Better data communication with {ggplot2}, part 2
Styling a chart using {ggfx} , {ggforce} and annotate()
In my previous article about how to improve data communication with {ggplot2} I have focused on the {forcats} package to reorder categorical data, on using colours to highlights parts of the plot and to use text to make our message to stand out. In this article, I would like to build upon those concepts to further improve our data communication skills using{ggfx} and {ggforce} to again highlight parts of the data we want our audience to focus on.
For this exercise, we are going to use the Palmer Archipelago (Antarctica) penguin data that is available through the {palmerpenguis} R package. You will find many examples of very good visualisations of this dataset, done in{ggplot2} such as the Tidytuesday contributions by Cara Thompson and Cedric Sherer.
{ggfx} to highlight parts of the data
{ggfx} is a newly released package by Thomas Lin Pedersen that provides a set of filters that are applicable to{ggplot2} geoms. The one that we are going to use now is with_blur(), which blurs the layers to which it is applied. In our case, we will blur all the points that we do not want to highlight.
This same concept is for instance applied in photography where we would blur the background of images so to focus the attention on the details we have chosen. When visualising data we can do the same, blur all the details that we do not want our audience to focus on.
The plot below would be our starting point for all the techniques that we will see on how to highlights certain parts of the chart. We are using {ggtext} to color parts of the title using HTML syntax and by calling ggtext::element_markdown() in the theme() to make sure the HTML code is correctly parsed.
Our base plot examines body mass versus bill length, and we can see from the data that Adelie penguins have the shortest bill.
Now, how can we highlight the Adelie penguins?
The standard way of doing it would be to color the other categories in a rather dull greyish color, and have a more vibrant color for Adelie penguins. Recent developments in the R package landscape have however broadly expanded our tool kit and allow us to experiment a little bit more.
We have indeed chosen to apply thefunction with_blur() from {ggfx} library to geom_point(). With this, we can first blur all the points, and then we can overlap on top another geom_point() layer applied to only the data corresponding to the Adelie penguins.
You can see from the code below that the first geom_point() is wrapped in with_blur(), this is what does the trick.
The advantage of it, compared to just coloring the points of Chinstrap and Gentoo in a dull greys color, is that we do not remove all the info from the plot, we really just make the info we are interested in stand out. For example, when doing a presentation, transitioning between the first plot and the {ggfx} version of it would be a very good choice if we want to focus our audience's attention to Adelie penguins.
Using {ggforce} to annotate
Another way in which we can make the Adelie penguins stand out, and Cedric Scherrer has provided many examples on it, it is by drawing an ellipse on top on the data of interest. We can even keep the blurry effect to be as explicit as possible.
To draw an ellipse we can use geom_mark_ellipse() from the {ggforce} package.
geom_mark_ellipse() and all geom_mark_* functions are highly customisable, so you can really adjust the ellipse, the arrow and the annotation the way you want it. For instance, in this case I have defined the length of the arrow, the direction of the arrow, the distance at which we want the line to stop ( con.cap) and we have removed the legend.
You can see also from the code that a filter argument can be specified directly in this function, making it really easy to choose which subset of data you want to target.
All these techniques, i.e. blurring, drawing ellipses, and annotation that stands out, are very valid and you may choose one or the other, or combine both, like in this example, for a very striking effect.
Using annotate()
Whenever you main focus is not on a set of data points but rather on detailed features of the data, annotate(), from {ggplot2} is what you need. Whereas in{ggforce} our annotation was limited by the labels in our data, if we want to have more elaborate descriptions we can use annotate() to place explanatory text on our plot.
For instance, in this case we have decided to add text and an arrow by calling annotate() twice, once for placing the text in the wanted position and the second time by adding an arrow. As you can see for the code below annotate() gives you a lot of freedom of customisation, but this come at a price. The placement of annotation layers is a rather "manual" affair, making it a bit less easy to reproduce on different plots, should you need to reuse the same code.
As you can see below, we were able to specify the start and end coordinates of the curve and specified the length of the arrow.
We have also used a recent R development, the {ragg} library to print emojis on our chart. Make sure you have it installed and you use dev="ragg_png" in your R chuck options if you want it to print correctly.
Overall, I feel the new with_blur() may have some very interesting applications in data communications and I would definitely encourage you to try it and experiment it a bit by yourself. Being able to borrow concepts from other fields, such a photography, and apply them to data visualisation makes me very exited about times to come. Let's see what the new developments will bring!
Originally published at https://github.com.
