https://github.com/grantmcdermott/parttree

R package for plotting simple decision tree partitions
https://github.com/grantmcdermott/parttree

Last synced: about 2 months ago
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R package for plotting simple decision tree partitions

• Host: GitHub
• URL: https://github.com/grantmcdermott/parttree
• Owner: grantmcdermott
• License: other
• Created: 2020-03-25T05:52:20.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-07-27T01:34:53.000Z (about 2 months ago)
• Last Synced: 2024-07-27T20:55:25.302Z (about 2 months ago)
• Language: R
• Homepage: http://grantmcdermott.com/parttree
• Size: 5.84 MB
• Stars: 90
• Watchers: 5
• Forks: 23
• Open Issues: 7
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# parttree 

[![R-CMD-check](https://github.com/grantmcdermott/parttree/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/grantmcdermott/parttree/actions/workflows/R-CMD-check.yaml)

[![Docs](https://img.shields.io/badge/docs-homepage-blue.svg)](https://grantmcdermott.com/parttree/index.html)

Visualize simple 2-D decision tree partitions in R. The **parttree**

package is optimised to work with [**ggplot2**](https://ggplot2.tidyverse.org/),

although it can be used to visualize tree partitions with base R graphics too.

## Installation

This package is not yet on CRAN, but can be installed from [GitHub](https://github.com/) 

with:

``` r

# install.packages("remotes")

remotes::install_github("grantmcdermott/parttree")

```

## Quickstart

The **parttree** [homepage](https://grantmcdermott.com/parttree/index.html)

includes an introductory vignette and detailed documentation. But here's a 

quickstart example using the

["kyphosis"](https://search.r-project.org/CRAN/refmans/rpart/html/kyphosis.html)

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()`.

```{r quickstart}

library(rpart)     # For the dataset and fitting decisions trees

library(parttree)  # This package (will automatically load ggplot2 too)

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()

```