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#dataviz

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Continued thread

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

Continued thread

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.

Continued thread

Breaking this down by category can get a little tricky. This is only looking at the true or false counts for whether I got dressed for work, gym, or other, at least once during that day. It is not representative of how many times I wore that outfit, or the total unique outfits I ever wear. For example, I may go out twice in a day, wear the same outfit, but I'm only looking to see if I wore an outfit for that purpose at least once (which is the usual case). It doesn't tell me for how long I might have worn that outfit, nor is it easy for me to track if this is an outfit that I am repeating easily since I wear clothes so infrequently overall.

Thus, out of all the 160 days I got dressed to leave the house:
- 55% of the days, I dressed for "errands, walks, volunteering, etc." at least once or more
- 30% of the days, I dressed for the gym at least once or more
- 15% of the days, I dressed for work at least once or more

Using the Stylebook App, I have a virtual catalog of all my clothing. I can put these together to make "outfits" which I record each day. The stats that are available within the app aren't very useful, they don't give me as much insight as to what I am actually wearing as opposed to what I might own.

I decided to copy the data into google sheets based on how many outfits I wear for work, gym, or other. Other here represents running errands, going on a walk, seeing a friend, etc. It is not necessarily an indicator of me getting dressed for the purposes of expressing my "style" so to speak, as it's hard to separate that and outfits that were put on for necessity. I haven't really considered labeling these outfits rigorously. As I switched apps at some point, I have a little more than 400 days worth of outfits recorded.

As a hybrid, mostly remote worker, I get dressed about 40% of the time, or around 3 days a week. 60% of the time, I am home in PJ's that I rotate every few days.

Continued thread

Another hundred lines for #AnnoPlot #dataviz project in March:

hcommons.social/@beadsland/113

Work in March included:

• accommodating less frequent updates to GISAID data
• debug flattening in preparation for tilt
• mixin for checking orientation and helper class for rotation and mirroring
• rework and refactor tilt calculations for inline bubble charts
• refactor degrees str method to helper class and consolidate older asdegrees util method

✅ Versión para Mastodon

🎨 ¿Visualizás datos con R y buscás inspiración?

Publicamos el Capítulo I de una serie de recursos para mejorar tus gráficos con R — útil tanto si estás empezando como si ya trabajás con datos.

Incluye herramientas como:
🧭 From Data to Viz, por Yan Holtz
🎨 ColorBrewer, por Cynthia Brewer
🧩 Radiografía interactiva del theme() en ggplot2
🔍 Galería de extensiones para ggplot2

📘 Leé el artículo completo:
👉 estacion-r.com/blog/87ed9382-b

#RStats #DataViz @rstats