ArborView

An interactive web-based visualization tool for CART decision trees built with R's rpart package. ArborView exports rpart models to JSON and renders them in the browser using D3.js, making it easy to explore and communicate tree structure, node statistics, and variable importance.

Live demo: https://arborview-delta.vercel.app/

Features

• Interactive hierarchical tree layout powered by D3.js
• Node sizing proportional to sample count
• Hover tooltips with per-node statistics
• Decision path breadcrumb — rules from root to any selected node
• Detail panel showing impurity, complexity, deviance, and class probabilities
• Variable importance chart
• Support for both classification (Gini) and regression (MSE) trees
• Dataset selector for comparing multiple models

Requirements

• Node.js (v18+) and npm
• R with packages: rpart, jsonlite, partykit

Installing Node.js and npm

npm is bundled with Node.js. Install Node.js (v18+) for your platform:

Mac

brew install node

If Homebrew is not installed, get it at https://brew.sh first. Alternatively, download the macOS installer directly from https://nodejs.org.

Windows (Command Prompt)

winget install OpenJS.NodeJS

Then close and reopen Command Prompt so node and npm are on your PATH. Alternatively, download the Windows installer from https://nodejs.org and run it.

Windows (PowerShell)

winget install OpenJS.NodeJS

Then close and reopen PowerShell so node and npm are on your PATH. Alternatively, download the Windows installer from https://nodejs.org and run it.

Verify the install on any platform:

node --version
npm --version

Adding Rscript to PATH

Rscript is installed with R and is needed when exporting a model from the command line. Verify that it is already available:

Rscript --version

If that command is not found, add the directory containing Rscript.exe to your PATH. On Windows, it is usually in a versioned R install directory such as:

C:\Program Files\R\R-4.x.x\bin

If you are not sure which version is installed, locate it from PowerShell:

Get-ChildItem "C:\Program Files\R" -Recurse -Filter Rscript.exe

Then add the bin directory to your user PATH:

1. Open Start and search for Edit environment variables for your account.
2. Select Environment Variables, then under User variables select Path and choose Edit.
3. Choose New and add the R bin directory, for example C:\Program Files\R\R-4.x.x\bin.
4. Select OK to save, then close and reopen your terminal.

Verify the update:

Rscript --version

Installation

npm install

Usage

1. Export a model from R

source("R/export_tree.R")

fit <- rpart::rpart(Class ~ ., data = mydata)
arborview_export(fit, "public/data/my_model.json", title = "My Model")

Or from the command line:

Rscript R/export_tree.R model.rds public/data/my_model.json "My Model"

Then add an entry for your file in public/data/manifest.json.

2. Start the dev server

npm run dev

Open the URL printed by Vite (typically http://localhost:5173) in your browser.

Scripts

Command	Description
npm run dev	Start the Vite development server
npm run build	Compile TypeScript and bundle for production
npm run preview	Serve the production build locally

Project Structure

ArborView/
├── src/
│   ├── main.ts          # App entry, dataset loading, sidebar UI
│   ├── tree.ts          # D3 tree renderer
│   ├── tooltip.ts       # Tooltip rendering
│   ├── utils.ts         # Utility functions
│   ├── types.ts         # TypeScript type definitions
│   └── styles.css       # Styles
├── R/
│   └── export_tree.R    # rpart → JSON exporter
├── public/
│   └── data/            # JSON tree files and manifest
├── docs/
│   ├── typescript-for-r-users.md      # TypeScript primer for R contributors
│   └── typescript-for-python-users.md # TypeScript primer for Python contributors
├── example_data/        # Sample rpart models (.rds)
└── index.html

JSON Schema

arborview_export() writes a single JSON file per model. Here is what each part of that file contains.

Top-level fields

Field	Type	Description
schema	string	Always "arborview/tree@1". Used to detect version mismatches.
title	string	Human-readable label shown in the dataset dropdown.
method	string	rpart method string: "class" (classification) or "anova" (regression).
response.type	string	"classification" or "regression".
response.levels	string array or null	Ordered class labels for classification trees; null for regression. Index i in this array corresponds to index i in each node's class_counts and class_probs.
variables.predictors	string array	Names of all predictor variables from the model formula.
variables.importance	object	Variable importance scores from rpart (higher = more important). Keys are predictor names; values are raw importance scores.
cptable	array or absent	One row per tree size evaluated during cross-validation. Each row has CP, nsplit, rel_error, xerror, xstd. Useful for plotting the bias-variance tradeoff and choosing a pruning level.
call	string	The original R call that produced the model, e.g. "rpart(y ~ x1 + x2)".
tree	object	Root node of the tree. Children are nested recursively (see below).

Node fields

Every node — whether a leaf or an internal split — has these fields:

Field	Type	Description
node_id	integer	rpart's integer node ID. Root = 1; left child of node k = 2k; right child = 2k+1.
depth	integer	Distance from root (root = 0, root's children = 1, …).
n	integer	Number of training observations that reached this node.
weight	number	Sum of case weights at this node. Equals n when no case weights were used.
deviance	number	Total deviance at this node (impurity × n). For classification: weighted Gini × n. For regression: residual sum of squares.
complexity	number	Cost-complexity parameter (alpha) at which this node would be pruned. Smaller = more stable split.
is_leaf	boolean	true for terminal leaves, false for internal split nodes.
impurity	number	Per-observation impurity. Classification: Gini index (1 − Σpᵢ²). Regression: MSE (deviance / n).
rule_from_root	string array	Plain-English decision rules from the root down to (but not including) this node. Empty for the root itself. Example: ["age < 12.5", "income >= 50000"].

Classification-only fields (present when response.type == "classification"):

Field	Type	Description
predicted_class	string	Majority-class label at this node.
class_counts	integer array	Raw observation counts per class. Order matches response.levels.
class_probs	number array	Proportion of observations per class. Order matches response.levels. Sums to 1.
node_prob	number	Fraction of the entire training set that reached this node.

Regression-only fields (present when response.type == "regression"):

Field	Type	Description
predicted_value	number	Mean response value for observations at this node.

Internal-node-only fields (present when is_leaf == false):

Field	Type	Description
split_var	string	Name of the predictor variable used to split at this node.
split	object	The split condition (see below).
children	array	Two-element array: [left_child, right_child], each a full node object.

Split objects

A numeric split (e.g. age < 12.5):

{
  "type": "numeric",
  "threshold": 12.5,
  "left_op": "<",
  "right_op": ">="
}

A categorical split (e.g. region ∈ {West, South}):

{
  "type": "categorical",
  "left_levels": ["West", "South"],
  "right_levels": ["East", "Midwest"]
}

left_levels and right_levels are the factor levels routed to each child. Levels absent from both sets were NA in the training data.

Manifest

public/data/manifest.json is a simple index of available datasets:

{
  "datasets": [
    { "id": "my_model", "label": "My Model", "file": "my_model.json" }
  ]
}

file is the filename relative to public/data/.

License

MIT — Copyright 2026 Joseph Kasprzyk, Zach Carpenter, Edith Zagona