Visualize simple 2-D decision tree partitions in R. The parttree package is optimised to work with ggplot2, although it can be used to visualize tree partitions with base R graphics too.
This package is not yet on CRAN, but can be installed from GitHub with:
# install.packages("remotes")
remotes::install_github("grantmcdermott/parttree")The parttree
homepage includes an
introductory vignette and detailed documentation. But here’s a
quickstart example using the
“kyphosis”
dataset that comes bundled with the rpart package. In this case, we
are interested in predicting kyphosis recovery after spinal surgery, as
a function of 1) the number of topmost vertebra that were operated, and
2) patient age. The key visualization layer below—provided by this
package—is geom_partree().
library(rpart) # For the dataset and fitting decisions trees
library(parttree) # This package (will automatically load ggplot2 too)
#> Loading required package: ggplot2
fit = rpart(Kyphosis ~ Start + Age, data = kyphosis)
ggplot(kyphosis, aes(x = Start, y = Age)) +
geom_parttree(data = fit, alpha = 0.1, aes(fill = Kyphosis)) + # <-- key layer
geom_point(aes(col = Kyphosis)) +
labs(
x = "No. of topmost vertebra operated on", y = "Patient age (months)",
caption = "Note: Points denote observations. Shading denotes model predictions."
) +
theme_minimal()