Colors and color functions

A. Viewing details of a single color palette

When the pal argument of the seecol() function specifies a single color palette, the function plots a more detailed view of this particular color palette:

seecol(pal_unikn)  # view details of pal_unikn 

The detailed overview of a color palette provides us with

• the color names (where available),
  
• their numeric indices (within the color palette),
  
• the HEX values for each color,
  
• the RGB values for each color.

When a color palette contains too many colors, the HEX and RGB values are no longer printed. However, setting hex and rgb to TRUE will force them to be shown.

Note that seecol() also returns the color palette that is being shown. Thus, a typical workflow comprises both seeing a particular color palette and saving it (for storing and applying it later):

my_pal <- seecol(pal_unikn_light)  # view details of AND save a color palette  

Due to saving the color palette (here to my_pal) we can later use it in a visualization:

barplot(1/sqrt(1:10), col = my_pal)  # use my_pal in a plot

Note that seecol() invisibly returns the color palette.
Thus, the following will plot the palette pal_bordeaux without doing anything else with it:

seecol(pal_bordeaux)

but the following would both plot and assign the palette to my_pal:

my_pal <- seecol(pal_bordeaux)

B. Viewing and comparing multiple color palettes

The second mode of seecol() is invoked by providing (a list of) multiple color palettes to its pal argument.
In this case, the function allows comparing these palettes by plotting a color vector for each palette. Some special keywords within the unikn package denote sets of color palettes:

• The keywords "unikn_all", "unikn_basic", pair_all", "pref_all" and "grad_all" refer to University of Konstanz color palettes.

• The keywords "all" and "add" allow comparing all or only the additional color palettes, respectively.

Calling seecol with pal set to these keywords allows comparing pre-defined sets of the color palettes:

Viewing the uni.kn color palettes:

seecol("unikn_all")  # all uni.kn color palettes

1. three basic color palettes:

seecol("unikn_basic")

Note, that pal_unikn_web and pal_unikn_ppt are almost identical, but differ in how vibrant their colors are.

2. three paired color palettes:

seecol("pair_all")

3. all preferred colors from the spectrum and their respective gradients:

seecol("pref_all")

4. only the pre-defined color gradients:

seecol("grad_all")

All additional color palettes (from other institutions):

seecol("add")

See the vignette on Institutional colors for creating color palettes for other institutions.

Other seecol() arguments

The seecol() function provides some aesthetic parameters for adjusting how color palettes are plotted:

• col_brd allows specifying the color of box borders (if shown. Default: col_brd = NULL);
  
• lwd_brd allows specifying the line width of box borders (if shown. Default: lwd_brd = NULL);
  
• main and sub allow replacing the default titles with custom titles.

Examples:

seecol("grad_all", col_brd = "black", lwd_brd = .5, main = "Color gradients (with black borders)")

seecol(pal_seegruen, col_brd = "white", lwd_brd = 5, main = "A color palette (with white borders)")

Selecting subsets

Using only a subset of colors:

seecol("unikn_all", n = 4)

seecol(pal_unikn, 4)

Importantly, when using pre-defined color palettes of unikn but a value of n that is smaller than the length of the current color palette, usecol and seecol select a predefined subset of colors:

barplot(1/sqrt(1:2), col = usecol(pal_seeblau, n = 2))
barplot(1/sqrt(1:3), col = usecol(pal_seeblau, n = 3))

Extending color palettes

For values of n that are larger than the number of available colors in pal, the specified color palette is extended using ColorRampPalette:

seecol("unikn_all", n = 12)

Both seecol() and usecol() allow extending or truncating color palettes to a desired number n of colors. For instance:

• Inspecting an extended version of pal_seeblau (with n = 8 colors):

seecol(pal_seeblau, n = 8)

• Using a truncated version of pal_bordeaux (with n = 3 colors):

barplot(1/sqrt(1:9), col = usecol(pal_bordeaux, n = 3))

Mixing colors and color palettes

By passing a vector to pal, we can concatenate 2 color palettes and connect them with a color (here: "white") as the midpoint of a new color palette:

seecol(pal = c(rev(pal_petrol), "white", pal_bordeaux))

We can combine a set of colors and extend this palette by specifying an n argument that is larger than the length of the specified palette:

seecol(pal = usecol(c(Karpfenblau, Seeblau, "gold"), n = 10))

These custom palettes can easily be used in a plot. For instance, we can define and use a subset of the pal_unikn_pair palette as follows:

my_pair <- seecol(pal_unikn_pair, n = 10)

# Create data: 
dat <- matrix(sample(5:10, size = 10, replace = TRUE), ncol = 5)

# Plot in my_pair colors:
barplot(dat, beside = TRUE, col = my_pair)

Creating linear color gradients is also supported by the shades_of() function (see below).

Controlling transparency

Both seecol() and usecol() accept an alpha argument (in a range from 0 to 1) for controlling the transparency of color palettes, with higher values for alpha corresponding to lower transparency (i.e., higher opacity).

Displaying a specific color palette at a medium opacity/transparency:

seecol(pal_unikn, alpha = 0.5)

Setting opacity for a custom color palette:

four_cols <- usecol(c("steelblue", "gold", "firebrick", "forestgreen"), alpha = 2/3)

seecol(four_cols, main = "Four named colors with added transparency")

Setting opacity for comparing of multiple color palettes:

seecol("grad", alpha = 0.67, main = "Seeing color palettes with added transparency")

Creating and comparing custom palettes

Suppose we want to compare a newly created color palette to existing color palettes. To achieve this, advanced users can use the seecol() function for displaying and comparing different custom palettes. When provided with a list of color palettes as the input to its pal argument, seecol() will show a comparison of the inputs:

# Define 2 palettes: 
pal1 <- c(rev(pal_seeblau), "white", pal_bordeaux)
pal2 <- usecol(c(Karpfenblau, Seeblau, "gold"), n = 10)

# Show the my_pair palette from above, the 2 palettes just defined, and 2 pre-defined palettes:  
seecol(list(my_pair, pal1, pal2, pal_unikn, pal_unikn_pair))

Note that unknown color palettes are named pal_\(n\), in increasing order. Palettes known to seecol() are labeled by their respective names. Labeling only custom palettes works by setting the pal_names argument to a character vector of appropriate length:

seecol(list(my_pair, pal1, pal2, pal_unikn, pal_unikn_pair), 
       pal_names = c("my_pair", "blue_bord", "blue_yell"),
       main = "Labeling custom color palettes")

If the pal_names argument is specified and corresponds to the length of all color palettes, the default names of all color palettes are overwritten by pal_names:

seecol(list(my_pair, pal1, pal2, pal_unikn, pal_unikn_pair), 
       pal_names = c("my_pair", "blue_bord", "blue_yell", "blue_black", "mix_pair"),
       main = "Comparing and labeling custom color palettes")

As before, we can use lower values of n for truncating/obtaining shorter subsets of color palettes:

seecol(list(my_pair, pal1, pal2, pal_unikn, pal_unikn_pair), n = 5)

or higher values of n for extending color palettes:

seecol(list(my_pair, pal1, pal2, pal_unikn, pal_unikn_pair), n = 15)

Finding similar colors with simcol()

Assuming that our favorite color is "deeppink", a good question is: How can we find similar colors? Given some target color, the simcol() function searches through a set of colors to find and return visually similar ones:

simcol("deeppink", plot = FALSE)
#>     deeppink    deeppink2      maroon1      maroon2   violetred1   violetred2 
#>   "deeppink"  "deeppink2"    "maroon1"    "maroon2" "violetred1" "violetred2"

By default, simcol() searches though all named R colors of colors() (of the grDevices package), but adjusting the col_candidates and tol arguments allows for more fine-grained searches:

simcol("deepskyblue", col_candidates = pal_seeblau, tol = c(50, 50, 100))
#>   deepskyblue      seeblau3      seeblau4      seeblau5 
#> "deepskyblue"     "#59C7EB"     "#00A9E0"     "#008ECE"

Finding color names with grepal()

We often search for some particular color hue (e.g., some sort of purple), but also know that the particular color named “purple” is not the one we want. Instead, we would like to see all colors that contain the keyword “purple” in its name. The grepal() function addresses this need:

grepal("purple")  # get & see 10 names of colors() with "purple" in their name

#>  [1] "mediumpurple"  "mediumpurple1" "mediumpurple2" "mediumpurple3"
#>  [5] "mediumpurple4" "purple"        "purple1"       "purple2"      
#>  [9] "purple3"       "purple4"

Note that the grepal() function allows searching color names by regular expressions:

length(grepal("gr(a|e)y", plot = FALSE))    # shades of "gray" or "grey"
#> [1] 224
length(grepal("^gr(a|e)y", plot = FALSE))   # shades starting with "gray" or "grey"
#> [1] 204
length(grepal("^gr(a|e)y$", plot = FALSE))  # shades starting and ending with "gray" or "grey"
#> [1] 2

By default, grepal() searches the vector of named colors x = colors() (of the grDevices package) and plots its results (as a side effect). However, it also allows for searching color palettes provided as data frames (with color names) and for suppressing the visualization (by setting plot = FALSE):

grepal("see", pal_unikn)   # finding "see" in (the names of) pal_unikn (as df)

#>   seeblau5 seeblau4 seeblau3 seeblau2 seeblau1 seegrau1 seegrau2 seegrau3
#> 1  #008ECE  #00A9E0  #59C7EB  #A6E1F4  #CCEEF9  #E5E5E5  #CCCCCC  #999999
#>   seegrau4
#> 1  #666666
grepal("blau", pal_unikn_pref, plot = FALSE)  # finding "blau" in pal_unikn_pref
#>   Seeblau Karpfenblau
#> 1 #59C7EB     #3E5496

Adjusting transparency with ac()

The ac() function provides a flexible wrapper around the adjustcolor() function of the grDevices package. Its key functionality is that it allows for vectorized col and alpha arguments:

my_cols <- c("black", "firebrick", "forestgreen", "gold", "steelblue")
seecol(ac(my_cols, alpha = c(rep(.25, 5), rep(.75, 5))))

The name ac is an abbreviation of “adjust color”, but also a mnemonic aid for providing “air conditioning”.

Creating color gradients with shades_of()

We have seen that the main usecol() function allows stretching and squeezing color palettes and thus creating complex color gradients. An even simpler way for creating linear color gradients is provided by the shades_of() function:

seecol(shades_of(n = 5, col_1 = Karpfenblau), main = "5 shades of Karpfenblau")

Internally, shades_of() is just a convenient wrapper for a special usecol() function. The limitation of shades_of() is that it only allows creating bi-polar color palettes (i.e., gradients between two colors). When the final color col_n is unspecified, its default of “white” is used (as in the example). By contrast, the usecol() function allows creating color gradients between an arbitrary number of colors. Thus, the following two expressions define the same bi-polar color palette:

pg_1 <- usecol(c("deeppink", "gold"), 5)
pg_2 <- shades_of(5, "deeppink", "gold")
all.equal(pg_1, pg_2)
#> [1] TRUE

seecol(pg_2, main = "A bi-polar color gradient")

Defining color palettes with newpal()

Having created, combined or found all those beautiful colors, we may wish to define a new color palette. Defining a new named color palette allows to consistently access and apply colors (e.g., to a series of visualizations in a report or publication). The newpal() function makes it easy to define color palettes:

col_flag <- c("#000000", "#dd0000", "#ffce00")  # source: www.schemecolor.com

flag_de  <- newpal(col = col_flag,
                   names = c("black", "red", "gold"))

seecol(flag_de, main = "Defining a flag_de color palette")

By default, newpal() returns the new color palette as a (named) vector. Setting as_df = TRUE returns a data frame.

Illustrating color palettes with demopal()

After choosing, creating or modifying a color palette, we usually inspect the result with seecol(). Alternatively, we can use the demopal() function to use a color palette pal in a visualization. Currently, the type argument supports the following visualizations:

1. “bar”: A bar plot
2. “curve”: A plot of normal distribution curves (with transparency)
3. “mosaic”: A mosaic/table plot
4. “polygon”: An area/polygon plot
5. “scatter”: A scatter plot of points (with transparency)

All types of demopal() invisibly return their (randomly generated) data and accept some graphical arguments (e.g., col_par and alpha), a scaling n and a seed value (for reproducible results), as well as main and sub arguments (for setting plot titles). Some functions additionally accept type-specific arguments (e.g., logical beside, horiz, and as_prop arguments for plot type = "bar").

demopal(pal = uni_freiburg_info, type = 3)

This concludes our quick tour through the colors and color functions of the unikn package. We hope that they enable you to find, design, and use beautiful color palettes — and spice up your visualizations by vivid and flamboyant colors!