rainbow_hcl(4)
"#E495A5" "#ABB065" "#39BEB1" "#ACA4E2“
However, all palettes are fully customizable:
diverge_hcl(7, h = c(246, 40), c = 96, l = c(65, 90))
Choosing the values would be daunting. But
there are some recommended palettes in the
colorspace documentation. There is also an
interactive tool that can be used to obtain a
customized palette. To start the tool:
pal <- choose_palette()
R has 657 built in color names
To see a list of names:
colors()
These colors are displayed on P. 3.
R color cheatsheet
Finding a good color scheme for presenting data
can be challenging. This color cheatsheet will help!
R uses hexadecimal to represent colors
Hexadecimal is a base-16 number system used to describe
color. Red, green, and blue are each represented by two
characters (#rrggbb). Each character has 16 possible
symbols: 0,1,2,3,4,5,6,7,8,9,A,B,C,D,E,F:
“00” can be interpreted as 0.0 and “FF” as 1.0
i.e., red= #FF0000 , black=#000000, white = #FFFFFF
Two additional characters (with the same scale) can be
added to the end to describe transparency (#rrggbbaa)
A few notes on HSV/HLC
HSV is a better model for how humans perceive color.
HCL can be thought of as a perceptually based version of
the HSV model….blah blah blah…
Without delving into color theory: color schemes based
on HSV/HLC models generally just look good.
R translates various color models to hex, e.g.:
• RGB (red, green, blue): The default intensity scale in R
ranges from 0-1; but another commonly used scale is 0-
255. This is obtained in R using maxColorValue=255.
alpha is an optional argument for transparency, with the
same intensity scale.
rgb(r, g, b, maxColorValue=255, alpha=255)
• HSV (hue, saturation, value): values range from 0-1, with
optional alpha argument
hsv(h, s, v, alpha)
• HCL (hue, chroma, luminance): hue describes the color and
ranges from 0-360; 0 = red, 120 = green, blue = 240, etc.
Range of chroma and luminance depend on hue and each
other
hcl(h, c, l, alpha)
peachpuff4
TIP: When it comes to selecting a color palette,
DO NOT try to handpick individual colors! You will
waste a lot of time and the result will probably not
be all that great. R has some good packages for
color palettes. Here are some of the options
Packages: grDevices and
colorRamps
grDevices comes with the base
installation and colorRamps
must be installed. Each palette’s
function has an argument for
the number of colors and
transparency (alpha):
Package: RcolorBrewer
This function has an argument for the number of
colors and the color palette (see P. 4 for options).
brewer.pal(4, “Set3”)
> "#8DD3C7" "#FFFFB3" "#BEBADA" "#FB8072“
For the rainbow palette you can also select start/end color
(red = 0, yellow = 1/6, green = 2/6, cyan = 3/6, blue
= 4/6 and magenta = 5/6) and saturation (s) and value (v):
rainbow(n, s = 1, v = 1, start = 0, end = max(1, n - 1)/n, alpha = 1)
To view colorbrewer palettes in R: display.brewer.all(5)
There is also a very nice interactive viewer:
http://colorbrewer2.org/
Package: colorspace
These color palettes are based
on HCL and HSV color models.
The results can be very
aesthetically pleasing. There
are some default palettes:
## My Recommendation ##
R Color Palettes
This is for all of you who don’t know anything
about color theory, and don’t care but want
some nice colors on your map or figure….NOW!
grDevices
palettes
cm.colors
topo.colors
terrain.colors
heat.colors
rainbow
see P. 4 for
options
heat.colors(4, alpha=1)
> #FF0000FF" "#FF8000FF" "#FFFF00FF" "#FFFF80FF“
colorspace
default palettes
diverge_hcl
diverge_hsl
terrain_hcl
sequential_hcl
rainbow_hcl
R can translate colors to rgb (this is handy for
matching colors in other programs)
col2rgb(c(“#FF0000”, “blue”))
Page 1, Melanie Frazier
Example:
Discrete variables
R color cheatsheet
Overview of colorspace palette selector
library("colorspace")
pal <- choose_palette()
Select the type of color scheme
based on the type of data
Default color schemes – can be
used “as is” or as a starting point
for modification
Interactively select:
• hue: color
• chroma: low chroma = gray
• luminance: high luminance =
pastel
• power: how the color changes
along a gradient
Save palette for future R sessions:
• txt file with hex codes
• .R file with a function describing
how to generate the palette.
source can be used to import the
function into R; but one
complication is that you have to
open the .R file and name the
function to use it.
• Copy values into relevant
colorspace functions.
Diverging color schemes:
diverge_hcl(7, h = c(260, 0), c =
100, l = c(28, 90), power = 1.5)
Sequential color schemes:
sequential_hcl(n, h, c.= c(), l=c(),
power)
Qualitative color schemes:
rainbow_hcl(n, c, l, start, end) (for
qualtitative schemes; start/ end
refer to the H1/H2 hue values)
Display color scheme with
different plot types
HCL
hue
How to use hex codes to define color
using the plot function
When “OK” is selected, the color palette
will be saved in the R session. To return 7
hex color codes from the selected palette:
pal <- choose_palette()
pal(7)
[NOTE: These values are not saved if you
don’t save the session]
Option 1
If you don’t need to control which colors are
associated with each level of a variable:
plot(Sepal.Length ~ Sepal.Width,
col=rainbow_hcl(3)[c(Species)],
data=iris, pch=16)
legend("topleft", pch=16, col=rainbow_hcl(3),
legend=unique(iris$Species))
Option 2
If you want to control which colors are
associated with the levels of a variable, I find it
easiest to create a variable in the data:
iris$color <- factor(iris$Species,
levels=c("virginica", "versicolor", "setosa"),
labels=rainbow_hcl(3))
plot(Sepal.Length ~ Sepal.Width,
col=as.character(color), pch=16, data=iris)
Continuous variables
Option 1
Break into categories and assign colors:
iris2 <- subset(iris, Species=="setosa")
color <- cut(iris2$Petal.Length,
breaks=c(0,1.3,1.5,2), labels=sequential_hcl(3))
Or, break by quantiles (be sure to include 0 & 1):
color <- cut(iris2$Petal.Length,
breaks=quantile(iris$Petal.Length, c(0, 0.25, 0.5,
0.75, 1)), labels=sequential_hcl(3))
plot(Sepal.Width ~ Sepal.Length, pch=16,
col=color, data=iris2)
Option 2
Fully continuous gradient:
data <- data.frame("a"=runif(10000),
"b"=runif(10000))
color=diverge_hcl(length(data$a))[rank(data$a)]
plot(a~b, col=color, pch=16, data=data)
Page 2, Melanie Frazier
Select # of colors in palette
For ggplot2, I think the most
flexible color scales are:
scale_colour_manual
scale_colour_gradient
for discrete and continuous
variables, respectively
Sequential Qualitative Diverging
colorRamps and grDevices
RColorBrewer
Useful Resources:
A larger color chart of R named colors:
http://research.stowers-
institute.org/efg/R/Color/Chart/ColorChart.pdf
Nice overview of color in R:
http://research.stowers-
institute.org/efg/Report/UsingColorInR.pdf
http://students.washignton.edu/mclarkso/docu
ments/colors Ver2.pdf
A color theory reference:
Zeileis, A. K. Hornik, P. Murrell. 2009. Escaping
RGBland: selecting colors for statistical graphics.
Computational and Statistics & Data Analysis
53:3259-3270
To begin interactive color selector: pal <- choose_palette()
To display RColorBrewer palette: display.brewer.all()
For interactive color selector: http://colorbrewer2.org/
colorRamps and grDevices color palette, display from:
http://bc.bojanorama.pl/2013/04/r-color-reference-sheet/
Page 4, Melanie Frazier
