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The purpose of the pals
package is twofold:
Memory use is reduced by compressing colormaps to fewer colors and by
calling colorRampPalette
only when a colormap is
requested.
This report gives some suggestions/recommendations for color and then gives an example of each evaluation tool.
The appearance of color depends on:
It is difficult to give definitive recommendations for the best palettes and colormaps. Nonetheless, here are some we like.
Diverging: coolwarm
/warmcool
(avoid Mach
banding in the middle).
Sequential (multi-hue): ocean.haline
,
parula
(default in Matlab).
Sequential (one hue): brewer.blues
.
Rainbow: cubicl
, kovesi.rainbow
.
Cyclical: ocean.phase
.
Categorical: brewer.paired
, stepped
Bivariate: brewer.seqseq2
.
require(pals)
pal.bands(coolwarm, parula, ocean.haline, brewer.blues, cubicl, kovesi.rainbow, ocean.phase, brewer.paired(12), stepped, brewer.seqseq2,
main="Colormap suggestions")
## Only 24 colors are available with 'stepped'
## Only 9 colors are available with 'brewer.seqseq2'
pals
packageShow palettes and colormaps as colored bands
The graphic below shows what we consider to be major flaw of the viridis palette…nearly half of the palette is more or less a green color.
require(pals)
op=par(mar=c(0,5,3,1))
labs=c('alphabet','alphabet2', 'glasbey','kelly','polychrome', 'stepped', 'stepped2', 'stepped3', 'tol', 'trubetskoy','watlington')
pal.bands(alphabet(), alphabet2(), glasbey(), kelly(),
polychrome(), stepped(), stepped2(), stepped3(),
tol(), trubetskoy(),
watlington(), labels=labs, show.names=FALSE)
Show the amount of red, green, blue, and gray in colors of a palette. The gray line corresponds to luminosity.
Show a palette with hierarchical clustering
The palette colors are converted to LUV coordinates before clustering.
A few colormaps in the pals
package are defined with
mathematical formulas (e.g. the cubehelix colormap), but most of the
colormaps are originally defined as a smooth curve through a seqence of
256 colors. There seems to be no theoretical reason for 256 colors,
other than tradition. It is natural to wonder if a smooth curve through
fewer colors would be equally sufficient. This function compresses a
colormap function down to a small-ish vector of colors that can be
passed into colorRampPalette
to re-create the original
palette with a non-noticeable difference. Most of the palettes in the
pals
package are stored as a compressed sequence of
colors.
How effective is pal.compress
? Compressing all 50
kovesi.*
colormaps reduced memory from a needlessly
wasteful 352000 bytes down to 46000 bytes, a savings of 87%.
In the figure below, the top band is the (mathematically-defined)
cubehelix
colormap function evaluated at 255 colors. These
256 colors are compressed to 17 colors in the cubebasis
vector (shown in the middle). These 17 colors are passed into the
colorRampPalette
function and expanded to 255 colors shown
in the bottom band. The maximum squared LUV distance between the
individual colors in the two bands is only 2.34, which is smaller than
the theoretical perceptual difference of roughly 2.5.
# smooth palettes are usually easy to compress
p1 <- cubehelix(255)
cubebasis <- pal.compress(cubehelix)
p2 <- colorRampPalette(cubebasis)(255)
pal.bands(p1, cubebasis, p2,
labels=c('cubehelix(255)', 'cubebasis','expanded'), main="compression of cubehelix")
## [1] 2.337919
Show a colormap with a Campbell-Robson Contrast Sensitivity Chart.
In a contrast sensitivity figure as drawn by this function, the spatial frequency increases from left to right and the contrast decreases from bottom to top. The bars in the figure appear taller in the middle of the image than at the edges, creating an upside-down “U” shape, which is the “contrast sensitivity function”. Your perception of this curve depends on the viewing distance.
The palette is converted to RGB or LUV coordinates and plotted in a three-dimensional scatterplot. The LUV space is probably better, but it is easier to tweak colors by hand in RGB space.
A random heatmap is generated (with 5% missing values) and a key is added to the heatmap by appending a blank column along the right side and then a column with the palette colors.
The graphic below shows that the alphabet2
palette is
dominated by red/pink/purple colors.
Display multiple palettes on a heatmap (similar to the ColorBrewer website).
In the example below, the tol.groundcover
palette has
several shades of green that are nearly indistinguishable.
Display a palette on a choropleth map similar to the ColorBrewer website.
A single palette/colormap is shown as five colored bands:
Show a colormap with a scatterplot
The test image shows a sine wave superimposed on a ramp of the palette. The amplitude of the sine wave is dampened/modulated from full at the top of the image to 0 at the bottom.
In the example below, the jet
colormap fails both tests,
the tol.rainbow
colormap fails to clearly show the sinewave
in the green/orange region.
This function combines several other functions into a single test image.
The examples below show the poor performance of the ‘viridis’ colormap in dark regions.
The ‘parula’ palette shows more structure in the peak of the volcano.
Some palettes with dark colors at one end of the palette hide the shape of the volcano in the dark colors.
Show a Z-order curve, coloring cells with a colormap. The difference in color between squares side-by-side is 1/48 of the full range. The difference in color between one square atop another is 1/96 the full range.
To use any colormap with the ggplot2
package, use the
ggplot2::scale_fill_gradientn()
function.
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:latticeExtra':
##
## layer
## Loading required package: reshape2
The following images show bands for all the colormaps and palettes in
the pals
package, grouped in
# Discrete
pal.bands(alphabet, alphabet2, cols25, glasbey, kelly, okabe, polychrome, stepped, stepped2, stepped3, tol, watlington,
main="Discrete", show.names=FALSE)
## Only 26 colors are available with 'alphabet'
## Only 26 colors are available with 'alphabet2'
## Only 25 colors are available with 'cols25'.
## Only 32 colors are available with 'glasbey'.
## Only 22 colors are available with 'kelly'.
## Only 8 colors are available with 'okabe'.
## Only 36 colors are available with 'polychrome'.
## Only 24 colors are available with 'stepped'
## Only 20 colors are available with 'stepped2'.
## Only 20 colors are available with 'stepped3'.
## Only 12 colors are available with 'tol'
## Only 16 colors are available with 'watlington'.
# Qualtitative
pal.bands(brewer.accent(8), brewer.dark2(8), brewer.paired(12), brewer.pastel1(9),
brewer.pastel2(8), brewer.set1(9), brewer.set2(8), brewer.set3(10),
labels=c("brewer.accent", "brewer.dark2", "brewer.paired", "brewer.pastel1",
"brewer.pastel2", "brewer.set1", "brewer.set2", "brewer.set3"),
main="Brewer qualitative")
# Sequential
pal.bands(brewer.blues, brewer.bugn, brewer.bupu, brewer.gnbu, brewer.greens,
brewer.greys, brewer.oranges, brewer.orrd, brewer.pubu, brewer.pubugn,
brewer.purd, brewer.purples, brewer.rdpu, brewer.reds, brewer.ylgn,
brewer.ylgnbu, brewer.ylorbr, brewer.ylorrd,
main="Brewer sequential")
# Diverging
pal.bands(brewer.brbg, brewer.piyg, brewer.prgn, brewer.puor, brewer.rdbu,
brewer.rdgy, brewer.rdylbu, brewer.rdylgn, brewer.spectral,
main="Brewer diverging")
# Ocean
pal.bands(ocean.thermal, ocean.haline, ocean.solar, ocean.ice, ocean.gray,
ocean.oxy, ocean.deep, ocean.dense, ocean.algae, ocean.matter,
ocean.turbid, ocean.speed, ocean.amp, ocean.tempo, ocean.phase,
ocean.balance, ocean.delta, ocean.curl, main="Ocean")
# Kovesi
op = par(mar=c(1,10,2,1))
pal.bands(kovesi.cyclic_grey_15_85_c0, kovesi.cyclic_grey_15_85_c0_s25,
kovesi.cyclic_mrybm_35_75_c68, kovesi.cyclic_mrybm_35_75_c68_s25,
kovesi.cyclic_mygbm_30_95_c78, kovesi.cyclic_mygbm_30_95_c78_s25,
kovesi.cyclic_wrwbw_40_90_c42, kovesi.cyclic_wrwbw_40_90_c42_s25,
kovesi.diverging_isoluminant_cjm_75_c23, kovesi.diverging_isoluminant_cjm_75_c24,
kovesi.diverging_isoluminant_cjo_70_c25, kovesi.diverging_linear_bjr_30_55_c53,
kovesi.diverging_linear_bjy_30_90_c45, kovesi.diverging_rainbow_bgymr_45_85_c67,
kovesi.diverging_bkr_55_10_c35, kovesi.diverging_bky_60_10_c30,
kovesi.diverging_bwr_40_95_c42, kovesi.diverging_bwr_55_98_c37,
kovesi.diverging_cwm_80_100_c22, kovesi.diverging_gkr_60_10_c40,
kovesi.diverging_gwr_55_95_c38, kovesi.diverging_gwv_55_95_c39,
kovesi.isoluminant_cgo_70_c39, kovesi.isoluminant_cgo_80_c38,
kovesi.isoluminant_cm_70_c39, kovesi.rainbow_bgyr_35_85_c72, kovesi.rainbow_bgyr_35_85_c73,
kovesi.rainbow_bgyrm_35_85_c69, kovesi.rainbow_bgyrm_35_85_c71,
main="Kovesi")
pal.bands(kovesi.linear_bgy_10_95_c74,
kovesi.linear_bgyw_15_100_c67, kovesi.linear_bgyw_15_100_c68,
kovesi.linear_blue_5_95_c73, kovesi.linear_blue_95_50_c20,
kovesi.linear_bmw_5_95_c86, kovesi.linear_bmw_5_95_c89,
kovesi.linear_bmy_10_95_c71, kovesi.linear_bmy_10_95_c78,
kovesi.linear_gow_60_85_c27, kovesi.linear_gow_65_90_c35,
kovesi.linear_green_5_95_c69, kovesi.linear_grey_0_100_c0,
kovesi.linear_grey_10_95_c0, kovesi.linear_kry_5_95_c72,
kovesi.linear_kry_5_98_c75, kovesi.linear_kryw_5_100_c64,
kovesi.linear_kryw_5_100_c67, kovesi.linear_ternary_blue_0_44_c57,
kovesi.linear_ternary_green_0_46_c42, kovesi.linear_ternary_red_0_50_c52,
main="Kovesi linear"
)
par(op)
# Bivariate
bivcol <- function(pal, nx=3, ny=3){
tit <- substitute(pal)
if(is.function(pal)) pal <- pal()
ncol <- length(pal)
if(missing(nx)) nx <- sqrt(ncol)
if(missing(ny)) ny <- nx
image(matrix(1:ncol, nrow=ny), axes=FALSE, col=pal)
mtext(tit)
}
op <- par(mfrow=c(4,4), mar=c(1,1,2,1))
bivcol(arc.bluepink)
bivcol(brewer.divbin, nx=3)
bivcol(brewer.divdiv)
bivcol(brewer.divseq)
bivcol(brewer.qualbin, nx=3)
bivcol(brewer.qualseq)
bivcol(brewer.seqseq1)
bivcol(brewer.seqseq2)
bivcol(census.blueyellow)
bivcol(stevens.bluered)
bivcol(stevens.greenblue)
bivcol(stevens.pinkblue)
bivcol(stevens.pinkgreen)
bivcol(stevens.purplegold)
bivcol(tolochko.redblue)
bivcol(vsup.redblue, nx=8)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.