ggforce: geom_parallel_sets – R documentation

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geom_parallel_sets

Create Parallel Sets diagrams

Description

A parallel sets diagram is a type of visualisation showing the interaction between multiple categorical variables. If the variables has an intrinsic order the representation can be thought of as a Sankey Diagram. If each variable is a point in time it will resemble an alluvial diagram.

Usage

stat_parallel_sets(
  mapping = NULL,
  data = NULL,
  geom = "shape",
  position = "identity",
  n = 100,
  strength = 0.5,
  sep = 0.05,
  axis.width = 0,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_parallel_sets(
  mapping = NULL,
  data = NULL,
  stat = "parallel_sets",
  position = "identity",
  n = 100,
  na.rm = FALSE,
  sep = 0.05,
  strength = 0.5,
  axis.width = 0,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_parallel_sets_axes(
  mapping = NULL,
  data = NULL,
  geom = "parallel_sets_axes",
  position = "identity",
  sep = 0.05,
  axis.width = 0,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_parallel_sets_axes(
  mapping = NULL,
  data = NULL,
  stat = "parallel_sets_axes",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_parallel_sets_labels(
  mapping = NULL,
  data = NULL,
  stat = "parallel_sets_axes",
  angle = -90,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()` or `aes_()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`geom`	The geometric object to use display the data
`position`	Position adjustment, either as a string, or the result of a call to a position adjustment function.
`n`	The number of points to create for each of the bounding diagonals
`strength`	The proportion to move the control point along the x-axis towards the other end of the bezier curve
`sep`	The proportional separation between categories within a variable
`axis.width`	The width of the area around each variable axis
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`show.legend`	logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes`	If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.
`...`	Other arguments passed on to `layer()`. These are often aesthetics, used to set an aesthetic to a fixed value, like `colour = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.
`stat`	The statistical transformation to use on the data for this layer, as a string.
`angle`	The angle of the axis label text

Details

In a parallel sets visualization each categorical variable will be assigned a position on the x-axis. The size of the intersection of categories from neighboring variables are then shown as thick diagonals, scaled by the sum of elements shared between the two categories. The natural data representation for such as plot is to have each categorical variable in a separate column and then have a column giving the amount/magnitude of the combination of levels in the row. This representation is unfortunately not fitting for the ggplot2 API which needs every position encoding in the same column. To make it easier to work with ggforce provides a helper gather_set_data(), which takes care of the transformation.

Aesthetics

geom_parallel_sets understand the following aesthetics (required aesthetics are in bold):

x
id
split
value
color
fill
size
linetype
alpha
lineend

Author(s)

Thomas Lin Pedersen

Examples

data <- reshape2::melt(Titanic)
data <- gather_set_data(data, 1:4)

ggplot(data, aes(x, id = id, split = y, value = value)) +
  geom_parallel_sets(aes(fill = Sex), alpha = 0.3, axis.width = 0.1) +
  geom_parallel_sets_axes(axis.width = 0.1) +
  geom_parallel_sets_labels(colour = 'white')

ggforce

Accelerating 'ggplot2'

v0.3.3

MIT + file LICENSE

Authors

Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), RStudio [cph]

Initial release