ggRandomForests: Visually Exploring Random Forests

ggRandomForests provides ggplot2-based diagnostic and exploration plots for random forests fit with randomForestSRC (>= 3.4.0) or randomForest. It keeps the data step apart from the figure step, so you can inspect, save, or reuse the tidy object on its own. Listed in the ggplot2 extensions gallery.

Installation

# CRAN (stable)
install.packages("ggRandomForests")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("ehrlinger/ggRandomForests")

Quick start

library(randomForestSRC)
library(ggRandomForests)

# 1. Fit a forest (regression)
rf <- rfsrc(medv ~ ., data = MASS::Boston, importance = TRUE)

# 2. Check convergence: did the forest grow enough trees?
plot(gg_error(rf))

# 3. Rank predictors by importance
plot(gg_vimp(rf))

# 4. Marginal dependence for top variables
gg_v <- gg_variable(rf)
plot(gg_v, xvar = "lstat")
plot(gg_v, xvar = rf$xvar.names, panel = TRUE, se = FALSE)

# 5. Partial dependence for a single predictor
pv <- plot.variable(rf, xvar.names = "lstat", partial = TRUE, show.plots = FALSE)
pd <- gg_partial(pv)
plot(pd)

For survival forests, see the package vignette:

vignette("ggRandomForests")

For variable importance with varPro — partial dependence, importance z-scores, beta importance, individual/local importance, and isolation forests — see the dedicated vignette:

vignette("varpro", package = "ggRandomForests")

The unsupervised varPro tools — gg_udependent(), gg_beta_uvarpro(), and gg_sdependent(), which read structure off a uvarpro() fit with no outcome — have their own short vignette:

vignette("uvarpro", package = "ggRandomForests")

Function reference

Each gg_* function has a matching plot() S3 method that hands back a single plottable object: a ggplot you extend with +, or a patchwork composite for the multi-panel methods. Every gg_* object also has print() and summary() methods: print() shows a short header at the REPL rather than dumping every row (use head() when you want the rows), and summary() gives you a diagnostics object you can print or keep.

Why ggRandomForests?

The package is built on one decision: keep the data step and the figure step apart. The gg_* functions pull a tidy data object out of the forest; the plot() methods turn that object into a ggplot2 figure. Two things follow from that split.

First, the data object stands on its own. It carries everything its plot needs, so you can save it, inspect it, or come back to it later without keeping the original forest — which can be large — in memory.

Second, you are never locked into the default figure. Because a plot() method returns a single plottable object (a ggplot, or a patchwork composite for the multi-panel methods), you can add layers, swap scales, or apply a theme; and if the default is not what you want, you can ignore it entirely and build the figure from the tidy data yourself.

Recent changes

See NEWS.md for the full changelog. Recent highlights:

• v3.4.0 Unsupervised varPro wrappers (gg_beta_uvarpro(), gg_sdependent()) with their own vignette; gg_partial_rfsrc() now handles factor predictors correctly.
• v3.3.0 varPro partial plots default to interpretable scales — probability for classification, survival S(τ) for survival.
• v3.1.0 varPro integration: release-rule importance, partial dependence, local importance, anomaly scores, and the dependency graph.

References

Breiman, L. (2001). Random forests, Machine Learning, 45:5–32.

Ishwaran H. and Kogalur U.B. randomForestSRC: Random Forests for Survival, Regression and Classification. R package version >= 3.4.0. https://cran.r-project.org/package=randomForestSRC

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R. R News 7(2), 25–31.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841–860.

Liaw A. and Wiener M. (2002). Classification and Regression by randomForest. R News 2(3), 18–22.

Wickham H. (2009). ggplot2: Elegant Graphics for Data Analysis. Springer New York.