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#dataviz

7 posts7 participants0 posts today
Public
Kate Nyhan

Challenge for #DataViz experts

I have an interesting dataset covering 15 years of author decisions about whether to embargo their theses, and for how long. But I'm not sure how to visualize it. Any suggestions?

Screenshot of a spreadsheet Each row is one year and has values for the number of theses with: no embargo, a temporary embargo lasting 0, 1, 2, 3, 4, or 5 years, or a permanent embargo. There is heat map formatting showing red for higher numbers and green for lower numbers, with each year, for the seven embargo categories. That shows that, among theses with any embargo, the embargo duration is most frequently 2 or 3 years. The years with any permanent embargoes are visible outliers.
Public
Harald Kliems

Some #DataViz maps and a table on county-level population in #Wisconsin: Dane County continues to lead in growth, in absolute and relative terms. And #Milwaukee County may have reversed its trend was growing between 2023 and 2024.

haraldkliems.netlify.app/posts

On the technical side, this was my first time using the new major version of the #tmap #Rstats package. Takes some getting used to, but a lot of it makes much more sense than in the previous version. r-tmap.github.io/tmap/

Harald KliemsHarald Kliems: County-level population change in WisconsinWhich counties have gained the most population between 2020 and 2024? Which ones shrunk?
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François, suricate nihiliste

@jaztrophysicist A short #dataviz for a forthcoming local communication by the regional supercomputing center CALMIP, to illustrate our recent results on the measurement of turbulent magnetic diffusion at the solar surface.

The convection turbulent velocity field at the solar photosphere, with a resolution of 2500km and a time-sampling of 15 minutes, over a consecutive time-window of 6 days, was computed on the supercomputer based on 4096^2 NASA/SDO images of the photosphere with 45seconds sampling. Images on the right represent the distribution of magnetic fields at the photospheric level of the Sun, also obtained from the SDO MDI instrument. #astrodon #fluids #turbulence #science #ComplexSystems

aanda.org/articles/aa/full_htm

GIF
Replied in thread
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sport of sacred spherical cows

Updated #CDC estimates show we'd pretty much been in a JN.1.11 soup since Dec, until late March, when LP.8.1 took majority.

Data collection continues to be low priority nationally—as exactly zero regions have enough data for CDC to plot.

CDC breaks out recombinant XFC from FLiRT parent LF.7 . (Our dataviz now identifies parentage for each recombinant.) Raj's dashboard was last updated today.

❖ #ThisIsOurPolio #Covid #Covid19 #SARS2 #variants #CovidIsNotOver #CovidIsAirborne #dataviz #datavis

Chart: Estimated U.S. Variant Proportions by Common Name Sources: CDC, Cov-Lineages, NYITCOM, D. Focosi, WHN, others [ beadsland on Ko-fi ] Reskin of CDC's Variants Nowcast, and any significant variants in GISAID not broken out by CDC. Five bar-style tree-charts, for fortnights through 4/27–5/10. Legend of last fortnight, organized by subheadings of color-grouped families and convergent clusters. Percentages overlay each color key, reflecting share as of most recent tree-chart. LP.8.1 accounts for over two thirds. For 4/13–4/26, packed bubble charts fill single-variant tiles, reflecting GISAID detail not shown by CDC's Nowcast. Legend: JN.1.11 + FLiRT31 [purple]: 70% - LP.8.1.1 / NY†, LP.8.1.8† & other LP.8.1 JN.1 + FLiRT-LF [browns]: 9% - XFC 3% - LF.7.7.2 2% - XFG† & other LF.7 2% - LF.7.7.1 & LF.7.2.1 / PC JN.1 + FLiRT [reds]: 2% - LB.1.3.1 / NL 1% - XEC.4 ½% - XEQ 6% - other XEC JN.1.11 [greens]: 3% - MC.10.1 & PA.1 ⅓% - KP.3 2% - MC.1, MC.19 & other KP.3.1.1 / MC JN.1.11 + FLiRT31 [blue]: ⅓% - XEK Other [greys]: ⅐% - NB.1.8.1†, JN.1.16 & other WHO-Vaccine-Target JN.1 ⅒% - Other (not specified) _____ XEC is a child of FLiRT cluster KS.1.1 and FLuQE cluster KP.3.3. XEK is a child of FLiRT31 cluster KP.2.3 and recombinant XEC. XEQ is a child of FLiRT cluster KS.1.1.2 and FLuQE cluster KP.3. XFC is a child of FLiRT31 cluster LP.8.1.1 and FLiRT cluster LF.7. XFG is a child of FLiRT31 cluster LP.8.1.2 and FLiRT cluster LF.7. [Add'l footnotes omitted.]
Public
InarticulateQuilter

Borders added! There’s a few buildings that need a touch more fabric paint, but luckily I haven’t rinsed the dye I made up yet

When quilted, this one will be called Suffragist City, a name that has utterly delighted me the whole time I worked on it

(just an aside - I know as a U.S. women’s suffrage-themed quilt this technically should have been purple and yellow, but that color combo is nails on a chalkboard for me, so 💜 and 💚 it is)

Quilt top featuring a city skyline representing when different racial categories of women won the right to vote in the U.S., displayed in full on a design wall. There are five scrappily pieced unique buildings in the foreground each with a machine embroidered sign representing racial class and an address representing the year; from left to right they are White Women/1920 (in purples) Asian Women/1952 (in greens) Native Women/1963 (very light greyish white) Black Women/1965 (greens) Latina Women/1975 (purples) Behind the these buildings, which resemble a votes for women flag, are scrappily pieced black buildings set against a bright blue sky where the words VOTES FOR WOMEN have been pieced from purple, white and green fabrics. Surrounding this center section on both sides and the top is a small black and grey stripe border and a wider border of a black and white skyline print that has had random groupings of buildings painted in purple and green. The bottom border consists of a small light grey strip resembling a sidewalk and a wider section of mirrored black, purple, green and white fabrics that slant outward like rays.
#Quilting#InarticulateSkyline#DataViz
Continued thread
Public
🍄Inoculating the Odd in AuDHD

This is a chart that represents per category: how many times in a month did I get dressed? I only included months for where I had a full set of data.

We can see that, on average:
- I get dressed for "errands, walks, volunteering, etc." for about 2-3 weeks out of the month, which is decreasing.
- I get dressed to go to the gym for about 1-2 weeks out 2 weeks out of the month, which is increasing.
- I get dressed for work for a little less than 1 week out of the year, which is steady.

Note that since going on walks may include either gym clothes or outdoor clothes, I put it in the "other" category at times when I was only engaging with fitness, so the actual count of outfits only worn for fitness is higher than looking at the gym data alone. This means that outfits that might be purely for socializing, not for going on walks, are likely lower than is seen here. I also don't work out when traveling for work or family, which inflates my behavior out certain outfits relative to my daily life at home. I don't think a year is enough for me to really generalize how travel may be affecting this data.

line chart of times I got dressed for the categories of work, gym, or other
#clothing#data#quantifiedLife
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