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.
library("knitr") opts_chunk$set(fig.align="center", fig.width=6, fig.height=6) options(width=90)
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")
pals
packageShow palettes and colormaps as colored bands
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) par(op) pal.bands(coolwarm, viridis, parula, n=200)
Show the amount of red, green, blue, and gray in colors of a palette. The gray line corresponds to luminosity.
pal.channels(parula, main="parula")
Show a palette with hierarchical clustering
The palette colors are converted to LUV coordinates before clustering.
pal.cluster(alphabet2(), main="alphabet2")
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 352000 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. The cubebasis
vector has 17 colors (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 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") pal.maxdist(p1,p2) # 2.08
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.
pal.csf(parula, main="parula")
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.
#pal.cube(cubehelix) #pal.cube(polychrome())
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.
op <- par(mfrow=c(1,2), mar=c(1,1,2,2)) pal.heatmap(alphabet, n=26, main="alphabet") pal.heatmap(alphabet2, n=26, main="alphabet2") par(op)
Display multiple palettes on a heatmap (similar to the ColorBrewer website).
pal.heatmap2(watlington(16), tol.groundcover(14), brewer.rdylbu(11), nc=6, nr=20, labels=c("watlington","tol.groundcover","brewer.rdylbu"))
Display a palette on a choropleth map similar to the ColorBrewer website.
pal.map(brewer.paired, n=12, main="brewer.paired")
A single palette/colormap is shown as five colored bands:
pal.safe(parula, main="parula")
Show a colormap with a scatterplot
pal.scatter(polychrome, n=36, main="alphabet")
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.
pal.sineramp(parula, main="parula")
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.
op <- par(mfrow=c(3,1), mar=c(1,1,2,1)) pal.sineramp(jet, main="jet") pal.sineramp(tol.rainbow, main="tol.rainbow") pal.sineramp(kovesi.rainbow, main="kovesi.rainbow") par(op)
This function combines several other functions into a single test image.
The examples below show the superiority of the parula
colormap as compared to the viridis
colormap.
The examples below show the poor performance of the 'viridis' colormap in dark regions. The 'parula' palette shows more structure in the volcano.
pal.test(parula) pal.test(viridis) # dark colors are poor
Some palettes with dark colors at one end of the palette hide the shape of the volcano in the dark colors.
pal.volcano(parula) pal.volcano(viridis)
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.
pal.zcurve(parula, main="parula")
To use any colormap with the ggplot2
package, use the ggplot2::scale_fill_gradientn()
function.
require(ggplot2) require(pals) require(reshape2) ggplot(melt(volcano), aes(x=Var1, y=Var2, fill=value)) + geom_tile() + scale_fill_gradientn(colours=coolwarm(100), guide = "colourbar")
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) # Misc pal.bands(coolwarm,warmcool,cubehelix,gnuplot,jet,parula,tol.rainbow,cividis) # Niccoli pal.bands(cubicyf,cubicl,isol,linearl,linearlhot, main="Niccoli") # 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") # Matplotlib pal.bands(magma, inferno, plasma, viridis, main="Matplotlib") # 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) par(op)
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