This week's #TidyTuesday data is about salmon mortality 🐟 in Norway

📊 Small multiple line charts show each region
🔍 Larger chart explains how to read
🧩 Arranged using {ggh4x} with {cowplot} for annotations

Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-03-17

#RStats #DataViz #ggplot2

Last week’s #TidyTuesday: how different age groups valued probability phrases—with differences most apparent between the two ends, under 18 and 75+.

Also, how is “will happen” not 100% for…100% of people?

https://tidytuesday.seanlunsford.com/2026/week-10/

Finally got around to digging into the Netflix #tidytuesday viewing data. Just ~100 shows capture ~25% of all views! Very cool dataset. https://ahoulette.com/posts/netflix-analysis/ #rstats #dataviz
Netflix Viewing Analysis: A Look Into the Catalog Abyss – Semi-Random Data Musings

A really fun dataset for this week's #TidyTuesday, looking at how people interpret different probabilistic statements 📊

There's so many aspects of this to visualise, but I decided make some barcode plots comparing how people from the UK and US responded. It seems we're much the same with the exception of may/might happen and highly likely - where our American friends are more optimistic!

Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-03-10

#DataViz #RStats #ggplot2

RE: https://fosstodon.org/@ivelasq3/116199832839171986

Today at noon EDT (16:00 UTC) I'll join pos.it/dslab to show you how to get a dataset in front of the wider community through #TidyTuesday!

We all love the stunning visualizations from the #TidyTuesday project, and behind every great viz is a great dataset!

Tomorrow at 12pm ET, @jonthegeek pulls back the curtain on his data curation process that makes them possible 💙

Register here: https://pos.it/dslab