Serial axes coordinate is a methodology for visualizing the \(p\)-dimensional geometry and multivariate data. As the name suggested, all axes are shown in serial. The axes can be a finite \(p\) space or transformed to an infinite space (e.g. Fourier transformation).

In the finite \(p\) space, all axes can be displayed in parallel which is known as the parallel coordinate; also, all axes can be displayed under a polar coordinate that is often known as the radial coordinate or radar plot. In the infinite space, a mathematical transformation is often applied. More details will be explained in the sub-section `Infinite axes`

A point in Euclidean \(p\)-space \(R^p\) is represented as a polyline in serial axes coordinate, it is found that a point <–> line duality is induced in the Euclidean plane \(R^2\) (A. Inselberg and Dimsdale 1990).

Before we start, a couple of things should be noticed:

In the serial axes coordinate system, no

`x`

or`y`

(even`group`

) are required; but other aesthetics, such as`colour`

,`fill`

,`size`

, etc, are accommodated.Layer

`geom_path`

is used to draw the serial lines; layer`geom_histogram`

,`geom_quantiles`

, and`geom_density`

are used to draw the histograms, quantiles (*not*) and densities. Users can also customize their own layer (i.e.`quantile`

regression`geom_boxplot`

,`geom_violin`

, etc) by editing function`add_serialaxes_layers`

.

Suppose we are interested in the data set `iris`

. A parallel coordinate chart can be created as followings:

```
library(ggmulti)
# parallel axes plot
ggplot(iris,
mapping = aes(
Sepal.Length = Sepal.Length,
Sepal.Width = Sepal.Width,
Petal.Length = Petal.Length,
Petal.Width = Petal.Width,
colour = factor(Species))) +
geom_path(alpha = 0.2) +
coord_serialaxes() -> p
p
```

A histogram layer can be displayed by adding layer `geom_histogram`

```
+
p geom_histogram(alpha = 0.3,
mapping = aes(fill = factor(Species))) +
theme(axis.text.x = element_text(angle = 30, hjust = 0.7))
```

A density layer can be drawn by adding layer `geom_density`

```
+
p geom_density(alpha = 0.3,
mapping = aes(fill = factor(Species)))
```

A parallel coordinate can be converted to radial coordinate by setting `axes.layout = "radial"`

in function `coord_serialaxes`

.

```
$coordinates$axes.layout <- "radial"
p p
```

Note that: layers, such as `geom_histogram`

, `geom_density`

, etc, are not implemented in the radial coordinate yet.

Andrews (1972) plot is a way to project multi-response observations into a function \(f(t)\), by defining \(f(t)\) as an inner product of the observed values of responses and orthonormal functions in \(t\)

\[f_{y_i}(t) = <\mathbf{y}_i, \mathbf{a}_t>\]

where \(\mathbf{y}_i\) is the \(i\)th responses and \(\mathbf{a}_t\) is the orthonormal functions under certain interval. Andrew suggests to use the Fourier transformation

\[\mathbf{a}_t = \{\frac{1}{\sqrt{2}}, \sin(t), \cos(t), \sin(2t), \cos(2t), ...\}\]

which are orthonormal on interval \((-\pi, \pi)\). In other word, we can project a \(p\) dimensional space to an infinite \((-\pi, \pi)\) space. The following figure illustrates how to construct an “Andrew’s plot.”

```
<- ggplot(iris,
p mapping = aes(Sepal.Length = Sepal.Length,
Sepal.Width = Sepal.Width,
Petal.Length = Petal.Length,
Petal.Width = Petal.Width,
colour = Species)) +
geom_path(alpha = 0.2,
stat = "dotProduct") +
coord_serialaxes()
p
```

A quantile layer can be displayed on top

```
+
p geom_quantiles(stat = "dotProduct",
quantiles = c(0.25, 0.5, 0.75),
size = 2,
linetype = 2)
```

A couple of things should be noticed:

mapping aesthetics is used to define the \(p\) dimensional space, if not provided, all columns in the dataset ‘iris’ will be transformed. An alternative way to determine the \(p\) dimensional space to set parameter

`axes.sequence`

in each layer or in`coord_serialaxes`

.To construct a dot product serial axes plot, say Fourier transformation, “Andrew’s plot,” we need to set the parameter

`stat`

in`geom_path`

to “dotProduct.” The default transformation function is the Andrew’s (function`andrews`

). Users can customize their own, for example, Tukey suggests the following projected space\[\mathbf{a}_t = \{\cos(t), \cos(\sqrt{2}t), \cos(\sqrt{3}t), \cos(\sqrt{5}t), ...\}\]

where \(t \in [0, k\pi]\) (Gnanadesikan 2011).

`<- function(p = 4, k = 50 * (p - 1), ...) { tukey <- seq(0, p* base::pi, length.out = k) t <- seq(p) seq_k <- sapply(seq_k, values function(i) { if(i == 1) return(cos(t)) if(i == 2) return(cos(sqrt(2) * t)) <- seq_k[i - 1] + seq_k[i - 2] Fibonacci cos(sqrt(Fibonacci) * t) })list( vector = t, matrix = matrix(values, nrow = p, byrow = TRUE) ) }ggplot(iris, mapping = aes(Sepal.Length = Sepal.Length, Sepal.Width = Sepal.Width, Petal.Length = Petal.Length, Petal.Width = Petal.Width, colour = Species)) + geom_path(alpha = 0.2, stat = "dotProduct", transform = tukey) + coord_serialaxes()`

Note that: Tukey’s suggestion, element \(\mathbf{a}_t\) can “cover” more spheres in \(p\) dimensional space, but it is not orthonormal.

Rather than calling function `coord_serialaxes`

, an alternative way to create a serial axes object is to add a `geom_serialaxes_...`

object in our model.

For example, Figure 1 to 4 can be created by calling

```
<- ggplot(iris,
g mapping = aes(Sepal.Length = Sepal.Length,
Sepal.Width = Sepal.Width,
Petal.Length = Petal.Length,
Petal.Width = Petal.Width,
colour = Species))
+ geom_serialaxes(alpha = 0.2)
g +
g geom_serialaxes(alpha = 0.2) +
geom_serialaxes_hist(mapping = aes(fill = Species), alpha = 0.2)
+
g geom_serialaxes(alpha = 0.2) +
geom_serialaxes_density(mapping = aes(fill = Species), alpha = 0.2)
# radial axes can be created by
# calling `coord_radial()`
# this is slightly different, check it out!
+
g geom_serialaxes(alpha = 0.2) +
geom_serialaxes(alpha = 0.2) +
coord_radial()
```

Figure 5 and 7 can be created by setting “stat” and “transform” in `geom_serialaxes`

; to Figure 6, `geom_serialaxes_quantile`

can be added to create a serial axes quantile layer.

Some slight difference should be noticed here:

One benefit of calling

`coord_serialaxes`

rather than`geom_serialaxes_...`

is that`coord_serialaxes`

can accommodate duplicated axes in mapping aesthetics (e.g.*Eulerian path*,*Hamiltonian path*, etc). However, in`geom_serialaxes_...`

, duplicated axes will be omitted.Meaningful axes labels in

`coord_serialaxes`

can be created automatically, while in`geom_serialaxes_...`

, users have to set axes labels by`ggplot2::scale_x_continuous`

or`ggplot2::scale_y_continuous`

manually.As we turn the serial axes into interactive graphics (via package loon.ggplot), serial axes lines in

`coord_serialaxes()`

could be turned as interactive but in`geom_serialaxes_...`

all objects are static.

```
# The serial axes is `Sepal.Length`, `Sepal.Width`, `Sepal.Length`
# With meaningful labels
ggplot(iris,
mapping = aes(Sepal.Length = Sepal.Length,
Sepal.Width = Sepal.Width,
Sepal.Length = Sepal.Length)) +
geom_path() +
coord_serialaxes()
# The serial axes is `Sepal.Length`, `Sepal.Length`
# No meaningful labels
ggplot(iris,
mapping = aes(Sepal.Length = Sepal.Length,
Sepal.Width = Sepal.Width,
Sepal.Length = Sepal.Length)) +
geom_serialaxes()
```

Also, if the dimension of data is large, typing each variate in mapping aesthetics is such a headache. Parameter `axes.sequence`

is provided to determine the axes. For example, a `serialaxes`

object can be created as

```
ggplot(iris) +
geom_path() +
coord_serialaxes(axes.sequence = colnames(iris)[-5])
```

At very end, please report bugs here. Enjoy the high dimensional visualization! “Don’t panic… Just do it in ‘serial’” (Alfred Inselberg 1999).

Andrews, David F. 1972. “Plots of High-Dimensional Data.” *Biometrics*, 125–0136.

Gnanadesikan, Ram. 2011. “Methods for Statistical Data Analysis of Multivariate Observations.” In, 321:207–0218. John Wiley & Sons.

Inselberg, A., and B. Dimsdale. 1990. “Parallel Coordinates: A Tool for Visualizing Multi-Dimensional Geometry.” In *Proceedings of the First IEEE Conference on Visualization: Visualization ‘90*, 361–0378.

Inselberg, Alfred. 1999. “Don’t Panic... Just Do It in Parallel!” *Computational Statistics* 14 (1): 53–077.