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.
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, orientation = NA, show.legend = NA, inherit.aes = TRUE, ... ) geom_parallel_sets( mapping = NULL, data = NULL, stat = "parallel_sets", position = "identity", n = 100, na.rm = FALSE, orientation = NA, 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, orientation = NA, show.legend = NA, inherit.aes = TRUE, ... ) geom_parallel_sets_axes( mapping = NULL, data = NULL, stat = "parallel_sets_axes", position = "identity", na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE, ... ) geom_parallel_sets_labels( mapping = NULL, data = NULL, stat = "parallel_sets_axes", angle = -90, nudge_x = 0, nudge_y = 0, position = "identity", na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE, ... )
Set of aesthetic mappings created by
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
The data to be displayed in this layer. There are three options:
NULL, the default, the data is inherited from the plot
data as specified in the call to
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.
function will be called with a single argument,
the plot data. The return value must be a
will be used as the layer data. A
function can be created
~ head(.x, 10)).
The geometric object to use to display the data, either as a
Geom subclass or as a string naming the geom stripped of the
geom_ prefix (e.g.
"point" rather than
Position adjustment, either as a string naming the adjustment
"jitter" to use
position_jitter), or the result of a call to a
position adjustment function. Use the latter if you need to change the
settings of the adjustment.
The number of points to create for each of the bounding diagonals
The proportion to move the control point along the x-axis towards the other end of the bezier curve
The proportional separation between categories within a variable
The width of the area around each variable axis
FALSE, the default, missing values are removed with
a warning. If
TRUE, missing values are silently removed.
The orientation of the layer. The default (
automatically determines the orientation from the aesthetic mapping. In the
rare event that this fails it can be given explicitly by setting
"y". See the Orientation section for more detail.
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
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.
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.
The statistical transformation to use on the data for this
layer, either as a
Geom subclass or as a string naming the
stat stripped of the
stat_ prefix (e.g.
"count" rather than
The angle of the axis label text
Horizontal and vertical adjustment to nudge labels by. Useful for offsetting text from the category segments.
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
takes care of the transformation.
geom_parallel_sets understand the following aesthetics (required aesthetics are in bold):
This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the
orientation parameter, which can be either
"y". The value gives the axis that the geom should run along,
"x" being the default orientation you would expect for the geom.
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') # Use nudge_x to offset and hjust = 0 to left-justify label 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 = 'red', angle = 0, nudge_x = 0.1, hjust = 0)