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Analyze a categorical color palette

Source: R/analyze_palette.R
analyze_palette.Rd

Analyze a categorical color palette with respect to the differences between the colors in the palette.

Usage

analyze_palette(
  palette,
  cvd = c(protan = 0, deutan = 0, tritan = 0),
  bg = NULL,
  metric = c("ciede2000", "din99d", "cie76")
)

Arguments

palette

Either a matrix of RGB values (with values between 0 and 1), a data frame with RGB values, or a character vector of hex colors.

cvd

Color vision deficiency adaptation. This must be a named vector with names protan, deutan, and tritan and values between 0 and 1, where 0 means no adaptation and 1 means full adaptation.

bg

Background color to consider (but not include) when generating the palette. This is useful to avoid colors that are too close to the background/canvas color. If NULL (the default), the background color is not considered at all. Any color that is convertable via col2rgb is acceptable, including hex colors.

metric

The color metric to use for the color distance matrix.

Value

A list of lists, one for each type of color vision deficiency plus normal vision. Each list contains difference_matrix, min_distances, and bg_min_distance

See also

qualpal()

Examples

pal <- qualpal(5)
analyze_palette(pal$hex, cvd = c(protan = 1))
#> $deutan
#> $deutan$difference_matrix
#>          #c9cb70  #7cc4cc  #cb7469  #6e6cca
#> #7cc4cc 31.95148                           
#> #cb7469 41.40243 43.64204                  
#> #6e6cca 64.37811 33.37803 35.19014         
#> #d8c3e9 45.21974 31.62615 29.96390 29.99927
#> 
#> $deutan$min_distances
#>  #c9cb70  #7cc4cc  #cb7469  #6e6cca  #d8c3e9 
#> 31.95148 31.62615 29.96390 29.99927 29.96390 
#> 
#> $deutan$bg_min_distance
#> [1] NaN
#> 
#> 
#> $normal
#> $normal$difference_matrix
#>          #c9cb70  #7cc4cc  #cb7469  #6e6cca
#> #7cc4cc 31.95148                           
#> #cb7469 41.40243 43.64204                  
#> #6e6cca 64.37811 33.37803 35.19014         
#> #d8c3e9 45.21974 31.62615 29.96390 29.99927
#> 
#> $normal$min_distances
#>  #c9cb70  #7cc4cc  #cb7469  #6e6cca  #d8c3e9 
#> 31.95148 31.62615 29.96390 29.99927 29.96390 
#> 
#> $normal$bg_min_distance
#> [1] NaN
#> 
#> 
#> $protan
#> $protan$difference_matrix
#>          #c9cb70  #7cc4cc  #cb7469  #6e6cca
#> #7cc4cc 31.75402                           
#> #cb7469 26.29280 26.85895                  
#> #6e6cca 57.52869 26.64757 36.71673         
#> #d8c3e9 38.33444  6.73337 33.51401 26.53771
#> 
#> $protan$min_distances
#>  #c9cb70  #7cc4cc  #cb7469  #6e6cca  #d8c3e9 
#> 26.29280  6.73337 26.29280 26.53771  6.73337 
#> 
#> $protan$bg_min_distance
#> [1] NaN
#> 
#> 
#> $tritan
#> $tritan$difference_matrix
#>          #c9cb70  #7cc4cc  #cb7469  #6e6cca
#> #7cc4cc 31.95148                           
#> #cb7469 41.40243 43.64204                  
#> #6e6cca 64.37811 33.37803 35.19014         
#> #d8c3e9 45.21974 31.62615 29.96390 29.99927
#> 
#> $tritan$min_distances
#>  #c9cb70  #7cc4cc  #cb7469  #6e6cca  #d8c3e9 
#> 31.95148 31.62615 29.96390 29.99927 29.96390 
#> 
#> $tritan$bg_min_distance
#> [1] NaN
#> 
#>