Checking bivariate palettes for colorblind friendliness

Jakub Nowosad

2024-10-10

Source: vignettes/articles/check_bivariate_pals.Rmd 
library(colorblindcheck)
library(pals)
bivcol = function(pal, nx = 3, ny = 3){
  tit = substitute(pal)
  if (is.function(pal))
    pal = pal()
  ncol = length(pal)
  if (missing(nx))
    nx = sqrt(ncol)
  if (missing(ny))
    ny = nx
  image(matrix(1:ncol, nrow = ny), axes = FALSE, col = pal, asp = 1)
  mtext(tit)
}
par(mfrow = c(3, 4), mar = c(1, 1, 2, 1))
bivcol(arc.bluepink)
bivcol(brewer.divdiv)
bivcol(brewer.divseq)
bivcol(brewer.qualseq)
bivcol(brewer.seqseq1)
bivcol(brewer.seqseq2)
bivcol(census.blueyellow)
bivcol(stevens.bluered)
bivcol(stevens.greenblue)
bivcol(stevens.pinkblue)
bivcol(stevens.pinkgreen)
bivcol(stevens.purplegold)

arc.bluepink 

colorblindcheck::palette_check(arc.bluepink(), plot = TRUE, bivariate = TRUE)

#>           name  n tolerance ncp ndcp  min_dist mean_dist max_dist
#> 1       normal 16  7.135562 120  120 7.1355623  27.72463 53.76783
#> 2 deuteranopia 16  7.135562 120   99 0.3648051  19.64988 50.09252
#> 3   protanopia 16  7.135562 120   95 0.5934073  19.58975 46.04090
#> 4   tritanopia 16  7.135562 120  118 6.1471867  30.58057 72.03219

brewer.divdiv 

colorblindcheck::palette_check(brewer.divdiv(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp  min_dist mean_dist max_dist
#> 1       normal 9  16.97102  36   36 16.971022  42.56758 82.75525
#> 2 deuteranopia 9  16.97102  36   30  5.067789  30.46320 65.57001
#> 3   protanopia 9  16.97102  36   30  7.042854  32.71365 65.71263
#> 4   tritanopia 9  16.97102  36   31  7.095207  36.41785 65.82211

brewer.divseq 

colorblindcheck::palette_check(brewer.divseq(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp min_dist mean_dist max_dist
#> 1       normal 9  9.237516  36   36 9.237516  38.32933 87.90123
#> 2 deuteranopia 9  9.237516  36   36 9.237516  37.40017 89.41009
#> 3   protanopia 9  9.237516  36   36 9.237516  38.56532 83.88314
#> 4   tritanopia 9  9.237516  36   34 5.912858  33.38318 77.49865

brewer.qualseq 

colorblindcheck::palette_check(brewer.qualseq(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp  min_dist mean_dist max_dist
#> 1       normal 9  14.93833  36   36 14.938334  42.05184 79.63286
#> 2 deuteranopia 9  14.93833  36   28  4.858979  27.76300 56.59805
#> 3   protanopia 9  14.93833  36   30  6.708243  30.46437 53.17718
#> 4   tritanopia 9  14.93833  36   33  3.942693  37.66875 64.33504

brewer.seqseq1 

colorblindcheck::palette_check(brewer.seqseq1(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp  min_dist mean_dist max_dist
#> 1       normal 9  12.48476  36   36 12.484763  34.39167 70.52906
#> 2 deuteranopia 9  12.48476  36   27  3.584833  23.58200 66.74934
#> 3   protanopia 9  12.48476  36   26  2.357097  24.01347 62.78648
#> 4   tritanopia 9  12.48476  36   33 11.759762  38.99925 71.65934

brewer.seqseq2 

colorblindcheck::palette_check(brewer.seqseq2(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp min_dist mean_dist max_dist
#> 1       normal 9  13.21133  36   36 13.21133  39.99288  94.5981
#> 2 deuteranopia 9  13.21133  36   34 11.21547  39.78176  94.5981
#> 3   protanopia 9  13.21133  36   34 10.70449  38.68412  94.5981
#> 4   tritanopia 9  13.21133  36   35 12.20966  39.86703  94.5981

census.blueyellow 

colorblindcheck::palette_check(census.blueyellow(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp min_dist mean_dist max_dist
#> 1       normal 9  6.669521  36   36 6.669521  29.31884 68.67608
#> 2 deuteranopia 9  6.669521  36   34 3.377298  27.64322 66.25251
#> 3   protanopia 9  6.669521  36   35 3.023168  26.10517 60.00471
#> 4   tritanopia 9  6.669521  36   35 5.396144  23.28601 46.33222

stevens.bluered 

colorblindcheck::palette_check(stevens.bluered(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp  min_dist mean_dist max_dist
#> 1       normal 9  10.93275  36   36 10.932747  32.73816 56.19377
#> 2 deuteranopia 9  10.93275  36   35 10.561421  25.43956 52.51736
#> 3   protanopia 9  10.93275  36   32  7.652898  24.31248 54.67234
#> 4   tritanopia 9  10.93275  36   35 10.861179  36.01006 61.83824

stevens.greenblue 

colorblindcheck::palette_check(stevens.greenblue(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp min_dist mean_dist max_dist
#> 1       normal 9   9.29651  36   36 9.296510  26.34666 50.19184
#> 2 deuteranopia 9   9.29651  36   33 7.245191  24.30123 49.30228
#> 3   protanopia 9   9.29651  36   35 7.702681  24.31445 46.14009
#> 4   tritanopia 9   9.29651  36   29 6.057564  20.34418 51.34639

stevens.pinkblue 

colorblindcheck::palette_check(stevens.pinkblue(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp  min_dist mean_dist max_dist
#> 1       normal 9  10.87893  36   36 10.878926  30.75955 54.56011
#> 2 deuteranopia 9  10.87893  36   26  3.778188  20.83687 55.71269
#> 3   protanopia 9  10.87893  36   26  3.232687  21.71195 52.06754
#> 4   tritanopia 9  10.87893  36   36 10.880404  35.54375 67.25533

stevens.pinkgreen 

colorblindcheck::palette_check(stevens.pinkgreen(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp min_dist mean_dist max_dist
#> 1       normal 9  8.367352  36   36 8.367352  27.08876 56.88416
#> 2 deuteranopia 9  8.367352  36   30 1.276375  15.80572 33.62710
#> 3   protanopia 9  8.367352  36   31 2.254391  18.27315 36.19185
#> 4   tritanopia 9  8.367352  36   33 5.602960  27.12226 50.82910

stevens.purplegold 

colorblindcheck::palette_check(stevens.purplegold(), plot = TRUE, bivariate = TRUE)

#>           name n tolerance ncp ndcp  min_dist mean_dist max_dist
#> 1       normal 9  11.97625  36   36 11.976253  30.13646 53.56032
#> 2 deuteranopia 9  11.97625  36   34 10.169263  27.11770 45.73965
#> 3   protanopia 9  11.97625  36   34 11.217796  28.98069 49.34292
#> 4   tritanopia 9  11.97625  36   24  4.024065  20.65332 49.63636