rich.colors: Rich Color Palettes In gplots: Various R Programming Tools for Plotting Data

Description

Create a vector of `n` colors that are perceptually equidistant and in an order that is easy to interpret.

Usage

 `1` ```rich.colors(n, palette="temperature", alpha=1.0, rgb=FALSE, plot=FALSE) ```

Arguments

 `n` number of colors to generate. `palette` palette to use: `"temperature"` contains blue-green-yellow-red, and `"blues"` contains black-blue-white. `alpha` alpha transparency, from 0 (fully transparent) to 1 (opaque). `rgb` if `TRUE` then a matrix of RGBA values is included as an attribute. `plot` whether to plot a descriptive color diagram.

Value

A character vector of color codes.

Author(s)

Arni Magnusson.

`rgb`, `rainbow`, `heat.colors`.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```m <- abs(matrix(1:120+rnorm(120), nrow=15, ncol=8)) opar <- par(bg="gray", mfrow=c(1,2)) matplot(m, type="l", lty=1, lwd=3, col=rich.colors(8)) matplot(m, type="l", lty=1, lwd=3, col=rich.colors(8,"blues")) par(opar) barplot(rep(1,100), col=rich.colors(100), space=0, border=0, axes=FALSE) barplot(rep(1,20), col=rich.colors(40)[11:30]) # choose subset plot(m, rev(m), ylim=c(120,0), pch=16, cex=2, col=rich.colors(200,"blues",alpha=0.6)[1:120]) # semitransparent rich.colors(100, plot=TRUE) # describe rgb recipe par(mfrow=c(2,2)) barplot(m, col=heat.colors(15), main="\nheat.colors") barplot(m, col=1:15, main="\ndefault palette") barplot(m, col=rich.colors(15), main="\nrich.colors") barplot(m, col=rainbow(15), main="\nrainbow") par(opar) ```

Example output

```Attaching package: 'gplots'

The following object is masked from 'package:stats':

lowess

[1] "#000040FF" "#000047FF" "#00004FFF" "#000057FF" "#000060FF" "#000069FF"
[7] "#000073FF" "#00007DFF" "#000087FF" "#000091FF" "#00009CFF" "#0000A6FF"
[13] "#0000B0FF" "#0000BAFF" "#0000C4FF" "#0000CEFF" "#000AD6FF" "#0015DFFF"
[19] "#0020E6FF" "#002BEDFF" "#0035F2FF" "#003FF7FF" "#0049FBFF" "#0053FDFF"
[25] "#005CFFFF" "#0065FFFF" "#006EFEFF" "#0076FCFF" "#007FF9FF" "#0087F5FF"
[31] "#008FF0FF" "#0096E9FF" "#009DE2FF" "#00A4DBFF" "#00ABD2FF" "#00B2C9FF"
[37] "#00B8BFFF" "#00BEB5FF" "#00C3ABFF" "#01C9A1FF" "#01CE97FF" "#01D38CFF"
[43] "#01D882FF" "#02DC78FF" "#03E06EFF" "#04E465FF" "#06E85CFF" "#08EB53FF"
[49] "#0CEE4BFF" "#10F143FF" "#17F43CFF" "#1FF635FF" "#2AF82FFF" "#38FA2AFF"
[55] "#49FB25FF" "#5DFC20FF" "#73FD1CFF" "#89FE18FF" "#9FFF15FF" "#B3FF12FF"
[61] "#C5FF0FFF" "#D3FF0DFF" "#DEFE0BFF" "#E7FD09FF" "#EEFC08FF" "#F3FB06FF"
[67] "#F6F905FF" "#F9F704FF" "#FBF504FF" "#FCF303FF" "#FDF002FF" "#FDED02FF"
[73] "#FEEA02FF" "#FEE701FF" "#FEE301FF" "#FFDF01FF" "#FFDB01FF" "#FFD701FF"
[79] "#FFD200FF" "#FFCD00FF" "#FFC800FF" "#FFC200FF" "#FFBD00FF" "#FFB700FF"
[85] "#FFB000FF" "#FFAA00FF" "#FFA300FF" "#FF9C00FF" "#FF9500FF" "#FF8D00FF"
[91] "#FF8500FF" "#FF7D00FF" "#FF7500FF" "#FF6C00FF" "#FF6300FF" "#FF5A00FF"
[97] "#FF5100FF" "#FF4700FF" "#FF3D00FF" "#FF3300FF"
```

gplots documentation built on May 31, 2017, 4:06 a.m.