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Create a DenseLines Heatmap

Source: R/StatLineDensity.R, R/StatPathDensity.R, R/stat_line_density.R
stat_line_density.Rd

stat_line_density() is a 'ggplot2' statistic implementing the DenseLines algorithm described by Moritz and Fisher (2018). stat_path_density() is to stat_line_density() as geom_path() is to geom_line().

Usage

stat_line_density(
  mapping = NULL,
  data = NULL,
  geom = "raster",
  position = "identity",
  ...,
  bins = 30,
  binwidth = NULL,
  drop = TRUE,
  orientation = NA,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_path_density(
  mapping = NULL,
  data = NULL,
  geom = "raster",
  position = "identity",
  ...,
  bins = 30,
  binwidth = NULL,
  drop = TRUE,
  orientation = NA,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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.

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 to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

position

Position adjustment, either as a string naming the adjustment (e.g. "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.

...

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.

bins

numeric vector giving number of bins in both vertical and horizontal directions. Set to 30 by default.

binwidth

Numeric vector giving bin width in both vertical and horizontal directions. Overrides bins if both set.

drop

if TRUE removes all cells with 0 counts.

orientation

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.

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().

Details

stat_line_density() provides the density variable, which normalises count by its sum in each column of bins with the same value of the variable on the orientation axis. This is also provided by stat_path_density(), but should be used with caution as the DenseLines algorithm assumes lines are connected in order of the variable on the orientation axis. stat_path_density() therefore defaults to aes(fill = after_stat(count)) rather than after_stat(density).

Aesthetics

stat_line_density() understands the following aesthetics (required aesthetics are in bold):

  • x

  • y

  • group

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

  • after_stat(count)
    number of lines in bin.

  • after_stat(density)
    density of lines in bin. The result of the DenseLines algorithm.

  • after_stat(ncount)
    count, scaled to maximum of 1.

  • after_stat(ndensity)
    density, scaled to a maximum of 1.

Orientation

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.

References

Moritz, D. & Fisher, D. (2018). Visualizing a Million Time Series with the Density Line Chart. arXiv preprint arXiv:1409.0473. doi:10.48550/arxiv.1808.06019 .

See also

ggplot2::stat_bin_2d(), ggplot2::geom_line(), ggplot2::geom_raster().

Examples

library(ggplot2)

p <- ggplot(txhousing, aes(date, median, group = city))

p +
  stat_line_density(drop = FALSE, na.rm = TRUE)


p +
  aes(fill = after_stat(count)) +
  stat_line_density(
    aes(colour = after_stat(count)),
    geom = "point", size = 10, bins = 15, na.rm = TRUE
  ) +
  stat_line_density(
    aes(label = after_stat(ifelse(count > 25, count, NA))),
    geom = "label", size = 6, bins = 15, na.rm = TRUE
  )


ggplot(txhousing, aes(median, date, group = city)) +
  stat_line_density(
    aes(fill = after_stat(ndensity)),
    bins = 50, orientation = "y", na.rm = TRUE
  )


m <- ggplot(economics, aes(unemploy/pop, psavert, group = date < as.Date("2000-01-01")))
m + geom_path(aes(colour = after_stat(group)))

m + stat_path_density()