geom_pcp: Generalized Parallel Coordinate plots

In ggpcp: Parallel Coordinate Plots in the 'ggplot2' Framework

Generalized Parallel Coordinate plots

Description

The ggpcp package for generalized parallel coordinate plots is implemented as a ggplot2 extension. In particular, this implementation makes use of ggplot2's layer framework, allowing for a lot of flexibility in the choice and order of showing graphical elements.

command	graphical element
geom_pcp	line segments
geom_pcp_axes	vertical lines to represent all axes
geom_pcp_box	boxes for levels on categorical axes
geom_pcp_labels	labels for levels on categorical axes

These ggpcp specific layers can be mixed with ggplot2's regular geoms, such as e.g. ggplot2::geom_point(), ggplot2::geom_boxplot(), ggdensity::geom_hdr(), etc.

Usage

geom_pcp(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  na.rm = FALSE,
  axiswidth = c(0, 0.1),
  overplot = "small-on-top",
  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)).
stat	The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")
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.
na.rm	If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
axiswidth	vector of two values indicating the space numeric and categorical axes are supposed to take. Minimum of 0, maximum of 1.Defaults to 0 for a numeric axis and 0.1 for a categorical axis.
overplot	character value indicating which method should be used to mitigate overplotting of lines. Defaults to 'small-on-top'. The overplotting strategy 'small-on-top' identifies the number observations for each combination of levels between two categorical variables and plots the lines from highest frequency to smallest (effectively plotting small groups on top). The strategy 'none' gives most flexibility to the user - the plotting order is preserved by the order in which observations are included in the original data.
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 color = 'red' or size = 3. They may also be parameters to the paired geom/stat.

Value

a list consisting of a ggplot2::layer() object and its associated scales.

About Parallel Coordinate Plots

Parallel coordinate plots are a multivariate visualization that allows several aspects of an observed entity to be shown in a single plot. Each aspect is represented by a vertical axis (giving the plot its name), values are marked on each of these axes. Values corresponding to the same entity are connected by line segments between adjacent axes. This type of visualization was first used by d’Ocagne (1985). Modern re-inventions go back to Inselberg (1985) and Wegman (1990). This implementation takes a more general approach in that it is also able to deal with categorical in the same principled way that allows a tracking of individual observations across multiple dimensions.

Data wrangling

The data pipeline feeding geom_pcp is implemented in a three-step modularized form rather than in a stat_pcp function more typical for ggplot2 extensions. The three steps of data pre-processing are:

command	data processing step
pcp_select	variable selection (and horizontal ordering)
pcp_scale	(vertical) scaling of values
pcp_arrange	dealing with tie-breaks on categorical axes

Note that these data processing steps are executed before the call to ggplot2 and the identity function is used by default in all of the ggpcp specific layers. Besides the speed-up by only executing the processing steps once for all layers, the separation has the additional benefit, that it provides the users with the possibility to make specific choices at each step in the process. Additionally, separation allows for a cleaner user interface: parameters affecting the data preparation process can be moved to the relevant (set of) function(s) only, thereby reducing the number of arguments without any loss of functionality.

References

M. d’Ocagne. (1885) Coordonnées parallèles et axiales: Méthode de transformation géométrique et procédé nouveau de calcul graphique déduits de la considération des coordonnées parallèles. Gauthier-Villars, page 112, https://archive.org/details/coordonnesparal00ocaggoog/page/n10.

Al Inselberg. (1985) The plane with parallel coordinates. The Visual Computer, 1(2):69–91, doi: 10.1007/BF01898350.

Ed J. Wegman. (1990) Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85:664–675, doi: 10.2307/2290001.

Examples

library(ggplot2)
data(mtcars)
mtcars_pcp <- mtcars |>
  dplyr::mutate(
    cyl = factor(cyl),
    vs = factor(vs),
    am = factor(am),
    gear = factor(gear),
    carb = factor(carb)
  ) |>
  pcp_select(1:11) |>  # select everything
  pcp_scale() |>
  pcp_arrange()

 base <- mtcars_pcp |> ggplot(aes_pcp())


 # Just the base plot:
 base + geom_pcp()

 # with the pcp theme
 base + geom_pcp() + theme_pcp()

 # with boxplots:
 base +
  geom_pcp(aes(colour = cyl)) +
  geom_boxplot(aes(x = pcp_x, y = pcp_y),
   inherit.aes=FALSE,
   data = dplyr::filter(mtcars_pcp, pcp_class!="factor")) +
  theme_pcp()

# base plot with boxes and labels
 base +
  geom_pcp(aes(colour = cyl)) +
  geom_pcp_boxes() +
  geom_pcp_labels() +
  theme_pcp()

ggpcp documentation built on Nov. 28, 2022, 5:05 p.m.