inlcolor (version 1.0.6)

get_colors: Get color palette

Description

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

Usage

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

Value

When argument n is specified, the function returns an object of class 'inlpal'. When n is unspecified a variant of the get_colors function is returned that has default argument values set equal to the values specified by the user.

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::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

Author

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

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.

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 tools:::Rd_expr_doi("10.1002/2013EO450001").

See Also

plot method for drawing color palettes.

set_hinge function to set the hinge location in a color palette derived from one or two color schemes.

grDevices::col2rgb function to express palette colors represented in the hexadecimal format as RGB triplets (R, G, B).

Examples

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

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

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

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

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

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

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

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

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

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

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