iiinova — where your digital self keeps evolving

Reposted by Cormac Monaghan

David @corish.dev · 15/09/2026

Artificial intelligence is built on the work of all of us. The gains are going to just a few large firms. My late father often said to me, "that's great David, but what are you going to do about it?" I have written up what the Irish State can do, & by when. corish.dev/writing/ai-w...

corish.dev

AI, Work, Who Gains in Ireland

Artificial intelligence is built on the shared knowledge of humanity and finished by people paid by the hour. The gains go to the few firms that own the models. So, what are we going to do about it?

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Cormac Monaghan @c-monaghan.bsky.social · 11/09/2026

This town ain't big enough for the two of us 🤠 🔫

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Reposted by Cormac Monaghan

Vincent Arel-Bundock @vincentab.bsky.social · 04/09/2026

Big news! 🎉 𝚖𝚊𝚛𝚐𝚒𝚗𝚊𝚕𝚎𝚏𝚏𝚎𝚌𝚝𝚜 1.0.0 for #Rstats is out. It’s a big number and it feels like a big step. I wrote a blog on the challenges of interpreting statistical models, SPEED, cool new features, the future, and a 5 year package development and writing odyssey. arelbundock.com/posts/margin...

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Cormac Monaghan @c-monaghan.bsky.social · 04/09/2026

Father... How dare you abandon me downstairs

An image of an orange cat sitting on the floor. He does not look impressed.

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Reposted by Cormac Monaghan

Retraction Watch @retractionwatch.com · 04/09/2026

A psychology journal has retracted a seminal 2002 paper on deadlines and procrastination coauthored by behavioral scientist Dan Ariely of Duke University just days after a team of sleuths published analyses of the original study data, finding it had been tampered with.

retractionwatch.com

Procrastination study by Duke’s Dan Ariely retracted after sleuths find signs of data tampering

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Cormac Monaghan @c-monaghan.bsky.social · 26/08/2026

Running a meta analysis study and having all the processing, modelling, tables, figures, and #Quarto documents generated via targets (all in about 30 seconds) is immensely satisfying

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Reposted by Cormac Monaghan

Andrew Heiss @andrew.heiss.phd · 18/08/2026

My latest attempt at an AI/LLM policy in my intro to social science stats class. Basically two rules: 1. Everything human-facing must be human-generated 2. You are responsible for understanding, verifying, and citing everything an LLM generates quantf26.classes.andrewheiss.com/syllabus.htm...

Generative AI and LLMs
In this class, I have two general rules regarding LLMs:

Everything human-facing must be human-generated.
You are responsible for understanding, verifying, and citing everything an LLM generates.
I’ll explain what these mean below, but first, an important warning!

LLMs and learning
Phew, I can talk about the relationship between learning and LLMs for hours (see this for a more formal explanation of my thinking). LLMs and generative AI can be useful for statistical programming, but only when you already know what you are doing. They can be dangerous and counterproductive for beginners.

Additionally, using LLMs and generative AI to write does not lead to deeper learning. The point of writing is to help crystalize and organize your thinking. Pasting LLM-generated words into an assignment to make it look like you read and understood the content will not help you learn. Pasting LLM-generated code into an assignment and hoping that it works will not help you learn.

Rule 1: Everything human-facing must be human-generated
Generative AI tools like Gemini, ChatGPT, and Claude can be helpful for generating ideas or topics for your assignments and can even help you find existing research about topics you’re interested in. They are useful for debugging and troubleshooting code.

These uses are permitted in this course.

If you want to use LLM tools to document your code, clean up your notes, look up error messages, search for research related to your topic, and so on, cool. Do it. That’s fine. It’s unavoidable nowadays anyway, since every R-related Google search will give you a Gemini-generated answer at the top with mostly working R code.

Any writing and revisions, however, must be your own. You may not use AI tools to write any of the text you submit to me. AI text adds nothing to my understanding. I have no interest in engaging with it at all. There is nothing more disheartening for me than spending my time grading something that ChatGPT spat out in 10 seconds. I want to see good engagement with the readings. I want to see your thinking process. I want to see you make connections between the readings. I want to see your personal insights. I don’t want to see a bunch of words that look like a human wrote them. That’s not useful for future-you. That’s not useful for me. That’s a waste of time.

Thus, anything human-facing (i.e. not stuff done just for yourself, like your own personal notes, research, etc.) must be human-generated (i.e. written by you). You may not use AI tools to write any portion of your assignments. Using AI tools in this way, or failing to disclose the use of AI tools, will be treated as a case of plagiarism and referred to the Honor Council.

Rule 2: You are responsible for understanding, verifying, and citing everything an LLM generates
These tools are really good at working with code, but again, only if you know what you’re doing. Without guidance and expertise, LLMs love making lots of extraneous, convoluted, and weird code by default. The code ostensibly works, but it’s often strange and unnecessary and uses uncommon syntax and packages.

If you use LLMs for help with code, you must understand and verify what’s going on with your code and you must cite where it came from.

This means that you need to know what each line is doing. If the LLM uses a function or gives you an argument that you don’t understand or haven’t seen before, figure out why and figure out if it’s necessary. Look at the documentation for the function. Search Google for other examples. Ask the LLM about it, and then add comments to the code explaining what’s going on.

The citation doesn’t need to be anything formal (i.e. don’t worry about Chicago or APA guidelines)—it just needs to (1) say which LLM you used, and (2) mention what you asked the LLM.

You should do this by using code comments—inside your code chunk, add a # to the beginning of a line so that it’s treated as a comment (or text) instead of actual code.

Here’s an example of what this can look like:

Here’s an example of what this can look like:

# This calculates the average GDP per capita in each region in the dataset,
# for all countries after 2015. I couldn't remember how to use group_by() and
# summarise() together, so I asked Gemini:
#
# "I'm using tidyverse. I have a dataset named my_dataset and I'm filtering it
# to include all countries after 2015. I want to calculate region averages with
# group_by and summarise but cannot remember the syntax"

my_dataset |>
filter(year > 2015) |>
group_by(region) |>
# Gemini here included na.rm = TRUE, which omits any rows with missing values
# when calculating the average. That's okay and necessary here because
# South Sudan is missing some years of GDP data
summarise(avg_gdp = mean(gdp_per_cap, na.rm = TRUE))

# Gemini also included an extra ungroup() function at the end, but I don't need
# that because there are no groups leftover after summarising here

↑ That’s a lot of extra comments and you won’t always see stuff like that in real life code, but I want to see it here, and future-you will want to see it too.

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Cormac Monaghan @c-monaghan.bsky.social · 05/08/2026

Wordle 1,873 4/6 A certain #rstats theme popping up in today's Wordle 👀 🟨🟨⬛⬛⬛ ⬛⬛⬛🟨🟩 ⬛⬛🟩🟩🟩 🟩🟩🟩🟩🟩

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Reposted by Cormac Monaghan

Vincent Arel-Bundock @vincentab.bsky.social · 20/07/2026

I'll be in Ann Arbor next week to talk about `marginaleffects` and how to interpret statistical models. Let me know if you're around! pdhp.isr.umich.edu/workshops/ #RStats #PyData

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Cormac Monaghan @c-monaghan.bsky.social · 09/07/2026

Not all UFC fights are finished the same way. This week's #TidyTuesday looks at how weight class influences the path to victory. 🔗 Code: github.com/C-Monaghan/t... #rstats #ggplot2 #dataviz

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Reposted by Cormac Monaghan

Georgios Karamanis @karaman.is · 01/07/2026

This week's #TidyTuesday maps shipwrecks off Ireland, from the Wreck Inventory of Ireland Database Code: github.com/gkaramanis/t... #RStats #dataviz

A map titled "Shipwrecks off Ireland" showing the island of Ireland on a blue sea shaded by depth. Thousands of dark red dots mark recorded shipwrecks. They cluster most densely along the east and south coasts and in the north, thin out along the west coast, and continue as a sparse, widely spaced scatter far out into the Atlantic. A pale hatched band marks Ireland's maritime boundary, curving out to the west and south. The seas are labelled in italics: Atlantic Ocean to the west, Irish Sea to the east, Celtic Sea to the south. Ports are marked with star symbols and labelled: Dublin, Cork, Waterford, Shannon Foynes and Rosslare, with Derry (Foyle Port) and Greencastle in the north. A callout box in the lower right points to a wreck east of Dublin and reads: "RMS Leinster, 1918. Torpedoed by the German submarine UB-123, she sank with the loss of more than 500 of those aboard, the Irish Sea's worst single loss of life. The wreck now lies buried in sand about 25 metres down."

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Cormac Monaghan @c-monaghan.bsky.social · 01/07/2026

Centuries of maritime loss surround Ireland’s coast. This #TidyTuesday visualises the known locations of shipwrecks recorded in the Wreck Inventory of Ireland 🔗 Code: github.com/C-Monaghan/t... #rstats #ggplot2 #dataviz

Map of Ireland showing thousands of shipwreck locations plotted as faint white points around the coastline on a black background.

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Reposted by Cormac Monaghan

Daniel 🕹️ @strengejacke.de · 24/06/2026

A new paper by @dominiquemakowski.bsky.social, @mattansb.msbstats.info, me and colleagues just out! We show how to choose informative priors in Bayesian regression models using a systematic simulation study and a practical step-by-step tutorial in #Rstats and #Stan! doi.org/10.3389/fpsy... >

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Reposted by Cormac Monaghan

Daniel P. Moriarity @dpmoriarity.bsky.social · 21/06/2026

Love that my non academic friends wish me a happy father's day with shit like this 😂

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Reposted by Cormac Monaghan

Nicolas Lambert @neocarto.bsky.social · 18/06/2026

This dataviz is just amazing visquill.com/gallery/uk-a1

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Cormac Monaghan @c-monaghan.bsky.social · 16/06/2026

The number of babies named Arya rose dramatically after Game of Thrones premiered in 2011, turning an uncommon name into a much more popular choice across the UK. 🔗 Code: github.com/C-Monaghan/t... #TidyTuesday #rstats #ggplot2 #dataviz

Area chart showing babies named Arya across Britain and Ireland from the 1990s to 2024. Counts remain low until the 2011 premiere of Game of Thrones, after which the name becomes much more popular, peaking in the early 2020s.

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Cormac Monaghan @c-monaghan.bsky.social · 10/06/2026

This week for #TidyTuesday we are looking at video game films and asking a simple question → "Do better reviewed video game films actually make more money?" Some franchises thrive commercially despite weak reviews, and vice versa. 🔗 Code: github.com/C-Monaghan/t... #rstats #ggplot2 #dataviz

Scatterplot of video game film franchises showing the relationship between average Rotten Tomatoes critic score and average revenue per film. Each point represents a franchise, with point size and transparency indicating number of films in the franchise and colour indicating whether the franchise is a blockbuster (10+ films), major franchise (3-9 films), or a single/sequel franchise (1-2 films).

Labels highlight selected high-performing franchises.

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Cormac Monaghan @c-monaghan.bsky.social · 04/06/2026

Keeping it relatively simple for this week's #TidyTuesday 🔗 Code: github.com/C-Monaghan/t... #rstats #ggplot2 #dataviz

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Reposted by Cormac Monaghan

Json Momoa @jotatx.eurosky.social · 01/06/2026

Infographics should be as simple as informative as this one here.

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Cormac Monaghan @c-monaghan.bsky.social · 26/05/2026

Thesis writing has been taking my time so I haven't been on the #dataviz side of things. Fortunately, I had some free time and whipped up this little #TidyTuesday plot showing how Ireland energy generation has shifted from a "Biomass" focused to a more "Wind" focused. #rstats #ggplot2 #dataviz

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Reposted by Cormac Monaghan

juliusbogo.bsky.social @juliusbogo.bsky.social · 25/05/2026

I made Conway–Maxwell–Binomial regression on #TMB + #glmmTMB. A bounded-count family that handles both over- and under-dispersion through one parameter.. Demo on coral fertilization data from @benoitpujol.bsky.social #rstats #STEM #biology jbogomolovas2.github.io/Julius-s-Blo...

jbogomolovas2.github.io

Conway–Maxwell–Binomial regression: two-directional dispersion for bounded counts – Julius’s Blog

Data Science, Swimming Analytics, and R Programming

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Reposted by Cormac Monaghan

Andrew Heiss @andrew.heiss.phd · 05/05/2026

heyyy Positron has a packages panel now!! And it works with both #rstats and Python! github.com/posit-dev/po...

Screenshot from Positron showing all my installed R packages that contain "gg"

Screenshot from Positron showing all my installed Python packages that contain "py"

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Cormac Monaghan @c-monaghan.bsky.social · 08/04/2026

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

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.

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Reposted by Cormac Monaghan

Carlos Scheidegger @cscheid.net · 06/04/2026

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/)

quarto.org

What’s next: Quarto 2 – Quarto

We’ve started working on quarto-dev/q2, a full rewrite of Quarto in Rust.

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Reposted by Cormac Monaghan

#30DayChartChallenge @30daychartchall.bsky.social · 31/03/2026

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

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Cormac Monaghan @c-monaghan.bsky.social · 31/03/2026

#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 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.

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Cormac Monaghan @c-monaghan.bsky.social · 23/03/2026

A #PiDay visualisation for this weeks #TidyTuesday 🔗 Code: github.com/C-Monaghan/t... #RStats #dataviz #PiDay #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).

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Reposted by Cormac Monaghan

Ilya Kashnitsky @ikashnitsky.phd · 21/03/2026

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 👀

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Cormac Monaghan @c-monaghan.bsky.social · 19/03/2026

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

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Reposted by Cormac Monaghan

Adam Kucharski @adamjkucharski.bsky.social · 16/03/2026

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

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Cormac Monaghan @c-monaghan.bsky.social · 16/03/2026

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

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.

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Reposted by Cormac Monaghan

James Balamuta @coatless.bsky.social · 15/03/2026

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

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.

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Reposted by Cormac Monaghan

Isabella Velásquez @ivelasq3.bsky.social · 10/03/2026

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...

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Cormac Monaghan @c-monaghan.bsky.social · 10/03/2026

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

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

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Reposted by Cormac Monaghan

Ella Kaye @ellakaye.co.uk · 09/03/2026

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...

github.com

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

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Cormac Monaghan @c-monaghan.bsky.social · 03/03/2026

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

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bpiros.bsky.social @bpiros.bsky.social · 26/02/2026

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

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Reposted by Cormac Monaghan

Sean Lunsford @seanlunsford.com · 26/02/2026

This week's #TidyTuesday almost looks like modern art or something tidytuesday.seanlunsford.com/...

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.

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Reposted by Cormac Monaghan

Gustavo Frosi @gustavofrosi.bsky.social · 24/02/2026

This has been a challenge, but it's really cool! #TidyTuesday #ggplot2 #RStars

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Rajo @rajodm.bsky.social · 24/02/2026

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

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.

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Manasseh @manasseh6.bsky.social · 24/02/2026

Science Foundation Ireland Grants Commitments for #TidyTuesday, wk 8. #Rstats #Dataviz #ggplot2

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Cormac Monaghan @c-monaghan.bsky.social · 23/02/2026

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 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.

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Reposted by Cormac Monaghan

Nicola Rennie @nrennie.bsky.social · 23/02/2026

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

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.

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Steven Ponce @sponce1.bsky.social · 22/02/2026

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

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.

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Cormac Monaghan @c-monaghan.bsky.social · 17/02/2026

This week for #TidyTuesday we are looking at New Zealand agricultural production statistics. 🔗Code: github.com/C-Monaghan/t... #RStats #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.

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Cormac Monaghan @c-monaghan.bsky.social · 13/02/2026

More #OSM map art 🖼️ 🔗 Code: github.com/C-Monaghan/d... #dataviz #ggplot2

A map of Ireland showing active and abandoned railway lines.

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Cormac Monaghan @c-monaghan.bsky.social · 12/02/2026

Decided to try my hand at playing with #OSM data and made a little 🗺️ of Maynooth. 🔗 Code: github.com/C-Monaghan/d... #dataviz #ggplot2

A map of Maynooth located in county Kildare, Ireland. The map features both major and minor roads of Maynooth, along with its canals, railway line, and local university.

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