Overview

The flexible specification of HCL-based color palettes in colorspace allows to closely approximate color palettes from various other packages:

  • ColorBrewer.org (Harrower and Brewer 2003) as provided by R package RColorBrewer (Neuwirth 2014).
  • CARTO colors (CARTO 2019) as provided by R package rcartocolor (Nowosad 2018).
  • The viridis palettes of Smith and Van der Walt (2015) developed for matplotlib, as provided by R package viridis (Garnier 2018).
  • The scientific color maps of Crameri (2018) as provided by R package scico (Pedersen and Crameri 2018).

See the discussion of HCL-based palettes for more details. In the following sections specplot() is used to compare the HCL spectrum of the original palettes (top swatches, solid lines) and their HCL-based approximations (bottom swatches, dashed lines).

Before, a selection of such approximations using specplot() is highlighted and discussed in some more detail. Specifically, the graphic below shows two blue/green/yellow palettes (RColorBrewer::brewer.pal(7, "YlGnBu") and viridis::viridis(7)) and two purple/red/yellow (rcartocolor::carto_pal(7, "ag_Sunset") and viridis::plasma(7)). Each panel compares the hue, chroma, and luminance trajectories of the original palettes (top swatches, solid lines) and their HCL-based approximations (bottom swatches, dashed lines). The palettes are not identical but very close for most colors. Note that also the chroma trajectories from the HCL palettes (green dashed lines) have some kinks which are due to fixing HCL coordinates at the boundaries of admissible RGB colors.

These graphics illustrate what sets the viridis palettes apart from other sequential palettes. While the hue and luminance trajectories of "Viridis" and "YlGnBu" are very similar, the chroma trajectories differ: While lighter colors (with high luminance) have low chroma for "YlGnBu", they have increasing chroma for "Viridis". Similarly, "ag_Sunset" and "Plasma" have similar hue and luminance trajectories but different chroma trajectories. The result is that the viridis palettes have rather high chroma throughout which does not work as well for sequential palettes on a white/light background as all shaded areas convey high “intensity”. However, they work better on a dark/black background. Also, they might be a reasonable alternative for qualitative palettes when grayscale printing should also work.

Another somewhat nonstandard palette from the viridis family is the cividis palette based on blue and yellow hues and hence safe for red-green deficient viewers. The figure below shows the corresponding specplot() along with an HCL-based approximation. What is unusual about this palette: The hue and chroma trajectories would suggest a diverging palette, as there are two “arms” wth different hues and a zero-chroma point in the center. However, the luminance trajectory clearly indicates a sequential palette as colors go monotonically from dark to light. Due to this unusual mixture the palette cannot be composed using the trajectories discussed in the construction details.

However, the tools in colorspace can still be employed to easily reconstruct the palette. One strategy would be to set up the trajectories manually, using a linear luminance, piecewise linear chroma, and piecewise constant hue:

cividis_hcl <- function(n) {
  i <- seq(1, 0, length.out = n)
  hex(polarLUV(
    L = 92 - (92 - 13) * i,
    C = approx(c(1, 0.9, 0.5, 0), c(30, 50, 0, 95), xout = i)$y,
    H = c(255, 75)[1 + (i < 0.5)]
  ), fix = TRUE)
}

Instead of constructing the hex code from the HCL coordinates via colorspace’s hex(polarLUV(L, C, H)), the base R function hcl(H, C, L) from grDevices could also be used.

In addition to manually setting up a dedicated function cividis_hcl(), it is possible to approximate the palette using divergingx_hcl(), e.g.,

This uses a slight power transformation with p1 = 1 in the blue arm of the palette but otherwise essentially corresponds to what cividis_hcl() does. For convenience divergingx_hcl(n, palette = "Cividis") is preregistered using the above parameters.

Finally, we compare the flexible diverging “Temps” palette, originally from CARTO, and the “Zissou 1” palette from the wesanderson (Ram and Wickham 2018) package. Both employ a similar hue trajectory going from blue/green via yellow to orange/red. Also, the luminance trajectory is similar but for “Temps” this is more balanced and provides a stronger luminance contrast. The chroma trajectory is rather unbalanced in both palettes but for “Zissou 1” much more so, leading to very high-chroma colors throughout. Thus, both palettes are more suitable for palettes with fewer colors but in “Zissou 1” this issue is more pronounced.

Approximations of ColorBrewer.org palettes

demo("brewer", package = "colorspace")

Approximations of CARTO palettes

demo("carto", package = "colorspace")

Approximations of viridis palettes

demo("viridis", package = "colorspace")

Approximations of Crameri’s scientific color (scico) palettes

demo("scico", package = "colorspace")

References

CARTO. 2019. “CARTOColors – Data-Driven Color Schemes.” https://carto.com/carto-colors/.

Crameri, Fabio. 2018. “Geodynamic Diagnostics, Scientific Visualisation and Staglab 3.0.” Geoscientific Model Development 11 (6): 2541–62. https://doi.org/10.5194/gmd-11-2541-2018.

Garnier, Simon. 2018. Viridis: Default Color Maps from Matplotlib. https://CRAN.R-project.org/package=viridis.

Harrower, Mark A., and Cynthia A. Brewer. 2003. “ColorBrewer.org: An Online Tool for Selecting Color Schemes for Maps.” The Cartographic Journal 40: 27–37. http://ColorBrewer.org/.

Neuwirth, Erich. 2014. RColorBrewer: ColorBrewer Palettes. https://CRAN.R-project.org/package=RColorBrewer.

Nowosad, Jakub. 2018. Rcartocolor: “CARTOColors” Palettes. https://CRAN.R-project.org/package=rcartocolor.

Pedersen, Thomas Lin, and Fabio Crameri. 2018. Scico: Colour Palettes Based on the Scientific Colour-Maps. https://CRAN.R-project.org/package=scico.

Ram, Karthik, and Hadley Wickham. 2018. Wesanderson: A Wes Anderson Palette Generator. https://CRAN.R-project.org/package=wesanderson.

Smith, Nathaniel, and Stéfan Van der Walt. 2015. “A Better Default Colormap for Matplotlib.” In SciPy 2015 – Scientific Computing with Python. Austin. https://www.youtube.com/watch?v=xAoljeRJ3lU.