Very excited to participate on Haley Jeppson’s panel for “Use of Color in Statistical Charts” at #jsm2023! (Sunday 2-4 PM, CC-810)
I’ll use this (in progress) collection of @observablehq notebooks for computing on color:
https://observablehq.com/collection/@ijlyttle/color
Thanks to @zeileis, whose colorspace package in R inspired much of this work:
@ijlyttle @observablehq Very nice, impressive collection of useful tools! The interactive widgets are particularly fun to play around with.
@ijlyttle @observablehq Re: Unresolved thoughts on color vision deficiency.
In addition to the model-based implementation in #rstats {colorspace}, there is the classic {dichromat} based on experimental work by Brettel, Mollon, and Viénot in the late 1990s.
You can use these to check the differences.
@ijlyttle @observablehq Comparison of deutan emulations:
library("colorspace")
library("dichromat")
vi <- hcl.colors(9)
rb <- rainbow(9)
swatchplot(list(
"viridis" = rbind(
original = vi,
colorspace = deutan(vi),
dichromat = dichromat(vi, "deutan")),
"rainbow" = rbind(
original = rb,
colorspace = deutan(rb),
dichromat = dichromat(rb, "deutan"))),
nrow = 4)
@zeileis @observablehq Thanks once more! I'm relieved to notice only small differences in the results; looking forward to checking out "dichromat"!
@zeileis @observablehq Thanks Achim! It was driven by curiosity on things like "how do color spaces differ?", and "how do colors collapse under CVD?".
@ijlyttle @observablehq Yes, I can very much relate to that! This kind of curiosity inspired a lot of the functionality in colorspace.
I'm very happy to see that this was useful to you and that you took everything a couple of steps further.