underlander

underlander t1_jd492p4 wrote

Reply to comment by inkoativ in Optimize Figure Content in Kinder Surprise Eggs [OC] by inkoativ

so the chart’s kind of misleading. A reasonable person reading this (like me) would assume that “figure” is synonymous with “toy,” so we’re looking at whether or not the kinder egg had anything inside. But you’re saying what’s being measured here is whether it has something that you want, personally. So, that’s really confusing without an explanation

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underlander t1_jd44bdb wrote

Reply to Optimize Figure Content in Kinder Surprise Eggs [OC] by inkoativ

something’s weird here. How can a kinder egg, which comes out of an automatic manufacturing facility, vary by up to 10g and still not have a toy inside? The median weight of the candy is about 32g, so how do you get +/-5g, a big difference for such a lightweight candy, so regularly if there’s not a toy inside?

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underlander t1_j2dr8s9 wrote

Reply to Wild bears population size across the US and the EU. The US has 340 000 wild bears (300 000 Black bears, 33 000 Brown/Grizzly bears, 7 000 Polar bears). The EU has 15 500 wild bears (zero Black bears, 15 500 Brown/Grizzly bears, zero Polar bears). 2008-2022 data 🇺🇸🇪🇺🗺 [OC] by maps_us_eu

this is just a fancy table. The point of a table is to display all information with granularity. The point of a data visualization is to summarize information simply. If you need to write the value on every entity, then why use a color coded scale at all? Additionally, your legend is bonkers huge compared to the visualization itself. Look at how small the map is compared to the total frame of the image. And why do we give a shit about some of the information you’ve annotated? I don’t care about European population or the flag of Guam if they’re not on the chart.

What I don’t understand is how you’ve made 80 of these bear turds and never, ever, ever listened to feedback. Normally it’s a “practice makes perfect” kinda thing where people get better over time. Somebody could come back and look at this a few visualizations later and realize it’s an eyesore because they’ve got more experience under their belt. But not you. You’ll hover at this amateur level forever. And that sounds like hell to me

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underlander t1_iv5mos7 wrote

Reply to comment by madredditscientist in [OC] How long headphones last and where they fail by madredditscientist

tip for folks starting out in data visualization: consider the nature of the variable when deciding how to plot it. Time bins here are rank-order, so they should be plotted in a way that depicts that rank order but also reflects the fact that they’re discrete (not a continuous flow, they’re chopped up). So, a bar chart is most appropriate. Pie charts are for qualitative variables (like separate categories).

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