Package 'pals'

Bivariate palettes

Description

Color palettes designed for bivariate choropleth maps.

Usage

arc.bluepink(n = 9)

brewer.qualbin(n = 6)

brewer.divbin(n = 6)

brewer.divseq(n = 9)

brewer.qualseq(n = 9)

brewer.divdiv(n = 9)

brewer.seqseq1(n = 9)

brewer.seqseq2(n = 9)

census.blueyellow(n = 9)

tolochko.redblue(n = 9)

stevens.pinkgreen(n = 9)

stevens.bluered(n = 9)

stevens.pinkblue(n = 9)

stevens.greenblue(n = 9)

stevens.purplegold(n = 9)

vsup.viridis(n = 32)

vsup.redblue(n = 32)

Arguments

n	Number of colors to return.

Details

In many of these palette names, the color in the upper left corner is given first, and the color in the lower right corner is given second.

The 'brewer.*' palettes use 'bin' (binary), 'div' (diverging), 'qual' (qualitative), 'seq' (sequential) for the horizontal and vertical directions.

The 'arc.bluepink' palette uses white in the lower-left corner, which makes it difficult to see the difference between low values and missing data on maps.

The 'census.blueyellow' palette is slightly different, in that one direction uses lightness, and the other direction uses hue (yellow, green, blue).

The 'vsup.*' palettes are Value-Suppressing Uncertainty Palettes.

We strongly discourage using 'vsup.viridis', because the horizontal axis has changes in brightness, which are confounded with the changes in brightness in the vertical axis.

These palettes are all deliberately chosen to be discrete.

Bivariate color palettes can be difficult to use and interpret. Please be careful.

Value

A vector of colors as hex strings.

Author(s)

Palette colors by various authors. R code by Kevin Wright.

References

Joshua Stevens. http://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/

Cindy Brewer. http://www.personal.psu.edu/cab38/ColorSch/Schemes.html

Michael Correll AND Dominik Moritz AND Jeffrey Heer. (2018). Value-Suppressing Uncertainty Palettes. https://github.com/uwdata/papers-vsup

Robin Tolochko. http://tolomaps.tumblr.com/post/131671267233/creating-a-bivariate-choropleth-color-scheme

Aileen Buckley. https://www.slideshare.net/aileenbuckley/arc-gis-bivariate-mapping-tools-28903069

https://www.census.gov/population/www/cen2000/atlas/ Total Population, p. 4.

Examples

bivcol <- function(pal, nx=3, ny=3){
  tit <- substitute(pal)
  if(is.function(pal)) pal <- pal()
  ncol <- length(pal)
  if(missing(nx)) nx <- sqrt(ncol)
  if(missing(ny)) ny <- nx
  image(matrix(1:ncol, nrow=ny), axes=FALSE, col=pal)
  mtext(tit)
}
op <- par(mfrow=c(4,4), mar=c(1,1,2,1))
bivcol(arc.bluepink)
bivcol(brewer.divbin, nx=3)
bivcol(brewer.divdiv)
bivcol(brewer.divseq)
bivcol(brewer.qualbin, nx=3)
bivcol(brewer.qualseq)
bivcol(brewer.seqseq1)
bivcol(brewer.seqseq2)
bivcol(census.blueyellow)
bivcol(stevens.bluered)
bivcol(stevens.greenblue)
bivcol(stevens.pinkblue)
bivcol(stevens.pinkgreen)
bivcol(stevens.purplegold)
bivcol(tolochko.redblue)
bivcol(vsup.redblue, nx=8)
par(op)

---

ColorBrewer palettes

Description

These functions provide a unified access to the ColorBrewer palettes.

Usage

brewer.blues(n)

brewer.bugn(n)

brewer.bupu(n)

brewer.gnbu(n)

brewer.greens(n)

brewer.greys(n)

brewer.oranges(n)

brewer.orrd(n)

brewer.pubu(n)

brewer.pubugn(n)

brewer.purd(n)

brewer.purples(n)

brewer.rdpu(n)

brewer.reds(n)

brewer.ylgn(n)

brewer.ylgnbu(n)

brewer.ylorbr(n)

brewer.ylorrd(n)

brewer.brbg(n)

brewer.piyg(n)

brewer.prgn(n)

brewer.puor(n)

brewer.rdbu(n)

brewer.rdgy(n)

brewer.rdylbu(n)

brewer.rdylgn(n)

brewer.spectral(n)

brewer.accent(n)

brewer.dark2(n)

brewer.paired(n)

brewer.pastel1(n)

brewer.pastel2(n)

brewer.set1(n)

brewer.set2(n)

brewer.set3(n)

Arguments

n	The number of colors to display for palette functions.

Details

The palette names begin with 'brewer' to make it easier to use auto-completion.

Value

A vector of colors.

Examples

# Sequential
pal.bands(brewer.blues, brewer.bugn, brewer.bupu, brewer.gnbu, brewer.greens,
          brewer.greys, brewer.oranges, brewer.orrd, brewer.pubu, brewer.pubugn,
          brewer.purd, brewer.purples, brewer.rdpu, brewer.reds, brewer.ylgn,
          brewer.ylgnbu, brewer.ylorbr, brewer.ylorrd)

# Diverging
pal.bands(brewer.brbg, brewer.piyg, brewer.prgn, brewer.puor, brewer.rdbu,
          brewer.rdgy, brewer.rdylbu, brewer.rdylgn, brewer.spectral)

# Qualtitative
pal.bands(brewer.accent(8), brewer.dark2(8), brewer.paired(12), brewer.pastel1(9),
          brewer.pastel2(8), brewer.set1(9), brewer.set2(8), brewer.set3(10),
          labels=c("brewer.accent", "brewer.dark2", "brewer.paired", "brewer.pastel1",
          "brewer.pastel2", "brewer.set1", "brewer.set2", "brewer.set3"))

## Not run: 

# Sequential
pal.test(brewer.blues)
pal.test(brewer.bugn)
pal.test(brewer.bupu)
pal.test(brewer.gnbu)
pal.test(brewer.greens)
pal.test(brewer.greys)
pal.test(brewer.oranges)
pal.test(brewer.orrd) 
pal.test(brewer.pubu) # good
pal.test(brewer.pubugn) # good
pal.test(brewer.purd)
pal.test(brewer.purples)
pal.test(brewer.rdpu)
pal.test(brewer.reds)
pal.test(brewer.ylgn)
pal.test(brewer.ylgnbu)
pal.test(brewer.ylorbr)
pal.test(brewer.ylorrd)

# Diverging, max n=11 colors
pal.test(brewer.brbg)
pal.test(brewer.piyg)
pal.test(brewer.prgn)
pal.test(brewer.puor)
pal.test(brewer.rdbu)
pal.test(brewer.rdgy)
pal.test(brewer.rdylbu)
pal.test(brewer.rdylgn)
pal.test(brewer.spectral)

# Qualtitative. These are weird...don't do this
pal.test(brewer.accent)
pal.test(brewer.dark2)
pal.test(brewer.paired)
pal.test(brewer.pastel1)
pal.test(brewer.pastel2)
pal.test(brewer.set1)
pal.test(brewer.set2)
pal.test(brewer.set3)

# Need to move these to 'tests'         
pal.bands(brewer.accent(3), brewer.accent(4), brewer.accent(5), brewer.accent(6),
          brewer.accent(7), brewer.accent(8), brewer.accent(9), brewer.accent(10),
          brewer.accent(11), brewer.accent(12))
#brewer.purd(1) # Should err
#brewer.purd(2) # Should err
brewer.purd(3)
brewer.purd(9)
brewer.purd(25)
pal.bands(brewer.purd(3), brewer.purd(4), brewer.purd(5), brewer.purd(6),
          brewer.purd(7), brewer.purd(8), brewer.purd(9), brewer.purd(10),
          brewer.purd(11), brewer.purd(12), brewer.purd(13), brewer.purd(14),
          brewer.purd(15), brewer.purd(100))

## End(Not run)

---

Miscellaneous colormaps

Description

Colormaps designed for continuous data.

Usage

cubehelix(n = 25, start = 0.5, r = -1.5, hue = 1, gamma = 1)

gnuplot(n = 25, trim = 0.1)

tol.rainbow(n = 25, manual = TRUE)

jet(n = 25)

parula(n = 25)

turbo(n = 25)

coolwarm(n = 25)

warmcool(n = 25)

cividis(n = 25)

Arguments

n	Number of colors to return.
start	Start angle (radians) of the helix
r	Number of rotations of the helix
hue	Saturation of the colors, 0 = grayscale, 1 = fully saturated
gamma	gamma < 1 emphasizes low intensity values, gamma > 1 emphasizes high intensity values
trim	Proportion of tail colors to trim, default 0.1
manual	If TRUE, return manually-calibrated colors.

Details

The coolwarm and 'warmcool' palette by Moreland (2009) is colorblind safe. The transition to and from gray is smooth, to reduce Mach banding.

The cubehelix palette is sometimes used in astronomy. Images using this palette will look monotonically increasing to both the human eye and when printed in black and white. This palette is named 'cubehelix' because the r,g,b values produced can be visualised as a squashed helix around the diagonal from black (0,0,0) to white (1,1,1) in the r,g,b color cube.

The gnuplot palette uses black-blue-pink-yellow colors. This palette looks good when printed in black and white. Identical to the sp::bpy.colors palette.

The jet palette should not be used and is only provided for historical interest. The code for this palette comes from the example section of colorRampPalette. The 'jet' palette gained popularity as the default colormap in older versions of Matlab. Because of the unevenness of the gradient, jet will exaggerate some features of the data and minimize other features.

The parula palette here is similar to the default Matlab palette. Specific colors were adapted from the BIDS/colormap package.

The tol.rainbow palette by Tol (2012) is a dark rainbow palette from purple to red which works much better than standard rainbow palettes for colorblind people. If 1 <= n <= 13, manually-chosen equidistant rainbow colors are used, where distances are defined by the CIEDE2000 color difference. If 14 <= n <= 21, manually-chosen triplets of colours are used. If n > 21 or if manual=FALSE, the palette computes the colors according to Equation 3 of Tol (2012).

The cividis palette by Jamie R. Nuñez, Christopher R. Anderton, Ryan S. Renslow, is a variation of viridis that is less colorful.

The turbo palette by Mikhailov, is similar to jet, but avoids the artificial color banding that plagues jet. See also tol.rainbow.

Value

A vector of colors.

Author(s)

Palette colors by various authors. R code by Kevin Wright.

References

Dave A. Green. (2011). A colour scheme for the display of astronomical intensity images. Bull. Astr. Soc. India, 39, 289-295. http://arxiv.org/abs/1108.5083 http://www.mrao.cam.ac.uk/~dag/CUBEHELIX/

Kenneth Moreland. (2009). Diverging Color Maps for Scientific Visualization. Proceedings of the 5th International Symposium on Visual Computing. http://www.kennethmoreland.com/color-maps/ http://dx.doi.org/10.1007/978-3-642-10520-3_9

Paul Tol (2012). Color Schemes. SRON technical note, SRON/EPS/TN/09-002. https://personal.sron.nl/~pault/

My Favorite Colormap. (gnuplot) https://web.archive.org/web/20040119000943/http://www.ihe.uni-karlsruhe.de/mitarbeiter/vonhagen/palette.en.html

MathWorks documentation. http://www.mathworks.com/help/matlab/ref/colormap.html

BIDS/colormap. https://github.com/BIDS/colormap/blob/master/parula.py

Jamie R. Nuñez, Christopher R. Anderton, Ryan S. Renslow (2017). An optimized colormap for the scientific community. https://arxiv.org/abs/1712.01662

Anton Mikhailov, Turbo, An Improved Rainbow Colormap for Visualization (2019). https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html

Examples

pal.bands(coolwarm, cubehelix, gnuplot, parula, cividis, jet, turbo, tol.rainbow)

if(FALSE){

# ----- coolwarm -----
pal.test(coolwarm) # Minimal mach banding
# Note the mach banding gray line in the following:
# pal.volcano(colorRampPalette(c("#3B4CC0", "lightgray", "#B40426")))

# ----- cubehelix -----
# Full range of colors. Pink is overwhelming. Not the best choice.
pal.test(cubehelix)

# Mostly blues/greens. Dark areas severely too black. 
# Similar, but more saturated. See: http://inversed.ru/Blog_2.htm
pal.volcano(function(n) cubehelix(n, start=.25, r=-.67, hue=1.5))

# Dark colors totally lose structure of the volcano peak.
op <- par(mfrow=c(2,2), mar=c(2,2,2,2))
image(volcano, col = cubehelix(51), asp = 1, axes=0, main="cubehelix")
image(volcano, col = cubehelix(51, start=.25, r=-.67, hue=1.5), asp = 1, axes=0, main="cubehelix")
image(volcano, col = rev(cubehelix(51)), asp = 1, axes=0, main="cubehelix")
image(volcano, col = rev(cubehelix(51, start=.25, r=-.67, hue=1.5)), 
                                   asp = 1, axes=0, main="cubehelix")
par(op)

# ----- gnuplot -----
pal.test(gnuplot)

# ----- jet -----
# pal.volcano(jet)
pal.test(jet)

# ----- parula -----
# pal.volcano(parula)
pal.test(parula)

# ----- tol.rainbow -----
# pal.volcano(tol.rainbow)
pal.test(tol.rainbow)

} 

# ----- cividis -----
# pal.volcano(cividis)
pal.test(cividis)

---

Discrete palettes

Description

Color palettes designed for discrete, categorical data with a small number of categories.

Usage

alphabet(n = 26)

alphabet2(n = 26)

cols25(n = 25)

glasbey(n = 32)

kelly(n = 22)

polychrome(n = 36)

stepped(n = 24)

stepped2(n = 20)

stepped3(n = 20)

okabe(n = 8)

tableau20(n = 20)

tol(n = 12)

tol.groundcover(n = 14)

trubetskoy(n = 22)

watlington(n = 16)

Arguments

n	Number of colors to return.

Details

The alphabet palette has 26 distinguishable colors that have logical names starting with the English alphabet letters A, B, ... Z. This palette is based on the work by Green-Armytage (2010), but uses the names 'orange' instead of 'orpiment', and 'magenta' instead of 'mallow'.

The alphabet2 palette uses a similar idea with slightly different colors and slightly different names. This palette comes from the Polychrome package, generated with the createPalette function and then manually arranged and named.

The cols25 palette was created experimentally by Wright (unpublished) to create a set of colors that are distinct.

The glasbey palette by Glasbey et al (2007) has 32 colors that are maximally distinct. Glasbey has 'white' as the second color, but in this version of the palette, the color 'white' is moved to the end, and is actually light-gray, #F2F3F4.

The kelly palette of 22 colors maximize the contrast between colors in a set if the colors are chosen in sequential order. Kelly paid attention to the needs of people with color blindness. The first nine colors work well for such people and people with normal vision. Kelly did not provide RGB color values, and the paper was in black-and-white. A color image of the Kelly palette can be found in Green-Armytage (2010). The color 'white' has been re-defined as light-gray, #F2F3F4. Commentary: We think the kelly palette has an over-abundance of orange-ish colors, the purples are not very distinct, color 22 (olive green) is almost identical to color 2 (black), etc. Trubetskoy says "towards the bottom of Kelly's list things get complicated. The orange yellow, purplish red, yellowish brown and reddish orange all seemed to blend together".

The okabe palette was design to be (1) clear for both colorblind and non-colorblind people, (2) vividly colored, and (3) good for screen and printed. The color-blind simulation tools in R suggest this palette is not as useful as hoped.

The polychrome palette is also from the Polychrome package. Colors were given a name from the ISCC-NBS standard.

The stepped palette has 24 colors (5 hues, 4 levels within each hue, plus 4 shades of gray) that is useful for showing varying levels within categories. Inspired by (http://geog.uoregon.edu/datagraphics/color_scales.htm), but in order to better separate these colors in RGB space, red hue 0 was moved to hue 350, green hue 80 moved to hue 90. The number of colors within each hue was reduced from 5 to 4, and gray shades were added.

stepped2 and stepped3 are from the 'vega' package https://github.com/vega/vega/wiki/Scales.

The tableau20 palette has 10 pairs of dark/light colors that are used by the Tableau software.

The trubetskoy palette as 20 colors, plus black and white. The colors are designed to be easily distinguishable, tastefully luminant, intuitively named, supplied with RGB colors. https://sashamaps.net/docs/resources/20-colors/

The tol palette has 12 colors by Paul Tol.

The watlington palette has 16 colors. The color 'white' has been re-defined as light-gray, #F2F3F4.

Value

A vector of colors as hex strings.

Author(s)

Palette colors by various authors. R code by Kevin Wright.

References

Robert M. Boynton. (1989) Eleven Colors That Are Almost Never Confused. Proc. SPIE 1077, Human Vision, Visual Processing, and Digital Display, 322-332. http://doi.org/10.1117/12.952730

Kevin R. Coombes (2016). Polychrome. https://rdrr.io/rforge/Polychrome/man/alphabet.html

Chris Glasbey, Gerie van der Heijden, Vivian F. K. Toh, Alision Gray (2007). Colour Displays for Categorical Images. Color Research and Application, 32, 304-309. http://doi.org/10.1002/col.20327

P. Green-Armytage (2010): A Colour Alphabet and the Limits of Colour Coding. Colour: Design & Creativity (5) (2010): 10, 1-23. www.aic-color.org/journal/v5/jaic_v5_06.pdf

K. Kelly (1965): Twenty-two colors of maximum contrast. Color Eng., 3(6), 1965. http://www.iscc.org/pdf/PC54_1724_001.pdf

Masataka Okabe and Kei Ito (2002). Color Universal Design (CUD) - How to make figures and presentations that are friendly to Colorblind people. http://jfly.iam.u-tokyo.ac.jp/color/

Paul Tol (2012). Color Schemes. SRON technical note, SRON/EPS/TN/09-002. https://personal.sron.nl/~pault/

Sasha Trubetskoy (2017). List of 20 Simple, Distinct Colors. https://sashamaps.net/docs/resources/20-colors/

John Watlington. An Optimum 16 Color Palette. http://alumni.media.mit.edu/~wad/color/palette.html

Color Schemes Appropriate for Scientific Data Graphics http://geog.uoregon.edu/datagraphics/color_scales.htm

Examples

pal.bands(alphabet, alphabet2, cols25, glasbey, kelly, okabe, polychrome,
  tableau20, tol, watlington)
pal.bands(stepped, stepped2, stepped3)
pal.bands(tol.groundcover)

## Not run: 
alphabet()
alphabet()["jade"]
pal.bands(alphabet,n=26)
pal.heatmap(alphabet)
# pal.cube(alphabet)

pal.heatmap(alphabet2)

pal.heatmap(cols25)

pal.heatmap(glasbey())
# pal.cube(glasbey, n=32) # Blues are close together

pal.heatmap(kelly()) # too many orange/pink colors

pal.safe(okabe()) # not great

pal.heatmap(polychrome)

pal.heatmap(stepped, n=24)

pal.heatmap(stepped2, n=20)

pal.heatmap(stepped3, n=20)

pal.heatmap(tol, 12)

pal.heatmap(watlington(16))

## End(Not run)

---

Peter Kovesi's perceptually uniform colormaps

Description

Peter Kovesi's perceptually uniform colormaps

Usage

kovesi.cyclic_grey_15_85_c0(n)

kovesi.cyclic_grey_15_85_c0_s25(n)

kovesi.cyclic_mrybm_35_75_c68(n)

kovesi.cyclic_mrybm_35_75_c68_s25(n)

kovesi.cyclic_mygbm_30_95_c78(n)

kovesi.cyclic_mygbm_30_95_c78_s25(n)

kovesi.cyclic_wrwbw_40_90_c42(n)

kovesi.cyclic_wrwbw_40_90_c42_s25(n)

kovesi.diverging_isoluminant_cjm_75_c23(n)

kovesi.diverging_isoluminant_cjm_75_c24(n)

kovesi.diverging_isoluminant_cjo_70_c25(n)

kovesi.diverging_linear_bjr_30_55_c53(n)

kovesi.diverging_linear_bjy_30_90_c45(n)

kovesi.diverging_rainbow_bgymr_45_85_c67(n)

kovesi.diverging_bkr_55_10_c35(n)

kovesi.diverging_bky_60_10_c30(n)

kovesi.diverging_bwr_40_95_c42(n)

kovesi.diverging_bwr_55_98_c37(n)

kovesi.diverging_cwm_80_100_c22(n)

kovesi.diverging_gkr_60_10_c40(n)

kovesi.diverging_gwr_55_95_c38(n)

kovesi.diverging_gwv_55_95_c39(n)

kovesi.isoluminant_cgo_70_c39(n)

kovesi.isoluminant_cgo_80_c38(n)

kovesi.isoluminant_cm_70_c39(n)

kovesi.linear_bgy_10_95_c74(n)

kovesi.linear_bgyw_15_100_c67(n)

kovesi.linear_bgyw_15_100_c68(n)

kovesi.linear_blue_5_95_c73(n)

kovesi.linear_blue_95_50_c20(n)

kovesi.linear_bmw_5_95_c86(n)

kovesi.linear_bmw_5_95_c89(n)

kovesi.linear_bmy_10_95_c71(n)

kovesi.linear_bmy_10_95_c78(n)

kovesi.linear_gow_60_85_c27(n)

kovesi.linear_gow_65_90_c35(n)

kovesi.linear_green_5_95_c69(n)

kovesi.linear_grey_0_100_c0(n)

kovesi.linear_grey_10_95_c0(n)

kovesi.linear_kry_5_95_c72(n)

kovesi.linear_kry_5_98_c75(n)

kovesi.linear_kryw_5_100_c64(n)

kovesi.linear_kryw_5_100_c67(n)

kovesi.linear_ternary_blue_0_44_c57(n)

kovesi.linear_ternary_green_0_46_c42(n)

kovesi.linear_ternary_red_0_50_c52(n)

kovesi.rainbow_bgyr_35_85_c72(n)

kovesi.rainbow(n)

kovesi.rainbow_bgyr_35_85_c73(n)

kovesi.rainbow_bgyrm_35_85_c69(n)

kovesi.rainbow_bgyrm_35_85_c71(n)

Arguments

n	The number of colors to display for palette functions.

Details

All colormaps are named using Peter Kovesi's naming scheme: <category>_<huesequence>_<lightnessrange>_c<meanchroma>_s<colorshift>

Note: kovesi.rainbow is another name for rainbow_bgyr_35_85_c72.

Value

A vector of colors.

Author(s)

Colormaps by Peter Kovesi. R code by Kevin Wright.

References

Peter Kovesi (2016). CET Perceptually Uniform Colour Maps. http://peterkovesi.com/projects/colourmaps/

Peter Kovesi (2015). Good Colour Maps: How to Design Them. Arxiv. https://arxiv.org/abs/1509.03700

https://bokeh.github.io/colorcet/

Examples

if(FALSE){
pal.bands(kovesi.cyclic_grey_15_85_c0, kovesi.cyclic_grey_15_85_c0_s25,
kovesi.cyclic_mrybm_35_75_c68, kovesi.cyclic_mrybm_35_75_c68_s25,
kovesi.cyclic_mygbm_30_95_c78, kovesi.cyclic_mygbm_30_95_c78_s25,
kovesi.cyclic_wrwbw_40_90_c42, kovesi.cyclic_wrwbw_40_90_c42_s25,
kovesi.diverging_isoluminant_cjm_75_c23, kovesi.diverging_isoluminant_cjm_75_c24,
kovesi.diverging_isoluminant_cjo_70_c25, kovesi.diverging_linear_bjr_30_55_c53,
kovesi.diverging_linear_bjy_30_90_c45, kovesi.diverging_rainbow_bgymr_45_85_c67,
kovesi.diverging_bkr_55_10_c35, kovesi.diverging_bky_60_10_c30,
kovesi.diverging_bwr_40_95_c42, kovesi.diverging_bwr_55_98_c37,
kovesi.diverging_cwm_80_100_c22, kovesi.diverging_gkr_60_10_c40,
kovesi.diverging_gwr_55_95_c38, kovesi.diverging_gwv_55_95_c39,
kovesi.isoluminant_cgo_70_c39, kovesi.isoluminant_cgo_80_c38,
kovesi.isoluminant_cm_70_c39, kovesi.linear_bgy_10_95_c74,
kovesi.linear_bgyw_15_100_c67, kovesi.linear_bgyw_15_100_c68,
kovesi.linear_blue_5_95_c73, kovesi.linear_blue_95_50_c20,
kovesi.linear_bmw_5_95_c86, kovesi.linear_bmw_5_95_c89,
kovesi.linear_bmy_10_95_c71, kovesi.linear_bmy_10_95_c78,
kovesi.linear_gow_60_85_c27, kovesi.linear_gow_65_90_c35,
kovesi.linear_green_5_95_c69, kovesi.linear_grey_0_100_c0,
kovesi.linear_grey_10_95_c0, kovesi.linear_kry_5_95_c72,
kovesi.linear_kry_5_98_c75, kovesi.linear_kryw_5_100_c64,
kovesi.linear_kryw_5_100_c67, kovesi.linear_ternary_blue_0_44_c57,
kovesi.linear_ternary_green_0_46_c42, kovesi.linear_ternary_red_0_50_c52,
kovesi.rainbow_bgyr_35_85_c72, kovesi.rainbow_bgyr_35_85_c73,
kovesi.rainbow_bgyrm_35_85_c69, kovesi.rainbow_bgyrm_35_85_c71)
}

---

Matplotlib colormaps

Description

Viridis family of colormaps as found in Matplotlib. Designed to be perceptually uniform, but generally too dark to be useful.

Usage

magma(n)

inferno(n)

plasma(n)

viridis(n)

Arguments

n	Number of colors to return

Value

A vector of colors

Author(s)

Palettes by Matteo Niccoli. R code by Kevin Wright.

Examples

pal.bands(magma, inferno, plasma, viridis)

---

Matteo Niccoli's perceptually uniform colormaps

Description

These colormaps are intended by be more perceptually balanced than traditional rainbow-like palettes.

Usage

cubicyf(n)

isol(n)

cubicl(n)

linearl(n)

linearlhot(n)

Arguments

n	Number of colors to return

Details

isol(): Lab-based isoluminant rainbow with constant luminance L*=60. Best choice for displaying interval data with external lighting. best for displaying interval data with external lighting. This is so as to allow the lighting to provide the shading to highlight the details of interest. If lighting is combined with a colormap that has its own luminance function associated - even as simple as a linear increase this will confuse the viewer.

linearl(): Lab-based linear lightness rainbow. A linear lightness modification of Matlab's 'hot' palette. For interval data displayed without external lighting. 100

linlhot(): Linear lightness modification of Matlab's hot color palette. For interval data displayed without external lighting 100

cubicyf(): Lab-based rainbow scheme with cubic-law luminance(default) For interval data displayed without external lighting 100

cubicl(): Lab-based rainbow scheme with cubic-law luminance For interval data displayed without external lighting Similar to cubicyf(), but has red at high end (a modest deviation from 100

Value

A vector of colors

Author(s)

Palettes by Matteo Niccoli. R code by Kevin Wright.

References

Matteo Niccoli (2010). Perceptually improved colormaps. http://www.mathworks.com/matlabcentral/fileexchange/28982-perceptually-improved-colormaps Color definitions from here: http://www.mathworks.com/matlabcentral/fileexchange/28982-perceptually-improved-colormaps/content/pmkmp/pmkmp.m https://mycarta.wordpress.com/2012/05/29/the-rainbow-is-dead-long-live-the-rainbow-series-outline/

Examples

pal.bands(cubicyf,cubicl,isol,linearl,linearlhot)
pal.test(cubicyf) # purple blue green
pal.test(cubicl) # purple blue green orange
# pal.test(isol) # magenta blue green red. Poor in green area.
# pal.test(linearl) # black blue green tan. Poor in black area.
# pal.test(linearlhot) # black red yellow

---

Oceanography perceptually uniform colormaps

Description

These palettes have been designed to be a collection of perceptually uniform colormaps designed for oceanographic data display.

Usage

ocean.algae(n)

ocean.deep(n)

ocean.dense(n)

ocean.gray(n)

ocean.haline(n)

ocean.ice(n)

ocean.matter(n)

ocean.oxy(n)

ocean.phase(n)

ocean.solar(n)

ocean.thermal(n)

ocean.turbid(n)

ocean.balance(n)

ocean.curl(n)

ocean.delta(n)

ocean.amp(n)

ocean.speed(n)

ocean.tempo(n)

Arguments

n	Number of colors

Details

The 'oxy' palette does not include gray as shown in Thyng (2016).

The 'balance', 'delta', and 'curl' palettes were originally given as 2*256 colors (256 each for the left and right half of the palette) and have been downsampled to 256 colors.

The palettes from matplotlib have been converted from RGB codes to hexadecimal strings for use in this package.

Value

None

Author(s)

Palette colors by Kristen Thyng. R code by Kevin Wright

References

Thyng, K.M., C.A. Greene, R.D. Hetland, H.M. Zimmerle, and S.F. DiMarco (2016). True colors of oceanography: Guidelines for effective and accurate colormap selection. Oceanography, 29(3):9-13, http://dx.doi.org/10.5670/oceanog.2016.66.

Examples

pal.bands(ocean.thermal, ocean.haline, ocean.solar, ocean.ice, ocean.gray,
          ocean.oxy, ocean.deep, ocean.dense, ocean.algae, ocean.matter,
          ocean.turbid, ocean.speed, ocean.amp, ocean.tempo, ocean.phase,
          ocean.balance, ocean.delta, ocean.curl, main="Ocean palettes")

## Not run: 
pal.test(ocean.thermal)
pal.test(ocean.haline) # better than parula!
pal.test(ocean.solar)
pal.test(ocean.ice)
pal.test(ocean.gray)
pal.test(ocean.oxy)
pal.test(ocean.deep)
pal.test(ocean.dense)
pal.test(ocean.algae)
pal.test(ocean.matter)
pal.test(ocean.turbid)
pal.test(ocean.speed)
pal.test(ocean.amp)
pal.test(ocean.tempo)
pal.test(ocean.phase)
pal.test(ocean.balance)
pal.test(ocean.delta)
pal.test(ocean.curl)

## End(Not run)

---

Show palettes and colormaps as colored bands

Description

Show palettes as colored bands.

Usage

pal.bands(
  ...,
  n = 100,
  labels = NULL,
  main = NULL,
  gap = 0.1,
  sort = "none",
  show.names = TRUE
)

Arguments

...	Palettes/colormaps, each of which is either (1) a vectors of colors or (2) a function returning a vector of colors.
n	The number of colors to display for palette functions.
labels	Labels for palettes
main	Title at top of page.
gap	Vertical gap between bars, default is 0.1
sort	If sort="none", palettes are not sorted. If sort="hue", palettes are sorted by hue. If sort="luminance", palettes are sorted by luminance.
show.names	If TRUE, show color names

Details

What to look for:

1. A good discrete palette has distinct colors.

2. A good continuous colormap does not show boundaries between colors. For example, the rainbow() palette is poor, showing bright lines at yellow, cyan, pink.

Examples

pal.bands(c('red','white','blue'), rainbow)

op=par(mar=c(0,5,3,1))
pal.bands(cubehelix, gnuplot, jet, tol.rainbow, inferno,
  magma, plasma, viridis, parula, n=200, gap=.05)
par(op)

# Examples of sorting
labs=c('alphabet','alphabet2', 'glasbey','kelly','polychrome', 'watlington')
op=par(mar=c(0,5,3,1))
pal.bands(alphabet(), alphabet2(), glasbey(), kelly(),
  polychrome(), watlington(), sort="hue",
  labels=labs, main="sorted by hue")
par(op)
pal.bands(alphabet(), alphabet2(), glasbey(), kelly(),
  polychrome(), watlington(), sort="luminance",
  labels=labs, main="sorted by luminance")

---

Show the red, green, blue, gray amount in colors of a palette

Description

The amount of red, green, blue, and gray in colors are shown.

Usage

pal.channels(pal, n = 150, main = "")

Arguments

pal	A palette function or a vector of colors.
n	The number of colors to display for palette functions.
main	Main title.

Details

What to look for:

1. Sequential data should usually be shown with a colormap that is smoothly increasing in lightness, as shown by the gray line.

Value

None

Author(s)

Kevin Wright

References

None

Examples

pal.channels(parula)
pal.channels(coolwarm)
# pal.channels(glasbey) # Nonsensical.

---

Show a palette with hierarchical clustering

Description

The palette colors are converted to LUV coordinates before clustering. (RGB coordinates are available, but not recommended.)

Usage

pal.cluster(pal, n = 50, type = "LUV", main = "")

Arguments

pal	A palette function or a vector of colors.
n	The number of colors to display for palette functions.
type	Either "LUV" (default) or "RGB".
main	Title to display at the top of the test image

Details

What to look for:

Colors that are visually similar tend to be clustered together.

Value

None

Author(s)

Kevin Wright

References

None

Examples

pal.cluster(alphabet(), main="alphabet")
pal.cluster(glasbey, main="glasbey") # two royal blues are very similar
pal.cluster(kelly, main="kelly") # two black-ish colors are very similar
# pal.cluster(watlington, main="watlington")
# pal.cluster(coolwarm(15), main="coolwarm") # curiously, grey clusters with blue

---

Compress a colormap function to fewer colors

Description

Compress a colormap function to fewer colors

Usage

pal.compress(pal, n = 5, thresh = 2.5)

Arguments

pal	A colormap function or a vector of colors.
n	Initial number of colors to use for the basis.
thresh	Maximum allowable Lab distance from original palette

Details

Colormap functions are often defined with many more colors than needed. This function compresses a colormap function down to a sample of colors that can be passed into 'colorRampPalette' and re-create the original palette with a just-noticeable-difference.

Colormaps that are defined as a smoothly varying ramp between a set of colors often compress quite well. Colormaps that are defined by functions may not compress well.

Value

A vector of equally-spaced colors that form the 'basis' of a colormap.

Author(s)

Kevin Wright

References

None.

Examples

# The 'cm.colors' palette in R compresses to only 3 colors
cm2 <- pal.compress(cm.colors, n=3)
pal.bands(cm.colors(255), colorRampPalette(cm2)(255), cm2,
labels=c('original','compressed','basis'), main="cm.colors")

# The 'heat.colors' palette needs 84 colors
heat2 <- pal.compress(heat.colors, n=3)
pal.bands(heat.colors(255), colorRampPalette(heat2)(255), heat2,
labels=c('original','compressed','basis'), main="heat.colors")

# The 'topo.colors' palette needs 249 colors because of the discontinuity
# topo2 <- pal.compress(topo.colors, n=3)
# pal.bands(topo.colors(255), colorRampPalette(topo2)(255), topo2,
# labels=c('original','compressed','basis'), main="topo.colors")

# smooth palettes usually easy to compress
p1 <- coolwarm(255)
cool2 <- pal.compress(coolwarm)
p2 <- colorRampPalette(cool2)(255)
pal.bands(p1, p2, cool2,
labels=c('original','compressed', 'basis'), main="coolwarm")
pal.maxdist(p1,p2) # 2.33

---

Show a colormap with a Campbell-Robson Contrast Sensitivity Chart

Description

In a contrast sensitivity figure as drawn by this function, the spatial frequency increases from left to right and the contrast decreases from bottom to top. The bars in the figure appear taller in the middle of the image than at the edges, creating an upside-down "U" shape, which is the "contrast sensitivity function". Your perception of this curve depends on the viewing distance.

Usage

pal.csf(pal, n = 150, main = "")

Arguments

pal	A continuous colormap function
n	The number of colors to display for palette functions.
main	Main title.

Details

What to look for:

1. Are the vertical bands visible across the full vertical axis?

2. Do the vertical bands blur together?

Value

None

Author(s)

Kevin Wright

References

Izumi Ohzawa. Make Your Own Campbell-Robson Contrast Sensitivity Chart. http://ohzawa-lab.bpe.es.osaka-u.ac.jp/ohzawa-lab/izumi/CSF/A_JG_RobsonCSFchart.html

Campbell, F. W. and Robson, J. G. (1968). Application of Fourier analysis to the visibility of gratings. Journal of Physiology, 197: 551-566.

Examples

pal.csf(brewer.greys) # Classic example from psychology
pal.csf(parula)

---

Show one palette/colormap in three dimensional RGB or LUV space

Description

The palette is converted to RGB or LUV coordinates and plotted in a three-dimensional scatterplot. The LUV space is probably better, but it is easier to tweak colors by hand in RGB space.

Usage

pal.cube(pal, n = 100, label = FALSE, type = "RGB")

Arguments

pal	A palette/colormap function or a vector of colors.
n	The number of colors to display for palette functions.
label	If TRUE, show color name/value on plot
type	Either "RGB" (default) or "LUV".

Details

What to look for:

A good palette has colors that are spread somewhat uniformly in 3D.

Note: The rgl package is NOT included in "Depends" for the pals package because it can cause problems for people. You might have to manually install rgl with install.packages.

Value

None

References

None

Examples

## Not run: 
pal.cube(cubehelix)
pal.cube(glasbey, n=32) # RGB, blues are too close to each other
pal.cube(glasbey, n=32, type="LUV")
pal.cube(cols25(25), type="LUV", label=TRUE)
# To open a second cube
rgl.open() # Open a new RGL device
rgl.bg(color = "white") # Setup the background color
pal.cube(colors()[c(1:152, 254:260, 362:657)]) # All R non-grey colors

## End(Not run)

---

Measure the pointwise distance between two palettes

Description

Measure the pointwise distance between two palettes

Usage

pal.dist(pal1, pal2, n = 255)

Arguments

pal1	A color palette (function or vector)
pal2	A color palette (function or vector)
n	Number of colors to use, default 255

Details

The distance between two palettes (of equal length) is calculated pointwise using the Lab color space. A 'just noticeable difference' between colors is roughly 2.3.

Value

A vector of n distances.

Author(s)

Kevin Wright

References

https://en.wikipedia.org/wiki/Color_difference

Examples

pa0 <- c("#ff0000","#00ff00","#0000ff")
pa1 <- c("#fa0000","#00fa00","#0000fa") # 2.4
pa2 <- c("#f40000","#00f400","#0000f4") # 5.2
pal.dist(pa0,pa1) # 1.87, 2.36, 2.11
pal.dist(pa0,pa2) # 4.12 5.20 4.68
pal.bands(pa1,pa0,pa2, labels=c("1.87  2.36  2.11","0","4.12  5.20  4.68"))
title("Lab distances from middle palette")

---

Show a palette/colormap with a random heatmap

Description

Show a palette/colormap with a random heatmap

Usage

pal.heatmap(pal, n = 25, miss = 0.05, main = "")

Arguments

pal	A palette function or a vector of colors.
n	The number of squares vertically in the heatmap.
miss	Fraction of squares with missing values, default .05.
main	Main title

Value

None.

Author(s)

Kevin Wright

References

None

Examples

pal.heatmap(brewer.paired, n=12)
pal.heatmap(coolwarm, n=12)
pal.heatmap(tol, n=12)
pal.heatmap(glasbey, n=32)
pal.heatmap(kelly, n=22, main="kelly", miss=.25)

---

Show palettes/colormaps with comparison heatmaps

Description

Draw a heatmap for each palette. Each palette heatmap consists of a block of randomly-chosen colors, plus a block for each color with random substitutions of the other colors. A missing value NA is added to each palette of colors.

Usage

pal.heatmap2(..., n = 100, nc = 6, nr = 20, labels = NULL)

Arguments

...	Palettes/colormaps, each of which is either (1) a vectors of colors or (2) a function returning a vector of colors.
n	The number of colors to display for palette functions.
nc	The number of columns in each color block.
nr	The number of rows in each color block.
labels	Vector of labels for palettes

Value

None

Author(s)

Kevin Wright

References

None

Examples

pal.heatmap2(watlington(16), tol.groundcover(14), brewer.rdylbu(11),
  nc=6, nr=20,
  labels=c("watlington","tol.groundcover","brewer.rdylbu"))

---

Show a palette using a map of U.S. counties

Description

What to look for:

Usage

pal.map(pal = brewer.paired, n = 12, main = "")

Arguments

pal	A palette function or a vector of colors.
n	Number of colors to return.
main	Main title

Details

1. Are regions distinct?

2. Are outliers within each region distinct?

Display a palette on a choropleth map similar to ColorBrewer.

Broad bands of color are easy to distinguish. Does the palette allow visibility of outlier counties in the larger regions? Does the palette allow identification of colors when the pattern is more complex (as in the lower left corner of the map)?

Notes. The map shown by the ColorBrewer website is an SVG here https://github.com/axismaps/colorbrewer/tree/master/map/map.svg which contains the class identifier for each polygon, for 3 to 12 classes. Unfortunately, the polygons have no other identification (e.g. FIPS, county name). We used the identify.map function in R to manually define the classes similar to the 12-class map of ColorBrewer. This proved to be too tedious to do more than once, so our maps of 1-11 classes were created by combining classes from the 12-class map. The ColorBrewer website sometimes used this strategy to combine classes, but not always. The 'outlier' counties and 'random region' in this version are very similar to the 12-region map of ColorBrewer, but there are a few differences, mostly intentional. Also, the map projection used here is different from ColorBrewer.

Value

None

Author(s)

Kevin Wright

References

http://www.personal.psu.edu/cab38/Pub_scans/Brewer_pubs.html Map based on www.ColorBrewer.org, by Cynthia A. Brewer, Penn State.

Examples

pal.map(brewer.paired, main="brewer.paired")
pal.map(parula)

## Not run: 
for(i in 3:12){
  pal.map(n=i, main=i)
}

## End(Not run)

---

Measure the maximum distance between two palettes

Description

Measure the maximum distance between two palettes

Usage

pal.maxdist(pal1, pal2, n = 255)

Arguments

pal1	A color palette (function or vector)
pal2	A color palette (function or vector)
n	Number of colors to use, default 255

Details

The distance between two palettes (of equal length) is calculated pointwise using the Lab color space. A 'just noticeable difference' between colors is roughly 2.3.

Value

Numeric value of the maximum distance.

Author(s)

Kevin Wright

References

https://en.wikipedia.org/wiki/Color_difference

Examples

pa0 <- c("#ff0000","#00ff00","#0000ff")
pa1 <- c("#fa0000","#00fa00","#0000fa") # 2.4
pa2 <- c("#f40000","#00f400","#0000f4") # 5.2
pal.maxdist(pa0,pa1) # 2.36
pal.maxdist(pa0,pa2) # 5.20
pal.bands(pa1,pa0,pa2, labels=c("2.36","0","5.20"))
title("Maximum Lab distance from middle palette")

# distance between colormap functions
pal.maxdist(coolwarm,warmcool)

---

Show a palette/colormap for black/white and colorblind safety

Description

A single palette/colormap is shown (1) without any modifications (2) in black-and-white as if photocopied (3) as seen by deutan color-blind (4) as seen by protan color-blind (5) as seen by tritan color-blind

Usage

pal.safe(pal, n = 100, main = NULL)

Arguments

pal	A palette function or a vector of colors.
n	The number of colors to display for palette functions.
main	Title to display at the top of the test image

Details

Rates of colorblindness in women are low, but in men the rates are around 3 to 7 percent, depending on the race.

What to look for:

1. Are colors still unique when viewed in less-than full color?

2. Is a sequential colormap still sequential?

Value

None.

Author(s)

Kevin Wright

References

Vischeck. http://www.vischeck.com/vischeck/

None

Examples

pal.safe(glasbey)
pal.safe(rainbow, main="rainbow") # Really, really bad
pal.safe(cubicyf(100), main="cubicyf")
pal.safe(parula, main="parula")

---

Show a colormap with a scatterplot

Description

What to look for:

Usage

pal.scatter(pal, n = 50, main = "")

Arguments

pal	A palette function or a vector of colors.
n	The number of colors to display for palette functions.
main	Main title

Details

1. Can the colors of each point be uniquely identified?

Value

None.

Author(s)

Kevin Wright

References

None.

Examples

pal.scatter(glasbey, n=31, main="glasbey") # FIXME add legend
pal.scatter(parula, n=10) # not a good choice

---

Show a colormap with a sineramp

Description

The test image shows a sine wave superimposed on a ramp of the palette. The amplitude of the sine wave is dampened/modulated from full at the top of the image to 0 at the bottom.

Usage

pal.sineramp(
  pal,
  n = 150,
  nx = 512,
  ny = 256,
  amp = 12.5,
  wavelen = 8,
  pow = 2,
  main = ""
)

Arguments

pal	A palette function or a vector of colors.
n	The number of colors to display for palette functions.
nx	Number of 'pixels' horizontally (approximate).
ny	Number of 'pixels' vertically
amp	Amplitude of sine wave, default 12.5
wavelen	Wavelength of sine wave, in pixels, default 8.
pow	Power for dampening the sine wave. Default 2. For no dampening, use 0. For linear dampening, use 1.
main	Main title

Details

The ramp function that the sine wave is superimposed upon is adjusted slightly for each row so that each row of the image spans the full data range of 0 to 255. The wavelength is chosen to create a stimulus that is aligned with the capabilities of human vision. For the default amplitude of 12.5, the trough to peak distance is 25, which is about 10 percent of the 256 levels of the ramp. Some color palettes (like 'jet') have perceptual flat areas that can hide fluctuations/features of this magnitude.

What to look for:

1. Is the sine wave equally visible horizontally across the entire image?

2. At the bottom, is the ramp smooth, or are there features like vertical bands?

Value

None

Author(s)

Concept by Peter Kovesi. R code by Kevin Wright.

References

Peter Kovesi (2015). Good Colour Maps: How to Design Them. http://arxiv.org/abs/1509.03700.

Peter Kovesi. A set of perceptually uniform color map files. http://peterkovesi.com/projects/colourmaps/index.html

Peter Kovesi. CET Perceptually Uniform Colour Maps: The Test Image. http://peterkovesi.com/projects/colourmaps/colourmaptestimage.html

Original Julia version by Peter Kovesi from: https://github.com/peterkovesi/PerceptualColourMaps.jl/blob/master/src/utilities.jl

Examples

pal.sineramp(parula)
pal.sineramp(jet) # Bad: Indistinct wave in green at top. Mach bands at bottom.
pal.sineramp(brewer.greys(100))

---

Show a colormap with multiple images

Description

1. Z-curve

Usage

pal.test(pal, main = substitute(pal))

Arguments

pal	A palette function or a vector of colors.
main	Title to display at the top of the test image

Details

2. Contrast Sensitivity Function.

3. Frequency ramp. See: http://inversed.ru/Blog_2.htm Are the vertical bands visible across the full vertical axis?

4. 5. Two images of the 'volcano' elevation data in R using forward/reverse colors. Try to find the highest point on the volcano peak. Many palettes with dark colors at one end of the palette hide the peak (e.g. viridis). Also try to decide if the upperleft and upperright corners are the same color.

6. Luminosity in red, green, blue, and grey.

Value

None.

Author(s)

Kevin Wright

References

# See links above.

Examples

pal.test(parula)
pal.test(viridis) # dark colors are poor
pal.test(coolwarm)

---

Show a colormap with a surface of volcano elevation

Description

Some palettes with dark colors at one end of the palette hide the shape of the volcano in the dark colors. Viridis is bad.

Usage

pal.volcano(pal, n = 100, main = "")

Arguments

pal	A palette function or a vector of colors.
n	The number of colors to display for palette functions.
main	Main title

Details

What to look for:

1. Can you locate the highest point on the volcano?

2. Are the upper-right and lower-right corners the same elevation?

3. Do any Mach bands circle the peak?

Value

None.

Examples

pal.volcano(parula)
pal.volcano(brewer.rdbu) # Mach banding is bad
pal.volcano(warmcool, main="warmcool") # No Mach band 
pal.volcano(rev(viridis(100))) # Bad: peak position is hidden

---

Show a colormap with a space-filling z-curve

Description

Construct a Z-order curve, coloring cells with a colormap. The difference in color between squares side-by-side is 1/48 of the full range. The difference in color between one square atop another is 1/96 the full range.

Usage

pal.zcurve(pal, n = 64, main = "")

Arguments

pal	A continuous color palette function
n	Number of squares for the z-curve
main	Main title

Details

What to look for:

1. A good color palette of 64 colors should be able to resolve 4 sub-squares within each of the 16 squares.

Value

None

Author(s)

Kevin Wright.

References

Peter Karpov. 2016. In Search Of A Perfect Colormap. https://twitter.com/inversed_ru

Z-order curve. https://en.wikipedia.org/wiki/Z-order_curve

Examples

pal.zcurve(parula,n=4,main="parula")
pal.zcurve(parula,n=16)
pal.zcurve(parula,n=64)
pal.zcurve(parula,n=256)

---

pals: A package for comprehensive palettes and palette evaluation tools

Description

pals: A package for comprehensive palettes and palette evaluation tools

Details

The terms 'palette' and 'colormap' are often interchanged. In this package (1) 'palette' is usually a discrete set of distinct colors and (2) 'colormap' is usually a smoothly varying set of many colors.

The best palette/colormap is determined by (1) the type of structure in the data, (2) the type of graphic to be constructed, and (3) the type of device used to show the graphic. The ColorBrewer website approaches this problem by suggesting different colors for qualitative, sequential, and diverging data, and also considers the display of the graphic on LCD and photocopies. One limitation with ColorBrewer is that it only uses maps, and does not consider other types of graphics. For example, yellow colors work well for polygons (on maps, barcharts, etc), but are poor for lines and scatter plots.

The 'pals' package provides a suite of tools to evaluate palettes/colormaps.

The design goals of the package are:

• All palettes/colormaps are functions that return a vector of colors.

• The palette function names use only lowercase letters.

• The 'data' directory is not used.

• Provide an extensive collection of palettes and colormaps.

• Be memory efficient. Colormaps are compressed.

• Provide multiple tools to evaluate palettes.

To learn more, see the vignettes: browseVignettes(package="pals")

---

Pals max colors

Description

This function returns a data frame with the maximum number of colors for each palette currently available within the pals package.

Usage

pals.maxcolors()

Value

A data frame with the maximum number of colors for each palette.

Author(s)

R code by Brian M Schilder.

Examples

dat <- pals.maxcolors()

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Seismic data horizon offshore of Nova Scotia.

Description

Seismic data offsore of Nova Scotia in Canada. The data have some subtle structures that are interesting for comparing colormaps. Full details can be found at https://www.opendtect.org/osr/Main/PENOBSCOT3DSABLEISLAND License CC-BY.

Usage

data(penobscot)

Format

A matrix 463 x 595.

Source

https://github.com/agilescientific/notebooks https://github.com/agilescientific/notebooks/blob/master/Filtering_horizons.ipynb

Examples

#
library(pals)
data(penobscot)

# Hall used cubehelix palette
# http://wiki.seg.org/wiki/Smoothing_surfaces_and_attributes#External_links
image(penobscot, col=rev(cubehelix(99)))

# Niccoli suggested LinearL palette
# http://wiki.seg.org/wiki/How_to_evaluate_and_compare_color_maps
image(penobscot, col=linearl(99))

# Use this version to get a colorkey
# library(lattice)
# levelplot(penobscot, col.regions=rev(cubehelix(99)), 
#   cuts=97, asp=0.7, scale=list(draw=FALSE))

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