In allopole/datacolor: Color for Data Visualization

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Color for Data Visualization

Table of Contents

• Introduction
• Installation
• Analysis and display of palettes
• Converters
• Hex RGB to HCL
• Hex RGB palette to simulated colorblind hex RGB
• Precalculated palettes
• 7 category qualitative palette for nominal data
• Sequential palette for heatmaps
  • Comparison of Heat Palettes
• Divergent colorscale for AUC heatmaps
• HCL Palettes with power curves, stepped and cyclic elements
• Multihue HCL Palettes with bezier interpolation and lightness correction
• Comparison with non-bezier multi-hue palettes

Introduction

This package provides

• prebuilt color palettes appropriate for data representation,
• functions for defining color palettes for data representation,
• utilities for analysis of palettes as perceived by normal and colorblind viewers.

Color palettes for data should ideally be perceptually linear in all perceptual color dimensions (hue, chroma or saturation, lightness or luminance). Ideally, palettes should aslo be color-blind safe (i.e. they should preserve an appropriate mapping of data to perceived color for colorblind viewers). Palettes may also need to be black and white printer-friendly, if there is a chance plots will be printed in greyscale. Additional requirements for color scales can include the presence or absence of discontinuities and corners, maximizing discrimination between levels, and respecting semantic relationships between colors.

This package relies on the perceptually flat colorspace HCL, which represents color as preceived by people with normal vision. HCL functions from the grDevices and colorspace packages. This package also relies on the dichromat R package to simulate color perception under the three most common forms of colorblindness.

Installation

Install the datacolor package from github:

# install.packages("devtools")
# devtools::install_github("allopole/datacolor")

Analysis and display of palettes

See ?colorbar for description of colorbar and colorplot functions

library(datacolor)

# colorbar
pal <- grDevices::rainbow(36)
colorbar(pal)
colorbar(pal,dots=TRUE)
colorbar(pal,colorblind=TRUE)
colorbar(pal,dots=TRUE,colorblind=TRUE)

# colorplot
colorplot(pal)
colorplot(pal,dots=TRUE)
colorplot(pal,colorblind=TRUE)
colorplot(pal,dots=TRUE,colorblind=TRUE)

Converters

Hex RGB to HCL

See ?hex2hcl

pal <- grDevices::rainbow(7)
hex2hcl(pal)

Hex RGB palette to simulated colorblind hex RGB

See ?colorblind

pal <- grDevices::rainbow(7)
colorblind(pal)
colorplot(colorblind(pal)$deuteranopia)

Precalculated palettes

7 category qualitative palette for nominal data

See ?unipalette for details

• designed for high contrast against a white background, with good distinction among colors
• colors distinguishable under two most common forms of colorblindness
• colors distinguishable in greyscale for b/w printing
• Good for bar charts, box plots, pies, etc.
• Line should be relatively thick
• Points should be relatively large

colorplot(unipalette(),colorblind = TRUE)

## Sample plot
randomseries <- data.frame(x=1:4, y=runif(4*7), grp=rep(letters[1:7],each=4))
pal <- unipalette()
lattice::xyplot(y~x, group=grp, data=randomseries, type="b", pch=16, lwd=3, col = pal,
      key = list(space = "right", lines = list(col=pal, lwd=3), text=list(letters[1:7]))
      )

Sequential palette for heatmaps

A replacement for grDevices::heat.colors() or colorspace::heat_hcl()

See ?heat for details

Palette characteristics:

• perceptually linear
• color-blind-safe (under three most common forms of colorblindness) and print friendly
• under normal vision: linear lightness, chroma, and hue gradients
• under colorblind viewing, linear lightness, smooth chroma, flat hue gradients
• appropriate for display of temperature, intensity, etc.

colorplot(datacolor::heat(128),colorblind = T)
mtext("datacolor::heat()", side=1, line = 2.5)

Comparison of Heat Palettes

n <- 128

colorplot(grDevices::heat.colors(n), colorblind = TRUE)
mtext("grDevices::heat.colors(128)", side=1, line = 2.5)

colorplot(viridis::inferno(128), colorblind = TRUE)
mtext("viridis::inferno(128)", side=1, line = 2.5)

colorplot(colorspace::heat_hcl(n), colorblind = TRUE)
mtext("colorspace::heat_hcl()", side=1, line = 2.5)

colorplot(datacolor::heat(128),colorblind = T)
mtext("datacolor::heat()", side=1, line = 2.5)

## Maroon to yellow through salmon and pink
stops <- c('#800000', '#CD4949', '#FF9696','#FFFF00')
palette <- multiHue(128,colors=stops)
colorplot(palette,colorblind = TRUE) # display palette
mtext("datacolor::multiHue(128,c('#800000', '#CD4949', '#FF9696','#FFFF00'))", side=1, line = 2.5)

Divergent colorscale for AUC heatmaps

See ?AUColors for details

Palette characteristics:

• color-blind-safe (under three most common forms of colorblindness)
• perceptually linear
• equal brightness gradients on both sides of central value
• constant hue and chroma on either side of central value
• Dark value for no correlation (near 0.5); bright values for high correlation (near 1 and 0)
• Blue for positive correlation (1); orange for negative correlation (0)
• NOT suitable for b/w printing, as low and high values are not distinguishable in greyscale (however, greyscale printing still accurately shows the absolute distance from 0.5)

## default palette (n = 10):
p <- AUColors()
colorplot(p,colorblind = TRUE)

## inverted and reversed, with grey center bin:
p <- AUColors(n=21,invert=TRUE,reverse=TRUE)
colorplot(p,colorblind = TRUE)

# Sample AUC Heat Map
randommatrix <- matrix(runif(12*6),ncol=6)
nlevels <- 10 
lattice::levelplot(randommatrix,
  at = seq(0,1,1/nlevels), # number of breaks
  col.regions = AUColors(nlevels), # color map
  aspect = "iso")

HCL Palettes with power curves, stepped and cyclic elements

Use rampx(), stepx(), and cyclx() to construct numeric vectors of H, C, and L.

See ?cyclx for description of rampx(), stepx(), and cyclx().

Use hcl2hex() to convert HCL palette to a hex RGB palette.

Depricated: cyclic_hcl

library(datacolor)

n <- 36

pal <- hcl2hex(
  H=rampx(from=260,to=-100,n=n),
  C=stepx(from=40,to=60,n=n,step.n=n/3),
  L=100*cyclx(from=.75,to=.45,n=n,
              step.n=n/3,exponent=1,
              cyc.from=.8,cyc.to=1,cyc.n=n/3,cyc.exponent=2.5)
  )

colorplot(pal)

Multihue HCL Palettes with bezier interpolation and lightness correction

multihue(), multihue.diverge() and multihue.constantL() create multi-hue (multi "stop") color scales with perceptually linear lightness gradients and smooth hue gradients (with no first or second order discontinuities).

The palettes follow bezier curves through HCL space. The bezier curves are defined by two endpoint colors and one or more optional intermediate color "stops." The intermediate stops are treated as control points for the bezier curve, and are therefore not necessarily present in the final palette.

After interpolation, the palettes are stretched to counter any non-linearity in lightness gradient arising from the presence of intermediate color stops. The resulting palette has a perceptually linear lightness gradient.

multihue.diverge() builds a divergent palette by concatenating two bezier curves (defined by a "left" and "right" set of color stops).

See ?multiHue for description of multiHue(), multiHue.diverge() and multiHue.constantL()

For a discussion of bezier interpolation in HCL with lightness correction, see:

Gregor Aisch. Mastering Multi-hued Color Scales with Chroma.js. Blog post. Sep 9, 2013 https://www.vis4.net/blog/2013/09/mastering-multi-hued-color-scales/

Palette characteristics:

• perceptually linear lightness gradients (or constant lightness for multihue.constantL() )
• smooth hue and chroma gradient throughout (no "corners")
• multiple color stops allow increased color discrimination between levels
• hue and chroma gradients not necessarily linear. Only brightness is adjusted for linearity.
• multihue(), multihue.diverge() produce print-friendly palettes.
• multihue.diverge() may produce a palette with a central discontinuity (first or second order)

## default = blue to yellow through green, 11 colors
p <- multiHue()
colorplot(p,colorblind=TRUE)
colorplot(p)

## Maroon to yellow through salmon and pink
stops <- c('#800000', '#CD4949', '#FF9696','#FFFF00')
palette <- multiHue(128,colors=stops)
colorplot(palette, colorblind=TRUE) # display palette

## Divergent (default)
p <- multiHue.diverge(128)
colorplot(p,colorblind=TRUE)

## Divergent with discontinuity: left = Blue to Cyan, right = Yellow to Red
leftstops <- c("#3136AA","#3980B0","#00C5C0","#8EFDFD")
rightstops <- c("#F3F300","#FF8CB4","#CD4C4C","#8F0000")
palette <- multiHue.diverge(128,leftstops,rightstops,continuous=FALSE)
colorplot(palette,colorblind=TRUE)

## Constant Lightness (and chroma) color palette:
## 4 color stops define the bezier curve through HCL space; n=6 colors are output.
palette <- multiHue.constantL(n=64, h=c(-180, -105, -45, 0),c=50,l=60)
colorplot(palette,colorblind=TRUE)

Comparison with non-bezier multi-hue palettes

By comparison, here is the "rainbow" palette showing discontinuities typical of multi-hule palettes. This palette includes corners (second order discontinuities) in lightness. From a perceptual point of view, this palette is not a continuous colors scale, but a multiply-diverging scale with three distinct articulation points (at 1/6, 1/2 and 5/6 of the palette length).

pal <- grDevices::rainbow(128)
colorplot(pal,colorblind=TRUE)

allopole/datacolor documentation built on May 4, 2019, 6:39 p.m.