This geom makes it possible to add a layer showing edge presence as a density map. Each edge is converted to n points along the line and a jitter is applied. Based on this dataset a two-dimensional kernel density estimation is applied and plotted as a raster image. The density is mapped to the alpha level, making it possible to map a variable to the fill.

geom_edge_density(
  mapping = NULL,
  data = get_edges("short"),
  position = "identity",
  show.legend = NA,
  n = 100,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by ggplot2::aes() or ggplot2::aes_(). By default x, y, xend, yend, group and circular are mapped to x, y, xend, yend, edge.id and circular in the edge data.

data

The return of a call to get_edges() or a data.frame giving edges in correct format (see details for for guidance on the format). See get_edges() for more details on edge extraction.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

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.

n

The number of points to estimate in the x and y direction, i.e. the resolution of the raster.

...

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.

Aesthetics

geom_edge_density understand the following aesthetics. Bold aesthetics are automatically set, but can be overridden.

x y xend yend edge_fill filter

Computed variables

x, y

The coordinates for each pixel in the raster

density

The density associated with the pixel

Edge aesthetic name expansion

In order to avoid excessive typing edge aesthetic names are automatically expanded. Because of this it is not necessary to write edge_colour within the aes() call as colour will automatically be renamed appropriately.

See also

Examples

require(tidygraph) gr <- create_notable('bull') %>% activate(edges) %>% mutate(class = sample(letters[1:3], n(), replace = TRUE)) ggraph(gr, 'stress') + geom_edge_density(aes(fill = class)) + geom_edge_link() + geom_node_point()
#> Warning: Raster pixels are placed at uneven horizontal intervals and will be shifted. Consider using geom_tile() instead.
#> Warning: Raster pixels are placed at uneven vertical intervals and will be shifted. Consider using geom_tile() instead.