Institutional colors
-SPDS, -uni.kn
- -2023 08 09
- - Source:vignettes/color_inst.Rmd
- color_inst.Rmd
The color palettes included in the unikn package are -based on the CD manual of the University of Konstanz. However, -the functionality provided by the package makes it easy and -straightforward to define and use your own colors.
- -Version 0.8.0 of unikn (released in 2023-01-06) -contained 52 additional color palettes from other institutions (mostly -universities). As the number of color palettes has been growing beyond -the scope of this package, the color palettes from other institutions -are now being collected in a separate R package unicol. -Installing unicol (from GitHub) currently provides -access to a range of over 150 color palettes.
- -The rest of this vignette explains how the color-related tools of -unikn can be used for creating new color -palettes. If you do so, please consider submitting it to be -included in future versions of the unicol package. - (For a general introduction of the -unikn colors, color palettes, and color functions, see -the vignettes on Colors and color functions -and Color recipes. The vignette on Text boxes and decorations provides information on -plotting slides and text elements.)
-We install or load the unikn package to get -started:
- -Creating new color palettes -
-A color palette is an ordered collection of color objects. Creating -new color palettes in R is simple and only requires two steps:
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-
Define the palette’s elementary colors as R objects.
-Combine colors into a color palette.
-
In the following, we illustrate both steps. As we will see, Step 1 -can either be omitted (when the to-be-combined colors exist as R -objects) or can be included in Step 2.
-As an example, we will re-create the color palette of the German Max Planck Society (that
-unikn exports as mpg_pal) from the color
-definitions in its Corporate Design manual (available here).
1. Defining colors -
-Before we can a create new color palette in R, its elementary colors -need to exist as R objects. (If all colors to-be-combined into a new -palette exist as R objects, we can skip this step and directly proceed -to 2. Combining colors into a palette.) Defining new -colors in R requires a basic understanding of their underlying -representation.
-How colors are represented and perceived is a wide-ranging topic that -has fascinated scholars and practitioners for centuries. Essentially, -there are many alternative ways to analyze or synthesize colors, and the -most appropriate representational way depends on the specific context of -a particular use case (see Wikipedia: Color -model for an introduction).
-In R, the grDevices package (included in any default -installation) provides colors and color-related functions. Although R -supports many color models (including HCL, HSV, Lab, Luv, and RGB -variants), the three most common ways of expressing colors in R are:
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-
by R color name: R includes 657 named colors, whose names are -provided (as a character vector) by evaluating the
colors()-function of grDevices.
-by RGB values: Every color (or hue) is expressed by a triple of -numeric values on three dimensions that denote the additive primary -colors (red, green, blue). The range of values is either expressed on a -scale from 0 to 1, or — more typically — on a scale from 0 to 255 (i.e., -on an 8 bit scale allowing for \(2^8 = -256\) different values).
-by HEX values: Every color (or hue) is expressed by a character -string (with the prefix
#), followed by a triple of -hexadecimal values (each ranging from00-toFF) that correspond to the triple of decimal RGB values -(in the range from 0 to 255).
-
Importantly, the same color (or hue) can be denoted by its -name, its RGB values, and its HEX values. Thus, the three different -systems (i.e., color name, RGB values, or HEX values) can be -alternative representations of the same object. However, just -as the properties of an alphabet or numeral notation system constraints -the words or numbers that can be expressed by it, the universe of -possible colors is limited by their representation. For instance, -whereas the RGB and HEX systems usually distinguish between more than -16 million (i.e., \((2^8)^3 = 256^3 = -16.777.216\)) possible colors, only a limited number of them are -named. Similarly, when multiple names refer to the same triple of RGB or -HEX values, they denote the same color.1
- - -An example helps clarifying these abstract terms. When viewing an
-existing color or color palette with the seecol() function
-of unikn, the name, HEX and RGB values of the
-individual colors are being shown. For instance, the following color
-palette mpg_pal provides the colors of the Max Planck Society:
As the middle color of mpg_pal is one of the 657 named
-colors of colors(), R will recognize it as a color when we
-type its name (as a character object "white") in a context
-that expects color inputs. However, the two other colors of
-mpg_pal have specifically been defined by the Max Planck Society and thus are
-not pre-defined colors of R (i.e., not contained in
-colors()). To use such colors in R, we first need to define
-them as color objects.
So how can we define new colors in R? Most institutions will provide -definitions of their corporate colors or instructions how they can be -defined in some standard color system. Unfortunately, some definitions -(such as those using the subtractive CMYK model) are aimed at printing -(as opposed to computer screens) and other standards (such as numeric -PANTONE™ PMS codes) are proprietary. But as most institutions have an -interest in widely disseminating their colors, they typically include -translations or approximations for alternative color models in their -definitions.
- -
The Max Planck Society defines -two main colors in its CD manual (available here):
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-
A dark shade of green is defined as Pantone 328, and approximated -by CMYK 100/0/57/30 or RGB 17/102/86.
-A light shade of grey is Pantone 427, and approximated by -CMYK 0/0/6/15 or RGB 221/222/214.
-
In the following, we will first use the RGB values to define both -colors (as these are provided in the definition). However, we then show -that we alternatively could have used the colors’ HEX values (had they -been provided) to define the same color objects.
-Defining colors from their RGB values -
-As our two colors of interest are given in RGB values, we can use the
-rgb() function of the grDevices package to
-define corresponding objects:
-mpg_green <- grDevices::rgb( 17, 102, 86, maxColorValue = 255)
-mpg_grey <- grDevices::rgb(221, 222, 214, maxColorValue = 255)The first three arguments specify values on the red,
-green, and blue dimension, respectively.
-Importantly, the values must be in the range \([0, max]\), where \(max\) is provided by
-maxColorValue = 255. Whereas the sRGB standard expresses
-all values in a normalized range of \([0,
-1]\), most color definitions use the traditional 8-bit
-representation that distinguish between \(2^8
-= 256\) levels in the range \([0,
-255]\). The rgb() function additionally allows for
-adding names and a transparency level alpha,
-but as we can modify these aspects later, we do not need to use them at
-this point.
What do we obtain when we evaluate our new color objects? Let’s -see:
-
-mpg_green
-#> [1] "#116656"
-mpg_grey
-#> [1] "#DDDED6"When evaluating an object created by rgb(), R returns a
-character sequence of the form "#rrggbb". In this string,
-the sequence rrggbb expresses the three RGB values (for the
-red, green, and blue dimension,
-respectively) in hexadecimal (HEX) notation (in the range \([0, 255]\)). In HEX notation, the ten
-numeral symbols (i.e., the digits from 0 to 9) or the decimal system are
-extended by the first six letters of the alphabet (i.e., A to F) to turn
-our familiar base-10 notation into an analogous place-value notation
-with a base of 16. Thus, the HEX values of "#116656" is
-merely a more compact way of expressing
-
-
-
-
red: \(1 \cdot 16 + 1 = -17\) -
- -
-
green: \(6 \cdot 16 + 6 = -102\) -
- -
-
blue: \(5 \cdot 16 + 6 = -86\) -
-
Thus, the HEX values "#116656" provide an alternative
-way of expressing the RGB values 17/102/86 — and both refer to the color
-“MPG green” (shown as mpg_pal[1] above).
Defining colors from their HEX values -
-The equivalence of the RGB and HEX notations can be illustrated by -defining the same colors a second time, but this time specifying their -HEX values (returned above):
-
-mpg_green_2 <- "#116656"
-mpg_grey_2 <- "#DDDED6"If both definitions really yield identical R objects, the following
-checks must evaluate to TRUE:
Thus, we can define colors from both their RGB or HEX values — and -the two notations are really just two alternative ways of representing -the same colors.
-2. Combining colors into a palette -
-Having defined two new colors, we can easily combine them into a new -color palette. A color palette is simply a sequence of colors -that are stored in some sequential data structure. The two simplest data -structures in R that are suited for this purpose are vectors or data -frames. Thus, the following two expressions both define valid color -palettes:
-
-c(mpg_green, mpg_grey)
-#> [1] "#116656" "#DDDED6"
-data.frame(mpg_green, mpg_grey)
-#> mpg_green mpg_grey
-#> 1 #116656 #DDDED6As it often is easier to remember and access colors by their names,
-it usually makes sense to name the elements of a color palette (e.g., by
-using the name() function).
The newpal() function of unikn provides
-a convenient wrapper for defining color palettes. Its main argument
-col uses a vector of color objects that make up the new
-palette, whereas names and as_df allow
-specifying color names and switching between returning the new palette
-as a vector or data base.
-Importantly, the color objects of col can be provided as
-RGB values, as R color names, or as HEX values:
-mpg_pal_3 <- newpal(col = c(rgb( 17, 102, 86, maxColorValue = 255), "white", "#DDDED6"),
- names = c("MPG green", "white", "MPG grey"),
- as_df = FALSE)The new color palette mpg_pal_3 contains the three basic
-colors of mpg_pal (shown above):
-seecol(mpg_pal_3,
- main = "My new color palette", col_brd = "black", lwd_brd = .5)
The 5-color palette provided as mpg_pal includes two
-intermediate colors (at positions mpg_pal[2] and
-mpg_pal[4]) that are extrapolated by mixing the two main
-MPG colors with “white” in a balanced (50:50) fashion. The
-usecol() function of unikn automatically
-performs these extrapolations when extending a 3-element color palette
-(e.g., mpg_pal_3) to n = 5 colors:
-my_mpg_pal <- usecol(mpg_pal_3, n = 5)The resulting color palette my_mpg_pal recreates the
-colors of the unikn color palette mpg_pal
-(shown above):
-seecol(my_mpg_pal, main = "My new MPG color palette",
- col_brd = "black", lwd_brd = .5)
Technically, the two color palettes differ in some names and the way
-in which its middle color (“white”) was specified (by its R color name
-or HEX values). However, when using col2rgb() for
-converting both palettes into their RGB values, we see that they contain
-the same colors:
-all.equal(col2rgb(mpg_pal), col2rgb(my_mpg_pal), check.attributes = FALSE)
-#> [1] "Numeric: lengths (9, 15) differ"This concludes our illustration of defining a new colors and color -palettes.
-Comparing color palettes -
-Once a new palette has been defined (and is available in your current
-R environment), the seecol() and usecol()
-functions allow comparing, scaling, and using them in the same fashion
-as the native unikn color palettes:
-# Scaled palettes:
-seecol(list(mpg_pal, pal_unikn), n = 7,
- pal_names = c("Max Planck", "Uni Konstanz"),
- main = "Comparing scaled color palettes")
See the vignette on Color recipes -for additional examples of using unikn functions for -solving color-related tasks.
- -Call for contributions -
-We are still expanding the range of color palettes from other -institutions in a dedicated R package unicol.
-Collecting color palettes
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-
- Are you using the unikn color functions to create -your own color palettes? -
If so, please let us know (e.g., by posting on this
-GitHub
-issue)!
-We collect color palettes from other institutions to make them available
-to R users in future versions of unicol.
To enable us to verify and provide credit to your contributions, -please send us the following information:
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- your code (e.g., the
newpal()command creating your -color palette),
-
- - your reference or source information (e.g., some URL with color
-definitions),
-
- - your name and some contact information (e.g., an Email -address). -
We’re looking forward to your inputs and contributions (at this GitHub issue)!
-Resources -
-The following versions of unikn and corresponding -resources are currently available:
-| Type: | -Version: | -URL: | -
|---|---|---|
| A. unikn (R package): | -Release version | -https://CRAN.R-project.org/package=unikn | -
| - | Development -version | -https://github.com/hneth/unikn/ | -
| B. Online documentation: | -Release -version | -https://hneth.github.io/unikn/ | -
| - | Development version | -https://hneth.github.io/unikn/dev/ | -
Vignettes -
- - -The following vignettes provide instructions and examples for using -the unikn colors, color palettes, and functions:
- -| Nr. | -Vignette | -Content | -
|---|---|---|
| 1. | -Colors | -Colors and color functions | -
| 2. | -Color recipes | -Recipes for color-related tasks | -
| 3. | -Institutional colors | -Creating color palettes for other institutions | -
| 4. | -Text | -Text boxes and decorations | -
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Strictly speaking, different shades of a given color can -be further distinguished by different opacity or -transparency levels (typically denoted by an
alpha-value). However, the visual appearance of a transparent color can be -mimicked by mixing the color with the background color (usually -“white”). Hence, transparent shades of a color are usually not counted -as different colors.↩︎
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