A horizontal dot plot showing repair success rates across 14 categories of items brought to repair cafés. Each category is listed on the y-axis, and the x-axis shows the percentage of successful repairs from 50% to 100%. Each category has a dot connected by a thin line. The size of each dot represents the number of repair attempts, with larger dots indicating more attempts. A vertical dashed line marks the average success rate (around 75%). Categories like Textile, Tools (non-electric), and Bicycles have high success rates (above 85%), while categories such as Display and sound equipment and Computer equipment and phones have lower success rates (around 55–60%) despite having large numbers of repair attempts. The chart highlights that while most items can be repaired successfully, some high-volume categories have comparatively lower repair success rates.

This week's #TidyTuesday looks at repair cafés.

Some are fixed almost every time (like textiles 🧵), while others are brought in a lot but succeed less often (such as electronics).

🔗 Code: github.com/C-Monaghan/t...

#rstats #ggplot2 #dataviz

What’s next: Quarto 2 – Quarto We’ve started working on quarto-dev/q2, a full rewrite of Quarto in Rust.

And another Quarto announcement; I've alluded to it before, but we're making it "official".

We've started work on Quarto 2. The blog post has an overview: quarto.org/docs/blog/po...

We'll share more in future blog posts, but here's what you can expect from the Quarto 2 dev effort:

(1/)

A banner with the prompts for the 6th edition of the 30DayChartChallenge

Just one day - or, depending on your timezone maybe just a few hours - until the next #30DayChartChallenge kicks off. 🚀

1 prompt for each day of April across 5 categories to spark inspiration, experimentation, and learning. 📊

Who's in? 👀

#dataviz #datavisualization

A multi-line time-series plot showing yearly ocean temperatures (in Celsius) across different ocean depths. The depths range from 2 meters up to 40 meters. The graph shows the at the beginning of the year (in Winter) the temperature of the ocean is relatively similar across different depths (approx 5 degrees Celsius). However, as we get closer to late Spring / early Summer (around May) the lines on the graph begin to diverge. Temperatures begin to rise more rapidly at shallower depths and (when compared to deeper depths) become hotter. For example, depths of less than 10 meters can reach temperatures of around 18 degrees Celsius, whereas depths of between 30 - 40 meters only reach temperatures of around 10 degrees Celsius.

#TidyTuesday explores ocean temperatures 🌊

The ocean doesn’t warm evenly - surface waters heat up quickly, while deeper layers lag behind.

🔗Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

A spiral graph showing the first one thousand digits of pi. The spiral begins inward with the first number of pi (3) and gradually grows outward fading slowly until it reaches the final number (8).

A #PiDay visualisation for this weeks #TidyTuesday

🔗 Code: github.com/C-Monaghan/t...

#RStats #dataviz #PiDay #ggplot2

This is truly an exceptional idea and execution 😍

No more wondering how to share small datasets for quick analysis easily reproducible

Just use Ziptable to pack a small dataset in the URL itself! Mind-blowing really 👀

@chess.com REALLY wants me to solve today's puzzle ⚠️

Great to see all the visualisations people have been doing with the CAPphrase dataset (github.com/adamkucharsk...) for #TidyTuesday!

Map of Norway showing where farmed salmon are lost across aquaculture regions. Each county is represented by four coloured circles placed around the county centre. The circles show the total number of salmon lost between different causes: red for fish that died, yellow for fish that were discarded, teal for escaped fish, and grey for other losses. Circle size represents the number of fish lost, with larger circles indicating greater losses. Across most counties, the red circles representing fish mortality are much larger than the other categories, indicating that most losses are due to fish dying rather than escaping or being discarded. Escaped fish and discarded fish are present in many regions but at much smaller scales. Losses are concentrated along Norway’s western and northern coastline, where most aquaculture activity occurs. Inland regions show few or no losses. Overall, the map highlights that mortality dominates salmon losses across Norway’s aquaculture industry, with escapes and other causes contributing smaller shares.

What happens to lost farmed salmon in Norway?

This #TidyTuesday map shows where salmon losses occur across Norwegian counties and whether they die, escape, are discarded, or are lost for other reasons.

🔗 Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

Promotional card for webRoid v1.0.0 with the tagline "R on Android" on a dark navy background with green circuit-trace accents. Three phone screenshots show the R console after running a plot command (with a notification badge on the Plots tab), the plots gallery displaying the resulting scatter plot, and the code editor with syntax-highlighted R script and output panel.

This IS the droid you're looking for.

webRoid v1.0.0: R on Android. Console, editor, plots, packages, 9 themes. No server required.

Tested extensively on emulators. Your actual device? The Force is strong, but no promises.

play.google.com/store/apps/d...

#rstats #webR #Android #WebAssembly

Who needs PowerPoint when you have Quarto and @emilhvitfeldt.bsky.social's extensions?!

I wanted to make an image-heavy presentation. Usually, I'd reach for Keynote.

But Emil shared quarto-revealjs-editable, which let me stay in Quarto land 🥰 Thanks, Emil!

Find it here: github.com/EmilHvitfeld...

Dot plot showing how people interpret common probability phrases on a 0–100% likelihood scale. Phrases like “Almost No Chance” and “Highly Unlikely” appear near 0%, “About Even” sits around 50%, and phrases such as “Likely,” “Very Good Chance,” and “Highly Likely” cluster above 90%, with “Will Happen” interpreted as essentially certain. Several middle phrases—including “May Happen,” “Might Happen,” and “Could Happen”—are interpreted very similarly, highlighting ambiguity in everyday probability language

How likely is "likely"?

This week's #TidyTuesday explores probability phrases

"Likely" is more likely than "Probable", but "Likely" is less likely than a "Very Good Chance"

🔗 Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

Haha yes!! It's an Irish robin 🇮🇪

GitHub - EllaKaye/localtime: Quarto shortcode to display times in the reader's local timezone Quarto shortcode to display times in the reader's local timezone - EllaKaye/localtime

Announcing localtime, a #QuartoPub extension for displaying times in the reader's local timezone e.g.

{{< localtime 2026-03-09 20:30 UTC >}} will render as 2026-03-09 21:30 for someone in CET.

Has nice formatting options, and automatically accounts for daylight saving.

github.com/EllaKaye/loc...

Today I saw a Robin 🙏🏻

See full article: onlinelibrary.wiley.com/doi/10.1111/...

This #TidyTuesday looks at Golem Grad Tortoise Data 🐢

On Golem Grad island, tortoises have become increasingly male-biased and females are showing declining body condition and reproductive output compared to the mainland population.

🔗 Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

This week’s #TidyTuesday explores Science Foundation Ireland grant commitments. I built 100% stacked bar charts to show how yearly commitments are divided among the top research bodies.
#pydytuesday #matplotlib #dataviz

Sankey diagram titled 'Follow the Money' showing grant commitments from Science Foundation Ireland's top 10 programs to research institutions from 2000 to 2024. The Research Centres Programme dominates with €991M in commitments, followed by the Principal Investigator Programme at €594M. Trinity College Dublin is the largest recipient overall, receiving major funding from nearly all programs, followed by University College Dublin and University of Galway. Other significant recipients include University College Cork, University of Limerick, and Tyndall National Institute. The diagram reveals heavy concentration of funding among traditional universities, with smaller allocations to technological universities and research institutes.

This week's #TidyTuesday almost looks like modern art or something

tidytuesday.seanlunsford.com/...

This has been a challenge, but it's really cool!

#TidyTuesday
#ggplot2
#RStars

Beeswarm plot showing the number of grants awarded by Science Foundation Ireland across three decades (2000s, 2010s, 2020s), grouped by funding programme. The Research Frontiers Programme dominated the 2000s with 763 grants. In the 2010s, the Technology Innovation Development Award (496) and Conferences and Workshops Programme (434) were the largest. By the 2020s, the Discover Programme and Frontiers for the Future led, each with 393 and 321 grants respectively. Most other programmes across all decades awarded fewer than 150 grants.

Exploring Science Foundation Ireland programme groups with the most awarded grants from October 2021 to March 2025 for #TidyTuesday 2026, week 08.

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2

Science Foundation Ireland Grants Commitments for #TidyTuesday, wk 8.

#Rstats #Dataviz #ggplot2

Line plots showing how much grant funding 7 universities in Ireland received from Science Foundation Ireland over a 25 year period. These universities include (in order of total grant funding): Trinity College Dublin, University College Dublin, University of Galway, University College Cork, University of Limerick, Dublin City University, and Maynooth University. Overall, Science Foundation Ireland committed 3.46 billion euro into university research.

This week for #TidyTuesday we're looking at Science Foundation Ireland grant funding.

Below we can see how much research funding each Irish university received over a 25 year period.

🔗 Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

Line chart showing the number of grants beginning each year from Science Foundation Ireland, for proposals containing the words 'science', 'technology', 'engineering', or 'mathematics'. There is an increase in 'science' titles from around 2015. An annotation notes the start of the Discover Programme in 2013.

I dug into the proposal titles from this week's #TidyTuesday on Science Foundation Ireland grants, looking at which ones specifically mention STEM subjects 📈

Code: github.com/nrennie/tidy...

#RStats #ggplot2 #DataViz

Woops, I didn't know not adding something like that would break stuff 😅

A two-panel time series (2001–2024) exploring Science Foundation Ireland's legacy. The top panel shows annual grant commitments as a teal area chart, peaking at €469M in 2019 before a sharp 2024 drop reflecting SFI's July dissolution. The bottom panel shows new institutions funded each year as a bar chart, with 2013–2017 highlighted in teal, during which 59 new institutions entered the ecosystem. Together, the panels argue that while SFI's funding fluctuated, its institutional reach grew steadily until the end. Note: totals reflect commitments by grant start year, not annual expenditure; 2024 is a partial year.

📊 #TidyTuesday – 2026 W08 | Science Foundation Ireland Grants Commitments
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Multiple line plots showing the population increase/decrease in different animals from New Zealand across time. The animals include; sheep, poultry, horses, goats, deer, chickens, and cattle. Overall most of this populations have declined with time. However, the chicken population has been steadily increasing.

This week for #TidyTuesday we are looking at New Zealand agricultural production statistics.

🔗Code: github.com/C-Monaghan/t...

#RStats #dataviz #ggplot2

A map of Ireland showing active and abandoned railway lines.

More #OSM map art 🖼️

🔗 Code: github.com/C-Monaghan/d...

#dataviz #ggplot2

Corrected link: github.com/C-Monaghan/d...