GetColors: Get Palette Colors
In USGS-R/inlmisc: Miscellaneous Functions for the USGS INL Project Office

GetColors

R Documentation

Get Palette Colors

Description

Create a vector of n colors from qualitative, diverging, and sequential color schemes.

Usage

GetColors(
  n,
  scheme = "smooth rainbow",
  alpha = NULL,
  stops = c(0, 1),
  bias = 1,
  reverse = FALSE,
  blind = NULL,
  gray = FALSE,
  ...
)

Arguments

`n`	'integer' count. Number of colors to be in the palette. The maximum number of colors in a generated palette is dependent on the specified color scheme, see ‘Details’ section for maximum values.
`scheme`	'character' string. Name of color scheme, see ‘Details’ section for scheme descriptions. Argument choices may be abbreviated as long as there is no ambiguity.
`alpha`	'numeric' number. Alpha transparency, values range from 0 (fully transparent) to 1 (fully opaque). Specify as `NULL` to exclude the alpha channel value from colors.
`stops`	'numeric' vector of length 2. Color stops defined by interval endpoints (between 0 and 1) and used to select a subset of the color palette. Only suitable for schemes that allow for color interpolations.
`bias`	'numeric' number. Interpolation bias where larger values result in more widely spaced colors at the high end.
`reverse`	'logical' flag. Whether to reverse the order of colors in the scheme.
`blind`	'character' string. Type of color blindness to simulate: specify `"deutan"` for green-blind vision, `"protan"` for red-blind vision, `"tritan"` for green-blue-blind vision, or `"monochrome"` for total-color blindness. A partial-color blindness simulation requires that the dichromat package is available, see `dichromat` function for additional information. Argument choices may be abbreviated as long as there is no ambiguity.
`gray`	'logical' flag. Whether to subset/reorder the `"bright"`, `"high-contrast"`, `"vibrant"`, and `"muted"` schemes to work well after conversion to gray scale.
`...`	Not used

Details

The suggested data type for color schemes and the characteristics of generated palettes are given in the tables below. [Type: is the type of data being represented, either qualitative, diverging, or sequential. Max n: is the maximum number of colors in a generated palette. And the maximum n value when scheme colors are designed for gray-scale conversion is enclosed in parentheses. A value of infinity indicates that the scheme allows for color interpolations. N: is the not-a-number color. B: is the background color. F: is the foreground color. Abbreviations: –, not available]

Schemes "pale", "dark", and "ground cover" are intended to be accessed in their entirety and subset using vector element names.

Value

When argument n is specified the function returns an object of class 'inlpal' that inherits behavior from the 'character' class. And when n is unspecified a variant of the GetColors function is returned that has default argument values set equal to the values specified by the user.

The inlpal-class object is comprised of a 'character' vector of n colors in the RGB color system. Colors are specified with a string of the form "#RRGGBB" or "#RRGGBBAA" where RR, GG, BB, and AA are the red, green, blue, and alpha hexadecimal values (00 to FF), respectively. Attributes of the returned object include: "names", the informal names assigned to colors in the palette, where NULL indicates no color names are specified; "NaN", a 'character' string giving the color meant for missing data, in hexadecimal format, where NA indicates no color is specified; and "call", an object of class 'call' giving the unevaluated function call (expression) that can be used to reproduce the color palette. Use the eval function to evaluate the "call" argument. A simple plot method is provided for the 'inlpal' class that shows a palette of colors using a sequence of shaded rectangles, see ‘Examples’ section for usage.

Note

Sequential color schemes "YlOrBr" and "iridescent" work well for conversion to gray scale.

Author(s)

J.C. Fisher, U.S. Geological Survey, Idaho Water Science Center

References

Dewez, Thomas, 2004, Variations on a DEM palette, accessed October 15, 2018 at http://soliton.vm.bytemark.co.uk/pub/cpt-city/td/index.html

Mikhailov, Anton, 2019, Turbo, an improved rainbow colormap for visualization: Google AI Blog, accessed August 21, 2019 at https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html.

Tol, Paul, 2018, Colour Schemes: SRON Technical Note, doc. no. SRON/EPS/TN/09-002, issue 3.1, 20 p., accessed September 24, 2018 at https://personal.sron.nl/~pault/data/colourschemes.pdf.

Wessel, P., Smith, W.H.F., Scharroo, R., Luis, J.F., and Wobbe, R., 2013, Generic Mapping Tools: Improved version released, AGU, v. 94, no. 45, p. 409–410 https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2013EO450001

Examples

pal <- GetColors(n = 10)
print(pal)
plot(pal)

Pal <- GetColors(scheme = "turbo")
formals(Pal)
filled.contour(datasets::volcano, color.palette = Pal,
               plot.axes = FALSE)

# Diverging color schemes (scheme)
op <- par(mfrow = c(6, 1), oma = c(0, 0, 0, 0))
plot(GetColors(  9, scheme = "BuRd"))
plot(GetColors(255, scheme = "BuRd"))
plot(GetColors(  9, scheme = "PRGn"))
plot(GetColors(255, scheme = "PRGn"))
plot(GetColors( 11, scheme = "sunset"))
plot(GetColors(255, scheme = "sunset"))
par(op)

# Qualitative color schemes (scheme)
op <- par(mfrow = c(7, 1), oma = c(0, 0, 0, 0))
plot(GetColors(7, scheme = "bright"))
plot(GetColors(6, scheme = "dark"))
plot(GetColors(5, scheme = "high-contrast"))
plot(GetColors(9, scheme = "light"))
plot(GetColors(9, scheme = "muted"))
plot(GetColors(6, scheme = "pale"))
plot(GetColors(7, scheme = "vibrant"))
par(op)

# Sequential color schemes (scheme)
op <- par(mfrow = c(7, 1), oma = c(0, 0, 0, 0))
plot(GetColors( 23, scheme = "discrete rainbow"))
plot(GetColors( 34, scheme = "smooth rainbow"))
plot(GetColors(255, scheme = "smooth rainbow"))
plot(GetColors(  9, scheme = "YlOrBr"))
plot(GetColors(255, scheme = "YlOrBr"))
plot(GetColors( 23, scheme = "iridescent"))
plot(GetColors(255, scheme = "iridescent"))
par(op)

# Alpha transparency (alpha)
op <- par(mfrow = c(5, 1), oma = c(0, 0, 0, 0))
plot(GetColors(34, alpha = 1.0))
plot(GetColors(34, alpha = 0.8))
plot(GetColors(34, alpha = 0.6))
plot(GetColors(34, alpha = 0.4))
plot(GetColors(34, alpha = 0.2))
par(op)

# Color stops (stops)
op <- par(mfrow = c(4, 1), oma = c(0, 0, 0, 0))
plot(GetColors(255, stops = c(0.0, 1.0)))
plot(GetColors(255, stops = c(0.0, 0.5)))
plot(GetColors(255, stops = c(0.5, 1.0)))
plot(GetColors(255, stops = c(0.3, 0.9)))
par(op)

# Interpolation bias (bias)
op <- par(mfrow = c(7, 1), oma = c(0, 0, 0, 0))
plot(GetColors(255, bias = 0.4))
plot(GetColors(255, bias = 0.6))
plot(GetColors(255, bias = 0.8))
plot(GetColors(255, bias = 1.0))
plot(GetColors(255, bias = 1.2))
plot(GetColors(255, bias = 1.4))
plot(GetColors(255, bias = 1.6))
par(op)

# Reverse colors (reverse)
op <- par(mfrow = c(2, 1), oma = c(0, 0, 0, 0),
          cex = 0.7)
plot(GetColors(10, reverse = FALSE))
plot(GetColors(10, reverse = TRUE))
par(op)

# Color blindness (blind)
op <- par(mfrow = c(5, 1), oma = c(0, 0, 0, 0))
plot(GetColors(34, blind = NULL))
plot(GetColors(34, blind = "deutan"))
plot(GetColors(34, blind = "protan"))
plot(GetColors(34, blind = "tritan"))
plot(GetColors(34, blind = "monochrome"))
par(op)

# Gray-scale preparation (gray)
op <- par(mfrow = c(8, 1), oma = c(0, 0, 0, 0))
plot(GetColors(3, "bright", gray = TRUE))
plot(GetColors(3, "bright", gray = TRUE,
               blind = "monochrome"))
plot(GetColors(5, "high-contrast", gray = TRUE))
plot(GetColors(5, "high-contrast", gray = TRUE,
               blind = "monochrome"))
plot(GetColors(4, "vibrant", gray = TRUE))
plot(GetColors(4, "vibrant", gray = TRUE,
               blind = "monochrome"))
plot(GetColors(5, "muted", gray = TRUE))
plot(GetColors(5, "muted", gray = TRUE,
               blind = "monochrome"))
par(op)

USGS-R/inlmisc documentation built on Sept. 17, 2022, 2:38 a.m.