ⚖️
(ideally, it needs more fine-tune adjustments, but I was trying to show both ends of the scale) #DataViz

#30DayChartChallenge Day 4: Slope 📈
#dataviz

This chart shows how many species are considered threatened in #Canada, broken down by major taxonomic group (mammals, birds, amphibians, plants, etc.), according to the IUCN Red List. Title : How many species are threatened in Canada? Subtitle : The count in each major taxonomic group, according to the Red List. The entire image has a red background with everything else in white (text, labels, data points etc). The chart is a circular pictorial plot, using aspecific pictogram for each taxonomic group. There's Amphibians, Reptiles, Mammals, Molluscs, Plants, Fungi, Birds, Other Inverts, Fishes. At the center there is a frog, representing the Amphibians, it's alone as there is only one species of Amphibian that is threatened. It then extends to 5 pictograms for reptiles and then goes all the way to fishes which are the largest group in terms of threatened species, for a count of 52 species. The source is The IUCN Red List of Threatened Species Made by @stevecarufel.bsky.social for the #30DayChartChallenge

#30DayChartChallenge Day 2 : Pictograms
(erratum version, see thread)

⚠️🦊🐦🐸🐌🌿🍄⭐🦎🐛

This chart shows how many species are considered threatened in #Canada, broken down by major taxonomic group (mammals, birds, amphibians, plants, etc.), according to the IUCN #RedList.

#Rstats #dataviz #wildlife

Ooops, transparent background 🤦

See the strategy here www.canada.ca/en/services/...

Inspiration for the map was a low-resolution (i.e. mosaic) of this map www.canada.ca/en/environme...

#30DayChartChallenge Day 3: Mosaic. Although we have achieved much, the Government of Canada just announced its new Strategy to Protect Nature, investing $3.8B to protect nature, better design infrastructures and fund sustainable conservation projects🍁🌱🌊

albert-rapp.de/posts/ggplot...

#30DayChartChallenge Day 2: Pictogram 🎬🍿Thank you @albertrapp.bsky.social for the tutorial on how to use images with ggpattern (link below)

Oh dear haha 😂

Hope to participate as much as I can in the #30DayChartChallenge 📊 Starting day 1: Part-to-Whole, with a very basic graph, inspired by the #ArtemisII launch. Very proud of our Canadian representative on this mission 🚀🌑

Introducing ggauto: automating better charts – Nicola Rennie The ggauto package is an opinionated ggplot2 extension package that aims to help people make better charts by default. This blog post explains why it exists and how it works.

🎉 ggauto is now on CRAN 🎉

An #RStats package that selects better chart types, and provides more accessible styling for #ggplot2 plots 📊

Blog post explaining why I made it and how it works: nrennie.rbind.io/blog/introdu...

#DataViz

Okay so why was I the only one in the theater tonight who gasped when Ryan Gosling ran a centrifuge with two tubes in the whole thing, sitting right next to each other? Sorry was I the only scientist in the film society theater tonight??

Working smarter with dplyr 1.2.0 R-Ladies Rome | Isabella Velásquez. The dplyr hex logo on the side.

There's a new version of dplyr out, and I'm thrilled to chat about it with @rladiesrome.bsky.social tomorrow!

Let's learn the new #RStats functions that help you grip your data better (hehe).

3/18 at 12pm CT. Sign up here! www.meetup.com/rladies-rome...

🚢 {whalestrike} is now available on CRAN! cran.r-project.org/web/packages...

In case you missed it, the R package calculates the lethality of a ship strike, accounting for ship size, type, and whale species.
#MarineMammals #ShipStrike