parttree (version 0.1.1)

geom_parttree: Visualize tree partitions with ggplot2

Description

geom_parttree() is a simple wrapper around parttree() that takes a tree model object and then converts into an amenable data frame that ggplot2 knows how to plot. Please note that ggplot2 is not a hard dependency of parttree and must thus be installed separately on the user's system before calling geom_parttree.

Usage

geom_parttree(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  linejoin = "mitre",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  flip = FALSE,
  ...
)

Value

A ggplot layer.

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

An rpart::rpart.object or an object of compatible type (e.g. a decision tree constructed via the partykit, tidymodels, or mlr3 front-ends).

stat

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.

position

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.

linejoin

Line join style (round, mitre, bevel).

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().

flip

Logical. By default, the "x" and "y" axes variables for plotting are determined by the first split in the tree. This can cause plot orientation mismatches depending on how users specify the other layers of their plot. Setting to TRUE will flip the "x" and "y" variables for the geom_parttree layer.

...

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.

Aesthetics

geom_parttree() aims to "work-out-of-the-box" with minimal input from the user's side, apart from specifying the data object. This includes taking care of the data transformation in a way that, generally, produces optimal corner coordinates for each partition (i.e. xmin, xmax, ymin, and ymax). However, it also understands the following aesthetics that users may choose to specify manually:

• fill (particularly encouraged, since this will provide a visual cue regarding the prediction in each partition region)

• colour

• alpha

• linetype

• size

Details

Because of the way that ggplot2 validates inputs and assembles plot layers, note that the data input for geom_parttree() (i.e. decision tree object) must assigned in the layer itself; not in the initialising ggplot2::ggplot() call. See Examples.

See Also

plot.parttree(), which provides an alternative plotting method using base R graphics.

Examples

# install.packages("ggplot2")
library(ggplot2)  # ggplot2 must be installed/loaded separately

library(parttree) # this package
library(rpart)    # decision trees
data.table::setDTthreads(2)
#
## Simple decision tree (max of two predictor variables)

iris_tree = rpart(Species ~ Petal.Length + Petal.Width, data=iris)

# Plot with original iris data only
p = ggplot(data = iris, aes(x = Petal.Length, y = Petal.Width)) +
  geom_point(aes(col = Species))

# Add tree partitions to the plot (borders only)
p + geom_parttree(data = iris_tree)

# Better to use fill and highlight predictions
p + geom_parttree(data = iris_tree, aes(fill = Species), alpha=0.1)

# To drop the black border lines (i.e. fill only)
p + geom_parttree(data = iris_tree, aes(fill = Species), col = NA, alpha = 0.1)

#
## Example with plot orientation mismatch

p2 = ggplot(iris, aes(x=Petal.Width, y=Petal.Length)) +
  geom_point(aes(col=Species))

# Oops
p2 + geom_parttree(data = iris_tree, aes(fill=Species), alpha = 0.1)

# Fix with 'flip = TRUE'
p2 + geom_parttree(data = iris_tree, aes(fill=Species), alpha = 0.1, flip = TRUE)

#
## Various front-end frameworks are also supported, e.g.:

# install.packages("parsnip")
library(parsnip)

iris_tree_parsnip = decision_tree() |>
  set_engine("rpart") |>
  set_mode("classification") |>
  fit(Species ~ Petal.Length + Petal.Width, data=iris)

p + geom_parttree(data = iris_tree_parsnip, aes(fill=Species), alpha = 0.1)

#
## Trees with continuous independent variables are also supported.

# Note: you may need to adjust (or switch off) the fill legend to match the
# original data, e.g.:

iris_tree_cont = rpart(Petal.Length ~ Sepal.Length + Petal.Width, data=iris)
p3 = ggplot(data = iris, aes(x = Petal.Width, y = Sepal.Length)) +
 geom_parttree(
   data = iris_tree_cont,
   aes(fill = Petal.Length), alpha=0.5
   ) +
  geom_point(aes(col = Petal.Length)) +
  theme_minimal()

# Legend scales don't quite match here:
p3

# Better to scale fill to the original data
p3 + scale_fill_continuous(limits = range(iris$Petal.Length))