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
mapping.
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)).
The geometric object to use to display the data for this layer.
When using a stat_*() function to construct a layer, the geom argument
can be used to override the default coupling between stats and geoms. The
geom argument accepts the following:
A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the
function name of the geom_ prefix. For example, to use geom_point(),
give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The position argument accepts the following:
The result of calling a position function, such as position_jitter().
This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a
string, strip the function name of the position_ prefix. For example,
to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.
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
If FALSE, the default, missing values are removed with
a warning. If TRUE, missing values are silently removed.
The orientation of the layer. The default (NA)
automatically determines the orientation from the aesthetic mapping. In the
rare event that this fails it can be given explicitly by setting orientation
to either "x" or "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
display.
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()'s params argument. These
arguments broadly fall into one of 4 categories below. Notably, further
arguments to the position argument, or aesthetics that are required
can not be passed through .... Unknown arguments that are not part
of the 4 categories below are ignored.
Static aesthetics that are not mapped to a scale, but are at a fixed
value and apply to the layer as a whole. For example, colour = "red"
or linewidth = 3. The geom's documentation has an Aesthetics
section that lists the available options. The 'required' aesthetics
cannot be passed on to the params. Please note that while passing
unmapped aesthetics as vectors is technically possible, the order and
required length is not guaranteed to be parallel to the input data.
When constructing a layer using
a stat_*() function, the ... argument can be used to pass on
parameters to the geom part of the layer. An example of this is
stat_density(geom = "area", outline.type = "both"). The geom's
documentation lists which parameters it can accept.
Inversely, when constructing a layer using a
geom_*() function, the ... argument can be used to pass on parameters
to the stat part of the layer. An example of this is
geom_area(stat = "density", adjust = 0.5). The stat's documentation
lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through
.... This can be one of the functions described as
key glyphs, to change the display of the layer in the legend.
The statistical transformation to use on the data for this layer.
When using a geom_*() function to construct a layer, the stat
argument can be used the override the default coupling between geoms and
stats. The stat argument accepts the following:
A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the
function name of the stat_ prefix. For example, to use stat_count(),
give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.
The angle of the axis label text
Horizontal and vertical adjustment to nudge labels by. Useful for offsetting text from the category segments.
geom_parallel_sets understand the following aesthetics (required aesthetics are in bold):
x|y
id
split
value
color
fill
size
linetype
alpha
lineend
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 "x" or "y". The value gives the axis that the geom should run along, "x" being the default orientation you would expect for the geom.
Thomas Lin Pedersen
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.
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)
Run the code above in your browser using DataLab