---
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}
#| label: setup
#| include: false

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(eulerr)
```

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** uses the *pole of inaccessibility*
algorithm [@mapbox2018] (originally from *Mapbox*'s **polylabel** C++ library),
implemented in the **eunoia** Rust crate that powers eulerr's geometry layer.

When a label is too large to fit inside its region's polygon, **eulerr** falls
back to one of three exterior-placement strategies, all delegated to **eunoia**.
The strategy is selected via `labels$placement`:

`"raycast"` (the default)
:   labels are placed along the ray from the diagram centroid through the
    region's pole of inaccessibility, padded away from the diagram by a
    per-region margin. Closed-form, fast, deterministic.

`"force_directed"`
:   initial positions come from the raycast geometry, then a damped
    spring-and-repulsion solver relaxes the labels against each other *and*
    against foreign region polygons. Useful when several labels would otherwise
    collide or drift across unrelated regions.

`"elbow"`
:   labels are assigned to a left or right column outside the diagram, stacked
    vertically without overlap and reached by an orthogonal three-segment
    ("elbow") leader. Reminiscent of d3-pie style callouts.

In every case, a leader line is drawn from the source region to the exterior
label; tether and leader styling are controlled via `labels$tether`,
`labels$gap`, and `labels$leader`.

```{r}
#| label: placement-comparison

set.seed(1)
# A chain of four sets with long names and very small overlaps — the
# overlap regions are too narrow to fit their quantity labels inside,
# so all three strategies push the overlap labels to the exterior.
fit <- euler(c(
  Animalia = 8,
  Plantae = 7,
  Fungi = 6,
  Bacteria = 5,
  "Animalia&Plantae" = 0.05,
  "Plantae&Fungi" = 0.05,
  "Fungi&Bacteria" = 0.05,
  "Animalia&Bacteria" = 0.05
))
```

```{r}
#| label: placement-raycast
#| fig-width: 3
#| fig-height: 3
#| fig-cap: The raycast strategy places labels along rays from the diagram
#|   centroid, padded away from the diagram by a per-region margin.

plot(
  fit,
  quantities = TRUE,
  labels = list(placement = "raycast"),
  main = "raycast"
)
```

```{r}
#| label: placement-force-directed
#| fig-width: 3
#| fig-height: 3
#| fig-cap: The force-directed strategy relaxes labels against each other and
#|   against foreign region polygons, which is useful when several labels would
#|   otherwise collide or drift across unrelated regions.

plot(
  fit,
  quantities = TRUE,
  labels = list(placement = "force_directed"),
  main = "force-directed"
)
```

```{r}
#| label: placement-elbow
#| fig-width: 3
#| fig-height: 3
#| fig-cap: The elbow strategy stacks exterior labels in left/right columns
#|   reached by an orthogonal three-segment leader, in the style of d3-pie
#|   callouts.

plot(
  fit,
  quantities = TRUE,
  labels = list(placement = "elbow"),
  main = "elbow"
)
```

Strategy-specific knobs live in their own sublists. The force-directed solver
takes `labels$force_directed = list(iterations = ...)` and the elbow placement
takes `labels$elbow = list(margin = ..., min_gap = ...)`, where `min_gap` sets
the minimum vertical centre-to-centre spacing between stacked label boxes in a
column:

```{r}
#| label: elbow-min-gap
#| fig-cap: "Elbow leaders with extra vertical breathing room between labels"
#| fig-width: 3
#| fig-height: 3

plot(
  fit,
  quantities = TRUE,
  labels = list(
    placement = "elbow",
    elbow = list(min_gap = 1)
  )
)
```

### 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}
#| label: colorexamle
#| fig-cap: "The eight first colors of the default color palette"

set.seed(2)
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))
```

## Composing diagrams

Several Euler diagrams can be arranged into a single figure using operator
syntax inspired by the **patchwork** package. The `|` operator place plots side
by side, while `/` stacks them vertically.

```{r}
#| label: composition-basic
#| fig-cap: "Two diagrams arranged side by side"

p1 <- plot(euler(c(A = 1, B = 8, "A&B" = 1)), main = "First")
p2 <- plot(euler(c(A = 1, C = 1, "A&C" = 1)), main = "Second")

p1 | p2
```

Each operator returns an `eulergram`, so compositions nest freely:

```{r}
#| label: composition-nested
#| fig-cap: "Nested composition: two plots on top, one below"

p3 <- plot(euler(c(X = 3, Y = 2, "X&Y" = 1)), main = "Third")

(p1 | p2) / p3
```

Individual panels can be titled with the `main` argument to `plot()`, as above.
The gap between adjacent plots is controlled by the `composition$spacing` entry
of `eulerr_options()`:

```{r}
#| label: composition-spacing
#| eval: false

eulerr_options(composition = list(spacing = grid::unit(2, "lines")))
p1 | p2
```

## 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
