Graphic Design with ggplot2

Working with Colors

Cédric Scherer // rstudio::conf // July 2022

Setup

library(tidyverse)

library(tidyverse)

bikes <- readr::read_csv(
  here::here("data", "london-bikes-custom.csv"), 
  col_types = "Dcfffilllddddc"
)

bikes$season <- forcats::fct_inorder(bikes$season)

theme_set(theme_light(base_size = 14, base_family = "Roboto Condensed"))

theme_update(
  panel.grid.minor = element_blank(),
  plot.title = element_text(face = "bold"),
  legend.position = "top",
  plot.title.position = "plot"
)

Color Palettes

Pre-Defined Color Palettes: Viridis

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_viridis_d(
    option = "plasma",
    begin = .3
  )

Pre-Defined Color Palettes: Viridis

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = month)
  ) +
  geom_boxplot() +
  scale_fill_viridis_d(
    option = "plasma",
    begin = .3
  )

Pre-Defined Color Palettes: Viridis

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_viridis_c(
    option = "plasma",
    end = .9
  )

Pre-Defined Color Palettes: Viridis

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_viridis_c(
    option = "plasma",
    end = .9,
    direction = -1
  )

Pre-Defined Color Palettes: Brewer

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_brewer(
    palette = "Set1"
  )

Pre-Defined Color Palettes: Brewer

RColorBrewer::display.brewer.all()

Pre-Defined Color Palettes: Brewer

RColorBrewer::display.brewer.all(colorblindFriendly = TRUE)

{rcartocolor}

# install.packages("rcartocolor")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  rcartocolor::scale_fill_carto_d(
    palette = "Vivid" 
  )

{rcartocolor}

rcartocolor::display_carto_all()

{rcartocolor}

rcartocolor::display_carto_all(colorblind_friendly = TRUE)

{scico}

# install.packages("scico")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scico::scale_fill_scico_d(
    palette = "hawaii"
  )

{scico}

scico::scico_palette_show()

{ggsci} and {ggthemes}

# install.packages("ggsci")
ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  ggsci::scale_fill_npg()

# install.packages("ggthemes")
ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  ggthemes::scale_fill_gdocs()

{nord}

# install.packages("nord")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  nord::scale_fill_nord(
    palette = "aurora"
  )

{nord}

# install.packages("nord")

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  nord::scale_color_nord(
    palette = "silver_mine",
    discrete = FALSE
  )

{MetBrewer}

# install.packages("MetBrewer")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  MetBrewer::scale_fill_met_d(
    name = "Klimt"
  )

{MetBrewer}

MetBrewer::display_all()

{MetBrewer}

MetBrewer::display_all(colorblind_only = TRUE)

{MetBrewer}

# install.packages("MetBrewer")

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  MetBrewer::scale_color_met_c(
    name = "Hiroshige" 
  )

Customize Palettes

Customize Existing Palettes

library("rcartocolor")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  rcartocolor::scale_fill_carto_d(
    name = "Vivid" 
  )

Customize Existing Palettes

library("rcartocolor")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_manual(
    values = carto_pal(
      name = "Vivid", n = 4
    )
  )

Customize Existing Palettes

library("rcartocolor")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_manual(
    values = carto_pal(
      name = "Vivid", n = 5
    )[1:4]
  )

Customize Existing Palettes

library("rcartocolor")

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_manual(
    values = carto_pal(
      name = "Vivid", n = 6
    )[c(1, 3:5)]
  )

Customize Existing Palettes

carto_custom <- 
  carto_pal(
    name = "Vivid", n = 6
  )[c(1, 3:5)]

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_manual(
    values = carto_custom
  )

Customize Existing Palettes

# install.packages("colorspace")
library(colorspace)

carto_light <- lighten(carto_custom, .8)

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_manual(
    values = carto_light
  )

Customize Existing Palettes

ggplot(
    bikes, 
    aes(x = day_night, y = count)
  ) +
  geom_boxplot(
    aes(fill = season,
        fill = after_scale(
          lighten(fill, .8)
    ))
  ) +
  scale_fill_manual(
    values = carto_custom
  )

Customize Existing Palettes

ggplot(
    bikes, 
    aes(x = day_night, y = count)
  ) +
  geom_boxplot(
    aes(
      fill = stage(
        season,
        after_scale =
          lighten(fill, .8)
      )
    )
  ) +
  scale_fill_manual(
    values = carto_custom
  )

Customize Existing Palettes

ggplot(
    bikes, 
    aes(x = day_night, y = count)
  ) +
  geom_boxplot(
    aes(color = season,
        fill = after_scale(
          lighten(color, .8)
    ))
  ) +
  scale_color_manual(
    values = carto_custom
  )

Customize Existing Palettes

ggplot(
    bikes, 
    aes(x = day_night, y = count)
  ) +
  geom_boxplot(
    aes(color = season,
        fill = after_scale(
          lighten(color, .8)
    ))
  ) +
  geom_jitter(
    aes(color = season), 
    position = position_jitterdodge(
      dodge.width = .75, 
      jitter.width = .2
    ),
    alpha = .4
  ) +
  scale_color_manual(
    values = carto_custom
  )

Customize Existing Palettes

ggplot(
    bikes, 
    aes(x = day_night, y = count)
  ) +
  geom_boxplot(
    aes(color = season,
        fill = after_scale(
          lighten(color, .8)
        ))
  ) +
  geom_jitter(
    aes(color = season,
        color = after_scale(
          darken(color, .3)
    )), 
    position = position_jitterdodge(
      dodge.width = .75, 
      jitter.width = .2
    ),
    alpha = .4
  ) +
  scale_color_manual(
    values = carto_custom
  )

Customize Existing Palettes

ggplot(
    bikes, 
    aes(x = day_night, y = count)
  ) +
  geom_boxplot(
    aes(color = season,
        fill = after_scale(
          lighten(color, .8)
        ))
  ) +
  geom_jitter(
    aes(color = season,
        color = after_scale(
          darken(color, .3)
        )), 
    position = position_jitterdodge(
      dodge.width = .75, 
      jitter.width = .2
    ),
    alpha = .4
  ) +
  scale_color_manual(
    values = carto_custom
  )

Create New Palettes

Create Sequential Palettes

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_gradient(
    low = "#28A87D",
    high = "#FFD166"
  )

Create Diverging Palettes

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_gradient2(
    low = "#663399",
    high = "#993334",
    mid = "grey95"
  )

Create Diverging Palettes

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_gradient2(
    low = "#663399",
    high = "#993334",
    mid = "grey92",
    midpoint = 10    
  )

Create Diverging Palettes

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_gradient2(
    low = "#663399",
    high = "#993334",
    mid = "grey92",
    midpoint = 10,
    limits = c(-10, 30)   
  )

Create Any Palette

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_gradientn(
    colors = carto_custom  
  )

Create Any Palette

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_gradientn(
    colors = carto_custom,
    values = c(0, .2, .8, 1)
  )

Build Your Own
scale_color|fill_*()

Illustration by W. E. B. Du Bois, Courtesy Library of Congress

Build Your Own scale_color|fill_*()

dubois_colors <- function(...) {
  dubois_cols <- c(
    `black`    = "#000000",
    `purple`   = "#582f6c",
    `violet`   = "#94679C",
    `pink`     = "#ef849f",
    `softred`  = "#f4b7a7",
    `iceblue`  = "#bccbf3",
    `palegrey` = "#e4e4e4"
  )

  cols <- c(...)

  if (is.null(cols))
    return (dubois_cols)

  dubois_cols[cols]
}

dubois_colors("black", "pink", "softred", "iceblue")

    black      pink   softred   iceblue 
"#000000" "#ef849f" "#f4b7a7" "#bccbf3" 

Build Your Own scale_color|fill_*_d()

dubois_pal_d <- function(palette = "default", reverse = FALSE) {
  function(n) {
    if(n > 5) stop('Palettes only contains 5 colors')

    if (palette == "default") { pal <- dubois_colors("black", "violet", "softred", "iceblue", "palegrey")[1:n] }
    if (palette == "dark") { pal <- dubois_colors(1:5)[1:n] }
    if (palette == "light") { pal <- dubois_colors(3:7)[1:n] }
    
    pal <- unname(pal)

    if (reverse) rev(pal) else pal
  }
}

dubois_pal_d()(3)

[1] "#000000" "#94679C" "#f4b7a7"

Build Your Own scale_fill|color_*_d()

scale_color_dubois_d <- function(palette = "default", reverse = FALSE, ...) {
  if (!palette %in% c("default", "dark", "light")) stop('Palette should be "default", "dark" or "light".')

  pal <- dubois_pal_d(palette = palette, reverse = reverse)

  ggplot2::discrete_scale("colour", paste0("dubois_", palette), palette = pal, ...)
}

scale_fill_dubois_d <- function(palette = "default", reverse = FALSE, ...) {
  if (!palette %in% c("default", "dark", "light")) stop('Palette should be "default", "dark" or "light".')

  pal <- dubois_pal_d(palette = palette, reverse = reverse)

  ggplot2::discrete_scale("fill", paste0("dubois_", palette), palette = pal, ...)
}

Use Your Own scale_fill_*_d()

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_dubois_d()

Use Your Own scale_color_*_d()

ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        color = season)
  ) +
  geom_boxplot() +
  scale_color_dubois_d(
    palette = "dark",
    reverse = TRUE
  )

Test Your Palettes

Emulate CVD

deut <- 
  colorspace::deutan(
    viridis::turbo(
      n = 100, direction = -1
    )
  )

ggplot(
    bikes, 
    aes(x = temp_feel, y = count,
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_gradientn(
    colors = deut
  )

Emulate CVD

g <- 
  ggplot(
    bikes, 
    aes(x = day_night, y = count, 
        fill = season)
  ) +
  geom_boxplot() +
  scale_fill_manual(
    values = carto_custom
  )

g

Emulate CVD

# devtools::install_github(
#   "clauswilke/colorblindr"
# )

colorblindr::cvd_grid(g)

Emulate CVD

Emulate CVD

# devtools::install_github("clauswilke/colorblindr")

colorblindr::cvd_grid(g2)

Emulate CVD

Emulate CVD

# devtools::install_github("clauswilke/colorblindr")

colorblindr::cvd_grid(g3)

Recap

• use categorical palettes for qualitative data
  • e.g. scale_*_discrete() and scale_*_manual() for custom options
  • e.g. scale_*_viridis_d and scale_*_brewer() for pre-defined options
• use sequential or diverging palettes for quantitative data
  • e.g. scale_*_gradient() or scale_*_gradient2() for custom options
  • e.g. scale_*_viridis_c and scale_*_distiller() for pre-defined options
• various packages provide palettes incl. scale_* components
  • e.g. {rcartocolors}, {scico}, {ggsci}, {ggthemes}, {nord}
• those and even more packages return palettes as vectors
  • modify and supply them to scale_*_manual() and scale_*_gradientn()
• use after_scale to modify and recycle color scales

Exercise

Exercise

• Create a similar visualization as close as possible:

Appendix

HCL Spectrum

Evaluate HCL Spectrum

colorspace::specplot(
  colorspace::diverging_hcl(
    n = 100, palette = "Blue-Red"
  )
)

Evaluate HCL Spectrum

colorspace::specplot(
  scico::scico(
    n = 100, palette = "hawaii"
  )
)

colorspace::specplot(
  MetBrewer::met.brewer(
     n = 100, name = "Hiroshige"
  )
)

Evaluate HCL Spectrum

colorspace::specplot(
  rcartocolor::carto_pal(
    n = 7, name = "TealRose"
  )
)

colorspace::specplot(
  MetBrewer::met.brewer(
     n = 100, name = "Cassatt2"
  )
)

Evaluate HCL Spectrum

colorspace::specplot(
  rainbow(
    n = 100
  )
)

colorspace::specplot(
  viridis::turbo(
     n = 100, direction = -1
  )
)

Build Your Own scale_color|fill_*_c()

dubois_pal_c <- function(palette = "dark", reverse = FALSE, ...) {
  dubois_palettes <- list(
    `dark`    = dubois_colors("black", "purple", "violet", "pink"),
    `light`   = dubois_colors("purple", "violet", "pink", "palered")
  )

  pal <- dubois_palettes[[palette]]
  pal <- unname(pal)

  if (reverse) pal <- rev(pal)

  grDevices::colorRampPalette(pal, ...)
}

dubois_pal_c(palette = "light", reverse = TRUE)(3)

[1] "#FFFFFF" "#C1759D" "#582F6C"

Build Your Own scale_color|fill_*_c()

scale_fill_dubois_c <- function(palette = "dark", reverse = FALSE, ...) {
  if (!palette %in% c("dark", "light")) stop('Palette should be "dark" or "light".')

  pal <- dubois_pal_c(palette = palette, reverse = reverse)

  ggplot2::scale_fill_gradientn(colours = pal(256), ...)
}

scale_color_dubois_c <- function(palette = "dark", reverse = FALSE, ...) {
  if (!palette %in% c("dark", "light")) stop('Palette should be "dark" or "light".')

  pal <- dubois_pal_c(palette = palette, reverse = reverse)

  ggplot2::scale_color_gradientn(colours = pal(256), ...)
}

Use Your Own scale_color|fill_*_c()

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_dubois_c()

Use Your Own scale_color|fill_*_c()

ggplot(
    bikes, 
    aes(x = temp_feel, y = count, 
        color = temp_feel)
  ) +
  geom_point() +
  scale_color_dubois_c(
    palette = "light",
    reverse = TRUE
  )