visualization.Rmd

---
title: "Visualizing Euler diagrams with eulerr"
author: "Johan Larsson"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Visualizing Euler diagrams with eulerr}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: eulerr.bib
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

Once we have ascertained that our Euler diagram fits well, we can turn to
visualizing the solution. For this purpose, **eulerr** relies on the
**grid** graphics system [@RCT_2017] and offers intuitive and
granular control over the output.

Plotting the ellipses is straightforward using the parametrization of a rotated
ellipse,

$$
\begin{bmatrix}
x \\ y
\end{bmatrix} =
\begin{bmatrix}
h + a \cos{\theta} \\
k + b \sin{\theta}
\end{bmatrix},
$$

where $\theta \in [0, 2\pi],\quad a,b>0$.

Most users will also prefer to label the ellipses and their intersections
with text and this, however, is considerably more involved.

### Labeling

Labeling the ellipses is complicated since the shapes of the intersections
often are irregular, lacking well-defined centers; we know of no analytical
solution to this problem. Instead, **eulerr** relies on the **polylabelr**
package [@larsson2018d], which was created by the author. It provides a simple 
wrapper for the **polylabel** [@mapbox2018] C++ library from *Mapbox*.

### Aesthetics

Euler diagrams display both quantitative and qualitative data. The quantitative
aspect is the quantities or sizes of the sets depicted in the diagram and is
visualized by the relative sizes, and possibly the labels, of the areas of the
shapes---this is the main focus of this paper. The qualitative aspects,
meanwhile, consist of the mapping of each set to some quality or category, such
as having a certain gene or not. In the diagram, these qualities can be
separated through any of the following aesthetics:

* color,
* border type,
* text labeling,
* transparency,
* patterns,

or a combination of these. The main purpose of these aesthetics is to separate
out the different ellipses so that the audience may interpret the diagram with
ease and clarity.

Among these aesthetics, the best choice (from a viewer perspective) appears to
be color [@Blake_2016], which provides useful information without extraneous chart junk. 

The issue with color, however, is that it cannot be perceived perfectly by all.
Eight percent of men and 0.4% of women in European Caucasian countries, for 
instance, suffer the most common form, red--green color deficiency. 
Moreover, color is often printed at a premium in scientific publications 
and adds no utility
to a diagram of two shapes.

For these reasons, **eulerr** defaults to distinguishing ellipses with color
using a manually tuned color palette.

```{r colorexamle, fig.cap = "The eight first colors of the default color palette"}
set.seed(2)
library(eulerr)
con <- c(A = 1, B = 1, C = 1, D = 1, E = 1, F = 1, G = 1, H = 1,
         "A&B" = 0.2, "B&C" = 0.2, "C&D" = 0.2, "D&E" = 0.2, "E&F" = 0.2,
         "F&G" = 0.2, "G&H" = 0.2)
plot(euler(con), labels = as.character(1:8))
```

## Normalizing dispersed layouts

If there are disjoint clusters of ellipses, the optimizer will often
spread these out more than is necessary, wasting space in our diagram. To
tackle this, we use a SKYLINE-BL rectangle packing algorithm [@Jylaenki_2010]
designed specifically for **eulerr**. In it, we surround each ellipse cluster
with a bounding box, pack these boxes into a bin of appropriate size and
aspect ratio, and adjust the coordinates of the ellipses in the clusters to
compact our diagram. As a bonus, this increases the chance of having
similar layouts for different function calls.

## References