My #30DayChartChallenge Day 4 entry, "The Space Divide: Minimum Chimp Habitat in Zoos and the Wild," looks at the "big or small" theme by comparing minimum cage sizes for #chimpanzees to their expansive natural habitats 🐵 I said I'd use Python but this is #Rstats and sadly I just couldn't get the facet chart I had in my mind to work within the time I have 😂

Diving into #Vermont wildlife for the #30DayChartChallenge "circle" day! 🦌 Using #Python & #plotly to compare monthly #Moose and #BlackBear sightings 🐻 Data wrangled with #BigQuery and #SQL. Any guesses which animal is seen more consistently throughout the year? 😜 #DataViz #Wildlife #RadialChart

Menacing upward trend alert! ⚠️ Day 2 of #30DayChartChallenge focuses on "slope," and #spottedlanternfly observations on #iNaturalist paint a concerning picture. This #invasivespecies (#Lycormadelicatula), highlighted in my #Rstats infographic (back to Python tomorrow 🐍), poses a threat to our trees. Join #citizenscientists and report any sightings to help track this pest! #HitchHikerBug #DataViz #Ecology

#30DayChartChallenge let's go! This #dataviz was for a bit of #Python practice, and I could *not* have sorted out the non-linear gradient for 1 > n > 0 without #claudeai. Made with #seaborn and patience 🎨 #FractionArt #ColorfulMath #PythonDataViz #FractionFunhouse

I used #rstats to create this map of Uganda that shows distribution of various types of health services. The data are from #HumanitarianDataExchange (HDX) but I struggled with the regions (adm1) and districts (adm3) as they don't quite line up. The pharmacies form intriguing clusters in the NW and SW on the border with the DRC. As always, I wish I had heaps more time to work on this map! 🗺️ #30DayMapChallenge

#30DayMapChallenge Day 7's theme is vintage, so here's an #rstats map of Montpelier that has a background the color of old paper and serif fonts. There's so much more that could be done, but I am out of time! I like the color ramp and it feels like old-school atlas or classroom wall map colors to me. I really wanted to add streets, structures, and the river, and zoom in on downtown, but that will wait for another day 🧭

#30DayMapChallenge raster data today! I have never before made a map with raster data, or used it at all. I do not like this map! I need to spend a whole lot more time playing around with the #rstats {terra} package, and I am grateful for data embedded in packages or I would probably have not been able to make a map today. This cm.colors ramp is wild...

Day 5 of the #30DayMapChallenge is "a journey" so here's my counterclockwise #PemiLoop from a few years ago. I wish I had a lot more time to play around with the colors and layout, and hoped to color the arrows to match days 1, 2, and 3 of the hike, but that proved surprisingly tricky. Anyway, better than yesterday when I could not even get any hexagons to appear 😂 (Note: we left off W Bond and N Twin due to intense rain, Owl's Head is generally done separately but shown here for completion)

Well, sadly my #30DayMapChallenge career has come to an abrupt end. I've spent two hours today trying to make one work and the hexbin and geogrid packages are just not my friends! I will need to try again and follow along with a tutorial. I spent too much time finding data and not enough time coding! I will try again tomorrow 🤪

A more interesting map today for #30DayMapChallenge. This one tells a bit a bit of story and gives rise to new questions! What's interesting to me is that the largest "biological hotspots" are in the Champlain Valley, which is the most densely region. Grateful for #VermontOpenGeodataPortal for providing these shapefiles and heaps of other #Vermont map data. I really thought there'd by more hotspots in the East, along the Upper Connecticut River Valley...