options(max.print = "75") knitr::opts_chunk$set(echo = TRUE, cache = FALSE, prompt = FALSE, tidy = FALSE, collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE, # Default figure options: # fig.width = 6, # fig.asp = .8 # .618, # golden ratio fig.align = "center", out.width = "60%") # URLs: ------ # unikn / Uni Konstanz: url_unikn <- "https://www.uni-konstanz.de" # unikn package: url_unikn_cran <- "https://CRAN.R-project.org/package=unikn" url_unikn_github <- "https://github.com/hneth/unikn" # unicol package: url_unicol_cran <- "https://CRAN.R-project.org/package=unicol" url_unicol_github <- "https://github.com/hneth/unicol"
This vignette shows how various color-related tasks can be solved by the tools provided by unikn. (For a general introduction of the unikn colors, color palettes, and color functions, see the vignette on Colors and color functions. The vignette on text provides information on creating text boxes and decorations.)
The following recipes illustrate how plotting functions in R can use the colors and color palettes provided by the unikn package.
Additional examples show how we can use the seecol()
, usecol()
, newpal()
, and grepal()
functions for solving color-related tasks.
Please install and/or load the unikn package to get started:
# install.packages('unikn') # install unikn from CRAN client library('unikn') # loads the package
Task: Using unikn colors in visualizations created by base R graphics.
When creating base R visualizations (e.g., using plot()
for creating a scatterplot),
use the usecol()
function in combination with any color palette (e.g., pal_unikn
) as the col
\ argument:
opar <- par(no.readonly = TRUE) # store current settings par(mar = c(1, 1, 2.1, 1), oma = c(0, 0, 0, 0)) # reduce margins set.seed(6 * pi) # reproducible randomness
my_col <- usecol(pal_unikn, alpha = .50) # with transparency plot(x = runif(99), y = runif(99), type = "p", pch = 16, cex = 4, col = my_col, main = "99 transparent dots", axes = FALSE, xlab = NA, ylab = NA)
par(opar) # re-store current settings
Task: Using unikn colors in visualizations created by the ggplot2 package.
When using the ggplot()
function of ggplot2 (e.g., for creating an area plot),
use the usecol()
function for defining a color palette (of the desired length and transparency)
that is then provided as the values
of the scale_color_manual()
or scale_fill_manual()
functions:
Wrap the desired color palette in the usecol()
function of unikn.
Provide this palette as the values
of the ggplot2 functions scale_color_manual()
or scale_fill_manual()
.
# 0. Create data: ---- # Example based on https://www.r-graph-gallery.com/137-spring-shapes-data-art/ n <- 50 groups <- 1:n df <- data.frame() set.seed(3) for (i in seq(1:30)){ data = data.frame(matrix(0, n, 3)) data[, 1] <- i data[, 2] <- sample(groups, nrow(data)) data[, 3] <- prop.table(sample(c(rep(0, 100), c(1:n)), nrow(data))) df = rbind(df, data)} names(df) <- c("X","Group","Y") df$Group <- as.factor(df$Group) df <- df[c(2, 1, 3)] df <- df[order(df$X, df$Group) , ] rownames(df) <- NULL my_data <- df # 1. Colors: ---- # A. using RColorBrewer: # library(RColorBrewer) # cur_col <- brewer.pal(11, "Paired") # cur_col <- colorRampPalette(cur_col)(n) # cur_col <- cur_col[sample(c(1:length(cur_col)), size = length(cur_col))] # randomize # B. using unikn colors: library(unikn) # Mix a color gradient: cur_col <- usecol(c(Bordeaux, "white", Petrol), n = n) # cur_col <- cur_col[sample(c(1:length(cur_col)), size = length(cur_col))] # randomize # 2. Plotting: ---- library(ggplot2) ggplot(my_data, aes(x = X, y = Y, fill = Group)) + geom_area() + scale_fill_manual(values = cur_col) + theme_void() + theme(legend.position = "none")
Task: Inspect (the colors and details of) a color palette.
For easily viewing an individual color palette (e.g., provided by the new HCL color palettes hcl.pals()
of\ R),
use the palette as the pal
\ argument of the seecol()
function:
col_pal <- hcl.colors(10, "Vik") # some color palette seecol(col_pal) # see its colors and details
Note that the other arguments of the seecol()
function ---\ especially n
and alpha
\ --- can modify the palette (in the same ways as the usecol()
function).
Task: Compare (the colors and range of) multiple color palettes.
For comparing multiple color palettes (e.g., 20\ random color palettes of hcl.pals()
),
provide them as a list in the pal
\ argument of the seecol()
\ function.
For instance, let's create and compare a set of 20\ random HCL palettes:
set.seed(4 * pi) # reproducible randomenss
# Parameters: n_col <- 8 # colors in each palette n_pal <- 20 # length(hcl.pals()) ptype <- NULL # c("diverging", "divergingx", "qualitative", "sequential")[4] s_pal <- sample(x = hcl.pals(type = ptype), size = n_pal, replace = FALSE) s_pal <- sort(s_pal) # Initialize: t_lbl <- paste0("See ", n_pal, " random HCL color palettes (n = ", n_col, ")") l_pal <- vector("list", n_pal) # lists of palettes # Loop: for (i in 1:n_pal){ l_pal[[i]] <- hcl.colors(n = n_col, palette = s_pal[i]) }
seecol()
for comparing multiple color palettes: seecol(pal = l_pal, pal_names = s_pal, col_brd = "white", lwd_brd = 1, main = t_lbl)
Task: Find a color that is similar to some given color.
Use the simcol()
function for finding (and plotting/seeing) similar colors:
simcol("forestgreen")
The simcol()
function compares a require target color (col_target
) with
a set of candidate colors (col_candidates
, set to colors()
by default).
Color similarity is defined in terms of the colors' pair-wise distances in RGB values.
If this distance falls below some tolerance value(s) (tol
), the corresponding col_candidates
are shown and returned.
Thus, increasing the tolerance value yields a wider range of colors:
simcol(col_target = "orange", col_candidates = pal_unikn_pref, tol = 150)
Specifying a vector of three tol
values compares RGB dimensions in the order of their rank.
This allows for more fine-grained similarity searches:
simcol(col_target = Seeblau, tol = c(20, 20, 80))
Task: Create a new color palette (with color names).
The general steps for creating an new color palette (with dedicated color names) are:
Choose some colors (and their R color names, or as RGB/HEX/HCL values, e.g., from sites like https://www.schemecolor.com).
Define the colors as an\ R vector (of type character).
Define optional color names as a second R vector (of type character).
Use the newpal()
command to define the new color palette (as an\ R data frame or vector).
Once a new palette has been defined, it can be inspected (with seecol()
) or further modified and used in visualizations (with usecol()
):
# 1. Choose colors: # Google logo colors (from <https://www.schemecolor.com/google-logo-colors.php>) # 2. Define colors (as vector): color_google <- c("#4285f4", "#34a853", "#fbbc05", "#ea4335") # 3. Define color names (as vector): names_google <- c("blueberry", "sea green", "selective yellow", "cinnabar") # 4. Define color palette: pal_google <- newpal(color_google, names_google) # Inspect color palette: seecol(pal_google, col_brd = "white", lwd_brd = 8, main = "Colors of the Google logo")
The vignette on Institutional colors provides additional examples for defining color palettes.
Task: Find colors by matching their names to a pattern (as a regular expression).
Here are some additional examples for using the grepal()
function for finding colors by their names (or a pattern):
# Get color palettes matching a pattern: pal_1 <- grepal("orange", plot = FALSE) pal_2 <- grepal("olive", plot = FALSE) pal_3 <- grepal("white", plot = FALSE) # See individual palettes: # seecol(pal_1, main = "Hues of 'orange' colors()") # seecol(pal_2, main = "Hues of 'olive' colors()") seecol(pal_3, main = "Hues of 'white' colors()", col_bg = "grey90")
Providing a list of color palettes to the pal
argument of the seecol()
function allows comparing multiple color palettes:
# See multiple color palettes: seecol(pal = list(pal_1, pal_2, pal_3), pal_names = c("orange", "olive", "white"), col_bg = "grey90")
The last example illustrates that (a)\ the pattern
\ argument of grepal()
can use regular expressions, and that (b)\ the x
\ argument of grepal()
works with vectors or data frames (e.g., the unikn color palettes).
Let's compare different types of "blue", "orange", and "purple or violet" provided by the grDevices vector of named colors()
with various shades of "blau", "pink", "peach" and "bordeaux" provided by unikn color palettes:
# Search colors() with specific patterns (in their names): blues <- grepal("blue$", plot = FALSE) # ending on "blue" oranges <- grepal("orange", plot = FALSE) # containing "orange" purpviol <- grepal("purple|violet", plot = FALSE) # containing "purple" or "violet" # Search unikn palettes for color names: blaus_1 <- grepal("blau", pal_unikn, plot = FALSE) blaus_2 <- grepal("blau", pal_karpfenblau, plot = FALSE) pinks <- grepal("pink", pal_pinky, plot = FALSE) peach <- grepal("peach", pal_peach, plot = FALSE) baux <- grepal("bordeaux", pal_bordeaux, plot = FALSE) # See multiple color palettes: seecol(list(blues, oranges, purpviol, c(blaus_1, blaus_2, pinks, peach, baux)), pal_names = c("blues", "oranges", "purpviol", "unikn colors"), main ="Comparing custom color palettes")
Task: Get different shades of a specific color.
The shades_of()
function returns a vector of \code{n}\ colors that are shades
(i.e., typically lighter or darker versions) of an\ initial color\ \code{col_1}.
By default, shades_of(n)
yields n
\ shades ranging from col_1 = "black"
to col_n = "white
(i.e., n
\ shades of grey):
greys <- shades_of(10) seecol(greys, main = "10 shades of grey")
By adjusting\ n
, the initial color\ col_1
, and/or the final color\ col_n
,
we can conveniently create simple color ranges:
seecol(shades_of(4, Seeblau, "black"), main = "4 shades of Seeblau") seecol(shades_of(5, "forestgreen"), main = "5 shades of 'forestgreen'")
Using alpha
in the range $[0; 1]$ allows adjusting the transparency of the color gradient:
wine_gold <- shades_of(4, Bordeaux, col_n = "gold", alpha = .75) seecol(wine_gold, main = "5 transparent shades from Bordeaux to gold")
Task: Creating color gradients.
The shades_of()
function is merely a convenient wrapper function for the usecol()
function.
Using the pal
argument of usecol()
allows defining more complex color gradients
(e.g., by specifying more than two colors).
For instance, to create a bi-polar (divergent) color palette, we could mix two main colors with "white" and then remove the middle color:
# Create bi-polar color gradient (and remove 'white' middle color): divergent_gradient <- usecol(pal = c("firebrick", "white", "steelblue"), n = 7)[-4] seecol(divergent_gradient, main = "A bi-polar color palette")
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/ |
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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