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README.Rmd
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README.Rmd
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```{r, echo=FALSE}
knitr::opts_chunk$set(collapse=TRUE, fig.path="tools/figures/", fig.height=2, dev="svg")
```
[](https://cran.r-project.org/package=shades) [](https://travis-ci.org/jonclayden/shades) [](https://codecov.io/gh/jonclayden/shades)
# Simple colour manipulation in R 😎
The `shades` package allows colours to be manipulated easily in R. Properties such as brightness and saturation can be quickly queried, changed or varied, and perceptually uniform colour gradients can be constructed. It plays nicely with the pipe operator from the [popular `magrittr` package](https://github.com/tidyverse/magrittr), and fits naturally into that paradigm. It can also be used [with `ggplot2` scales](#interoperability-with-ggplot2).
The package is available on [CRAN](https://cran.r-project.org/package=shades). You can also install the current development version from GitHub using [`devtools`](https://github.com/r-lib/devtools):
```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("jonclayden/shades")
```
Feedback on the package or suggestions are welcome, either by filing an issue or by email.
## Usage
Colours are represented in R using [CSS-style hex strings](https://en.wikipedia.org/wiki/Web_colors), but there is also a dictionary of predefined named colours such as `"red"` and `"blue"`. Either of these may be passed to most graphics functions, but creating variations on a particular colour can be awkward.
The `shades` package defines a simple class, `shade`, which uses exactly this same convention and is entirely compatible with built-in colours, but it also stores information about the coordinates of the colours in a particular [colour space](https://en.wikipedia.org/wiki/Color_space).
```{r}
library(shades)
red <- shade("red")
print(unclass(red))
```
From here, the package switches between colour spaces as required, allowing various kinds of colour manipulation to be performed straightforwardly. For example, let's find the saturation level of a few built-in colours.
```{r}
saturation(c("papayawhip","lavenderblush","olivedrab"))
```
Now let's consider a colour gradient stepping through two different colour spaces, which we might want to use as a palette or colour scale.
```{r gradients}
swatch(gradient(c("red","blue"), 5))
swatch(gradient(c("red","blue"), 5, space="Lab"))
```
Here, we are using the `swatch` function to visualise a set of colours as a series of squares. Notice the more uniform appearance of the gradient when it traverses through the [Lab colour space](https://en.wikipedia.org/wiki/Lab_color_space).
Similarly, we can create a set of new colours by changing the brightness and saturation levels of some base colours, and make the code more readable by using the [`magrittr` pipe operator](https://github.com/tidyverse/magrittr).
```{r saturation}
library(shades); library(magrittr)
c("red","blue") %>% brightness(0.6) %>% saturation(seq(0,1,0.25)) %>% swatch
```
This operation takes the original two colours, reduces their brightness to 60%, assigns a whole series of saturation levels to the result, and then passes it to `swatch` for visualisation. Notice that the pipeline is combinative (like the base function `outer`), returning each combination of parameters in a multidimensional array. The final shades are arranged in two rows by `swatch`, for convenience.
Note that `NA` can be used as a pass-through value:
```{r missing}
"cornflowerblue" %>% saturation(c(NA,seq(0,1,0.25))) %>% swatch
```
Any of these gradients can be directly passed to a standard graphical function, to be used as a colour scale. However, when choosing a colour scale, it is helpful to bear in mind that some viewers may have a colour vision deficiency (colour blindness), making it harder for them to distinguish certain colours and therefore to see a continuous scale. The `dichromat` function can be used to simulate this.
```{r dichromat}
rev(grDevices::rainbow(9)) %>% dichromat %>% swatch
gradient("viridis",9) %>% dichromat %>% swatch
```
Here we are using the built-in "viridis" colour map, [developed for Python's `matplotlib`](http:https://bids.github.io/colormap/), which was specifically designed to appear continuous under as many conditions as possible. When shown with simulated red-blindness, the default for `dichromat`, it is clearly much more interpretable than a typical rainbow palette generated by R's built-in graphics functions.
The package also supports colour mixing, either additively (as with light) or subtractively (as with paint). For example, consider additive mixtures of the three primary RGB colours.
```{r addmix}
c("red", addmix("red","green"), "green", addmix("green","blue"), "blue") %>% swatch
```
Similarly, we can subtractively combine the three secondary colours.
```{r submix}
c("cyan", submix("cyan","magenta"), "magenta", submix("magenta","yellow"), "yellow") %>% swatch
```
A "light mixture" infix operator, `%.)%`, and a "paint mixture" infix operator, `%_/%`, are also available.
```{r}
("red" %.)% "green") == "yellow"
("cyan" %_/% "magenta") == "blue"
```
Finally, you can calculate perceptual distances to a reference colour, as in
```{r}
distance(c("red","green","blue"), "red")
```
## Interoperability with ggplot2
The `shades` package can be used with the popular [`ggplot2` graphics library](https://github.com/tidyverse/ggplot2) in different ways, with different levels of integration. Firstly, gradients from this package can be used as `ggplot2` colour scales through the manual scale functions; for example,
```{r, ggplot, fig.height=5}
library(shades); library(ggplot2)
mtcars$cyl<- factor(mtcars$cyl)
ggplot(mtcars, aes(cyl,mpg,fill=cyl)) + geom_boxplot() + scale_fill_manual(values=gradient("viridis",3))
```
This does not require the two packages to know anything about each other, and is flexible and powerful, but it doesn't easily allow existing `ggplot2` scales to be modified using the colour manipulation functions from `shades`. As of `shades` version 1.3.0, it is also possible to call the package's colour property functions directly on palette functions and scales, so that (for example), we can darken all colours in an existing scale slightly:
```{r, scales, fig.height=5}
ggplot(mtcars, aes(cyl,mpg,fill=cyl)) + geom_boxplot() + scale_fill_brewer(type="qual")
ggplot(mtcars, aes(cyl,mpg,fill=cyl)) + geom_boxplot() + lightness(scale_fill_brewer(type="qual"), delta(-20))
```
Notice here that we have chosen to use the `delta()` function, which is available in all colour property functions, to request a *relative* reduction of 20 to the original lightness of each colour in the scale. We could also have given a literal value to fix the lightness of all colours to a certain level.
## Related packages
The `shades` package aims to bring together a range of colour manipulation tools and make them easy to use. However, there are several other packages available that can do similar things, sometimes in slightly different ways. These include
- the `grDevices` package, which is shipped with R and used as the basis for `shades`;
- the venerable [`colorspace` package](https://cran.r-project.org/package=colorspace), which provides formal colour classes and transformations between spaces;
- [`munsell`](https://cran.r-project.org/package=munsell), which interprets colours in Munsell notation and does some colour manipulation;
- [`viridis`](https://cran.r-project.org/package=viridis) and [`RColorBrewer`](https://cran.r-project.org/package=RColorBrewer), which provide the colour scales from `matplotlib` and ColorBrewer;
- [`dichromat`](https://cran.r-project.org/package=dichromat), which provides another implementation of the `dichromat` function (a duplication which I didn't discover until after writing this package's version!); and
- [`colorblindr`](https://github.com/clauswilke/colorblindr), which provides alternative tools for simulating colour blindness in figures.
This package was also partly influenced by [Colors.jl](https://github.com/JuliaGraphics/Colors.jl), a colour manipulation package for Julia.