If you're not 100% confident you can make it to her tutorial on visualising uncertainty, no doubt you won't want to miss Harriet Mason's talk on the ggdibbler package.

More info and registration link here: wombat2025.numbat.space/program/work...

#DataViz #RStats #Python #DataScience

Registrations for the annual #WOMBAT2025 workshop are open! 🎉
Register before August 31 to get the early bird discount!

🔗 Registration & Info: wombat2025.numbat.space

#DataScience #OpenSource #BusinessAnalytics #DataDrivenDecisions #RStats

Join Dianne Cook and Harriet Mason to learn about visualising uncertainty in plots using #RStats. Register by August 31 for an early bird discount! 🐧

wombat2025.numbat.space/program/tuto...

#DataScience #DataViz #BusinessAnalytics #DataDrivenDecisions #RStats

Wrapped up #useR2025 👩‍💻

Feeling inspired to explore Positro and dive into tools like Air, chores, hexsession, arrow, and sparsvctrs.

Also made a tile of my R package collection with hexsession 📦✨.

Thanks to everyone who made this event such a great learning experience 🙌

#rstats #RCommunity

Just finished at #useR2025 and had a great time! Especially because I had an opportunity to present ggdibbler.

The slides from my talk are available here: harriet-mason.github.io/talk_useR-20... (Although they are a bit confusing out of context)

The idea is to just have a "sample" version of every base geom in a ggplot extension, rather than crafting new bespoke plots. The only reason it is entirely maps is because I just got back from several months of leave and haven't implemented the other geoms yet lol.

ggdibbler is not supposed to be competing with Vizumap, it's building upon the package. There are two main reasons I implemented ggdibbler:
1) I wanted a ggplot extension so I could have all the flexibility of a ggplot.
2) I wanted it to run a bit faster

They are actually supposed to be the same plot. The difference between Lydia's pixel map and the ggdibbler implementation isn't the visuals. Changing the options to palette=Greens, linewidth=0, and n=10 (which are all accessible as ggplot2/ggdibbler options) makes the two plots nearly identical.