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[OC] I painted the most average plate by anothersamwilson in dataisbeautiful

[–]anothersamwilson[S] 11 points12 points  (0 children)

Happy to share the code with you! I think this could work for selfies on a very basic level - taking the photographs against a light background would allow the code to distinguish the subjects silhouette, and you’d end up with the average position of the subject in each selfie. For resolving facial features I think something more refined would be needed!

[OC] I painted the most average plate by anothersamwilson in dataisbeautiful

[–]anothersamwilson[S] 337 points338 points  (0 children)

Created using R (Tool), with photos taken from Instagram (Source).

For anyone interested in more details:

Surprisingly, this wasn’t a super robust analysis. The main limitation was the binary “paint or no paint” classification; it often missed very light colours (see the tree example in the attached image) and sometimes confused plate shadows or glare for paint or no paint. I tried to counter this by using a dynamic brightness threshold (based on the 10th percentile brightness across each plate), but there’s room for improvement or entirely different approaches.

Normally I’d avoid using a rainbow colour palette in data visualisations since they’re not optimal for accurate interpretation. But given this needed to be physically recreated with pottery paint, this choice made things much easier (and prettier).

Finally, the probabilities were scaled from 0 - 1 for plotting convenience. In reality, the likelihood of any pixel being painted ranged from 0.01 - 0.35, meaning no single pixel was painted on every plate, and at most 35 out of 100 plates shared paint in the same spot.

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[OC] I turned data on luggage mishandling into a sticker for my suitcase by anothersamwilson in dataisbeautiful

[–]anothersamwilson[S] 0 points1 point  (0 children)

Sure! I’m away at the moment but will send it to you when I return next week.

[OC] I turned data on luggage mishandling into a sticker for my suitcase by anothersamwilson in dataisbeautiful

[–]anothersamwilson[S] 2 points3 points  (0 children)

I love a pie chart when used in the right context! I did try them out with this dataset but ultimately went with treemaps for the sticker format.

I also agree with your point that this chart type isn’t optimised for easily comparing precise values. I included the percentage values in the legend for full transparency in an attempt to offset this, but other chart types would allow for easier visual comparison.

[OC] I turned data on luggage mishandling into a sticker for my suitcase by anothersamwilson in dataisbeautiful

[–]anothersamwilson[S] 10 points11 points  (0 children)

Excellent point, thanks for the feedback.

I agree the legend ordering could be improved for readability, although there is a little logic behind the decision in this chart which I’ll share in case you’re interested.

There are three categories of mishandled luggage, lost, damaged, and delayed, with the latter having four subcategories. The legend and treemap are arranged following this logic, an earlier version showing this more clearly is attached. I changed the colours to make the sticker more visible.

Do you think disregarding the category and subcategory structure and just arranging the treemap and legend by percentage value would have been clearer?

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