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 separates data extraction from plotting so the intermediate tidy objects can be inspected, saved, or used for custom analyses. 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")

Function reference

Each gg_* function has a matching plot() S3 method that hands back a single plottable object — a ggplot, or a patchwork composite when the method lays out multiple panels — so you can keep adding layers, scales, or a theme. 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. Highlights since v2.4.0:

• v2.6.1 Fix factor-level assignment in gg_partial for categorical variables.
• v2.6.0 New plotting functions exported; test coverage raised to 83%; removed internal dependency on hvtiRutilities.
• v2.5.0 New gg_partial_rfsrc() computes partial dependence directly from an rfsrc model without a separate plot.variable call; supports a grouping variable via xvar2.name.

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.