Color Functions

Sample functions and ideas for accessing the R built-in colors. Further examples on converting soil colors to RGB triplets, or for the selection of optimal colors for a thematic map please see the examples linked at the bottom of this page. An excellent discussion on the use of color for presenting scientific data is presented in this paper by Zeileis, Achim and Hornik, Kurt.

R Color Selection: Simple figure illustrating the layout() function to create a plot of the built-in R colors palettes.

Simple Color Display

#make a color wheel

pie(rep(1,12), col=rainbow(12))

#make a list of the common color palettes:

demo.pal <- function(n, border = if (n<32) "light gray" else NA,

main = paste("color palettes;  n=",n),

ch.col = c("rainbow(n, start=.7, end=.1)", "heat.colors(n)",

"terrain.colors(n)", "topo.colors(n)", "cm.colors(n)"))

{

        nt <- length(ch.col)

        i <- 1:n; j <- n / nt; d <- j/6; dy <- 2*d

        plot(i,i+d, type="n", yaxt="n", ylab="", main=main)

        for (k in 1:nt) {

                rect(i-.5, (k-1)*j+ dy, i+.4, k*j,

                        col = eval(parse(text=ch.col[k])), border = border)

                text(2*j,  k * j +dy/4, ch.col[k])

        }

}

n <- if(.Device == "postscript") 64 else 16

        # Since for screen, larger n may give color allocation problem

demo.pal(n)

A Queryable color picker (as suggested by Gabor Grothendieck on the R-help mailing list)

#make a color lookup function

getColorName <- function(colorNumber) colors()[colorNumber]

# pch = NA means no points plotted.  pch = 20 plots small dots.

# n is the number of points to identify interactively with mouse

printColorSampler <- function(n = 0, pch = NA, bg = "white") {

   i <- seq(colors())

   k <- ceiling(sqrt(length(i)))

   xy <- cbind(floor(i/k)*2, i %% k)

   opar <- par(bg = bg)

   on.exit(par = opar)

   plot(xy, axes = FALSE, xlab = "", ylab = "", pch=pch, col=colors())

   text(xy[,1]+.5, xy[,2]+.2, i, col = colors(), cex = 0.7)

   if (n > 0)

      colors()[identify(xy, n = n, labels = colors(), plot = FALSE)]

}

# test

printColorSampler(0)

printColorSampler(1)

printColorSampler(pch=20, bg="black")

Setup the plot layout, and plot both examples

#setup the layout, and print pane boundaries:

nf <- layout(matrix(c(1,1,2,2), 2, 2, byrow=FALSE), respect=TRUE, widths=c(1,2)) ; layout.show(nf)

#plot the pie chart:

pie(rep(1,12), col=rainbow(12))

#plot the palette chart:

demo.pal(n)

#save a copy to an EPS file:

dev.copy2eps()

Convert Munsell colors to computer-friendly RGB triplets

Soil color conversion: Munsell in LUV colorspace

Figure 1: Munsell color chips.

Figure 2: Common soil colors.

Figure 3: Commom soil colors in RGB.

Soil color conversion: soil color matrix

Figure 4: Soil colors in multiple color spaces

Figure 5: Soil profile colors.

The Munsell color system was designed as a series of discrete color chips which closely approximation to the color sensitivity of the human eye. The description of color via three variables tied to perceptible properties (hue, value, and chroma) under a standardized illuminant (sunlight on a clear day) makes the Munsell system a good choice for recording and interpreting soil color data. However, numerical analysis of colors encoded in the Munsell system is difficult because they are from a discrete set of color chips and referenced by values that include both letters and numbers. Rossel et. al. (2006) give a good background on various color space models and their relative usefulness in the realm of soil science. The conversion of Munsell soil colors to RGB triplets, suitable for displaying on a computer screen or printing, is made complicated by the numerous operations involved in converting between color spaces. Figure 1 shows all possible (both real and unreal) Munsell color chips in the L*U*V color space. Figure 2 shows some of the common soil color chips in the same color space. Figures 2 through 5 depict common soil colors in the RGB color space, visualized both in R and POVRAY. Example R code on the conversion is given below.

Munsell color data can be downloaded here.

Color conversion equations here.

References:

Rossel, R.A.V.; Minasny, B.; Roudier, P. & McBratney, A.B. Colour space models for soil science Geoderma, 2006, 133, 320-337.

Manual Conversion in R

Setup environment and load lookup table data

## load some libs

library(plotrix)

library(colorspace)

## munsell data comes with a lookup table in xyY colorspace 

## url: http://www.cis.rit.edu/mcsl/online/munsell.php

## note:

## Munsell chroma, CIE x, y, and Y. The chromaticity coordinates were calculated using illuminant C and the CIE 1931 2 degree observer.

all <- read.table("munsell-all.dat", header=T)

Convert xyY to XYZ [Equation Reference]

## x and y are approx (0,1)

## Y is approx (0,100)

## need manually rescale Y to (0,1)

all$Y <- all$Y/100.0

## do the conversion

X <- (all$x * all$Y ) / all$y

Y <- all$Y

Z <- ( (1- all$x - all$y) * all$Y )  / all$y

## combine to form matrix for simple manipulation

mun_XYZ_C <- matrix(c(X,Y,Z), ncol=3)

## test for y == 0

## X,Y,Z should then be set to 0

mun_XYZ_C[which(all$y==0),] <- c(0,0,0)

Perform Chromatic Adaption Functions in the colorspace package, and sRGB profiles assume a D65 illuminant [Reference]

## conversion matrix, from reference above

## this has been revised as of Jan, 2008 

M_adapt_C_to_D65 <- matrix(c(0.990448, -0.012371, -0.003564, -0.007168, 1.015594, 0.006770, -0.011615, -0.002928, 0.918157), ncol=3, byrow=TRUE)

## perform the chromatic adaption: convert from C -> D65 using Bradford method

mun_XYZ_D65 <- mun_XYZ_C %*% M_adapt_C_to_D65

## how different are the two?

summary( (mun_XYZ_D65 - mun_XYZ_C)  )

Convert XYZ (D65) to sRGB (D65), step 1 this assumes that XYZ is scaled to (0,1) [Reference Primaries for sRGB]

## first get the reference primaries transformation matrix from above

##

## sRGB profile transformation:

M_XYZ_to_sRGB_D65 <- matrix(c(3.24071, -0.969258, 0.0556352, -1.53726, 1.87599, -0.203996, -0.498571, 0.0415557, 1.05707), ncol=3, byrow=TRUE)

## apply the conversion matrix

mun_sRGB_D65 <- mun_XYZ_D65 %*% M_XYZ_to_sRGB_D65

Convert XYZ (D65) to sRGB (D65), step 2 (sRGB, gamma = 2.4) [Conversion Function to sRGB]

## define the transformation functions: 

## these are applied on a conditional basis:

fun1 <- function(col_comp) { 1.055 * ( col_comp ^ ( 1 / 2.4 ) ) - 0.055 } 

fun2 <- function(col_comp) { 12.92 * col_comp } 

## the specific function is contingent on the absolute value of r,g,b components

R <- ifelse(mun_sRGB_D65[,1] > 0.0031308, fun1(mun_sRGB_D65[,1]), fun2(mun_sRGB_D65[,1]))  

G <- ifelse(mun_sRGB_D65[,2] > 0.0031308, fun1(mun_sRGB_D65[,2]), fun2(mun_sRGB_D65[,2]))  

B <- ifelse(mun_sRGB_D65[,3] > 0.0031308, fun1(mun_sRGB_D65[,3]), fun2(mun_sRGB_D65[,3]))  

##clip values to range {0,1}

R_clip <- ifelse(R < 0, 0, R)  

G_clip <- ifelse(G < 0, 0, G)  

B_clip <- ifelse(B < 0, 0, B)  

R_clip <- ifelse(R > 1, 1, R_clip)  

G_clip <- ifelse(G > 1, 1, G_clip)  

B_clip <- ifelse(B > 1, 1, B_clip) 

## add these back to the original table:

all$R <- R_clip

all$G <- G_clip

all$B <- B_clip

## done with the conversion

## the manually converted data

plot( as(RGB(R_clip,G_clip,B_clip), 'LUV'), cex=0.5)

Using ColorBrewer to assist with thematic map color selection

RColorBrewer color combinations

Figure 2: 3 colors per combination

RColorBrewer Color Combinations: 9 colors

Figure 3: 9 colors per combination

Choosing the right colors for classes in a thematic map can be a difficult task. The ColorBrewer website provides an interactive tool for browsing numerous color combinations. Each of the color combinations presented on the ColorBrewer website are the culmination of numerous color interpretation studies. In addition, there is a list of special color combinations suitible for audiences which may include color blind individuals.

The R package RColorBrewer adds the color brewer color combinations as well as functions for generating new combinations to R. Figure 1 demonstrates the available color combinations, as returned by the function display.brewer.all.

An example R session:

#load the RColorBrewer package [must be installed first with install.packages()]

library(RColorBrewer)

&nbsp;

#display the "sequential" color combinations, with 3 colors per combination

#See Figure 2

display.brewer.all(n=3,type="seq",exact.n=TRUE)

title("Sequential Color Combinations: 3 Colors per Combination")

&nbsp;

#display the "sequential" color combinations, with 9 colors per combination

#See Figure 3

display.brewer.all(n=9,type="seq",exact.n=TRUE)

title("Sequential Color Combinations: 9 Colors per Combination")

&nbsp;

# convert R colors into RGB triplets;

col2rgb( brewer.pal("Accent", n=5) )