automatically build the perfect palette for you plot with paletteRAndrea Cirillo9th MilanoR meeting, 20th November 2017

paletteR: palettes from custom images

• takes any image

• applies statistical learning

• builds a palette

draw from any image an optimised custom palette

takes any imagex y R G B

1 275 0,9960784313725 0,9960784313725 0,9960784313725

1 274 0,9960784313725 0,9960784313725 0,9960784313725

1 273 0,9960784313725 0,9960784313725 0,9960784313725

1 272 0,9960784313725 0,9960784313725 0,9960784313725

1 271 0,9960784313725 0,9960784313725 0,9960784313725

1 270 0,9803921568627 1,0000000000000 0,9803921568627

1 269 0,9803921568627 1,0000000000000 0,9803921568627

1 268 0,9843137254902 1,0000000000000 0,9882352941176

1 267 0,9882352941176 1,0000000000000 0,9921568627451

1 266 0,9960784313725 1,0000000000000 1,0000000000000

1 263 0,9960784313725 0,9960784313725 1,0000000000000

1 257 0,9960784313725 1,0000000000000 1,0000000000000

1 256 0,9960784313725 1,0000000000000 1,0000000000000

1 255 0,9960784313725 1,0000000000000 1,0000000000000

1 254 0,9921568627451 0,9960784313725 1,0000000000000

readJPEG(…)

applies statistical learning

kmeans(painting_rgb[,c(“R","G","B")],centers = number_of_colors, iter.max = 30)

applies statistical learning

R B

G

builds a palette

rgb_raw_palette <- k_means$centers final_palette <- rgb(k_means$centers)show_col(final_palette)

builds a palette

no knowledge about relationships between colours

no order in colours

too dark and too bright colours

interlude: rgb and hsv

By SharkD - Own work, GFDL, https://commons.wikimedia.org/w/index.php?curid=3375025

RGB

interlude: rgb and hsv

By SharkD - Own work, GFDL, https://commons.wikimedia.org/w/index.php?curid=3375025

RGB

Hue

Value

HSV

paletteR gots cleaver

• learning colours

• palette optimization based on variable type

learning coloursrgb_raw_palette %>% t() %>% rgb2hsv() %>% t() %>% data.frame() %>% mutate(id = row.names(.)) -> hsv_raw_palette

learning coloursrgb_raw_palette %>% data.frame() %>% mutate(id = row.names(.)) %>% left_join(hsv_raw_palette) %>% left_join(rgb_text_vector) %>% arrange(h,s,v) -> sorted_raw_palette

learning coloursbrightness_stats <- boxplot.stats(sorted_raw_palette$v)saturation_stats <- boxplot.stats(sorted_raw_palette$s)first_quartile_v <- round(brightness_stats$stats[2],4) sorted_raw_palette %>% filter(v > first_quartile_v) -> sorted_raw_palette outlier_threshold_v <- brightness_stats$stats[5] sorted_raw_palette %>% filter(v < outlier_threshold_v) -> sorted_raw_palette sorted_raw_palette %>% filter(s > first_quartile_s) -> sorted_raw_palette

palette optimization based on variable type

hue

dens

ity

palette optimization based on variable type

categorical

• algorithmic creation of alternative samples of hues

• evaluation of relative divergence among contiguous colours

• selection of the best sequence hue

dens

ity

palette optimization based on variable type

continuous

• hue distribution mode selection

• colours interpolation between mode and contiguous colour

hue

dens

ity

the final palette

how to use itdevtools::install_github("andreacirilloac/paletter")

library(paletter)

colours_vector <- create_palette(image_path = image_path,number_of_colors =32,type_of_variable = “categorical")

how to use itggplot(data = mtcars, aes(x = rownames(mtcars),y = hp,color = rownames(mtcars), fill = rownames(mtcars))) + geom_bar(stat = 'identity') + scale_color_manual(values = colours_vector) + scale_fill_manual(values=colours_vector)+ theme_minimal() + guides(size = FALSE) + theme(legend.position = "bottom") + labs(title = "disp vs hp")+ coord_flip()