treeheatr: an introduction

vignette documentation github-action-status

Your decision tree may be cool, but what if I tell you you can make it hot?

Changes in treeheatr 0.2.0

The first argument of heat_tree(), data is now replaced with x, which can be a dataframe (or tibble), a party (or constparty) object specifying the precomputed tree, or partynode object specifying the customized tree. custom_tree argument is no longer needed.


Please make sure your version of R >= 3.5.0 before installation.

You can install the released version of treeheatr from CRAN with:


Or the development version from GitHub with remotes:

# install.packages('remotes') # uncomment to install devtools


Penguin dataset

Classification of different types of penguin species.


heat_tree(penguins, target_lab = 'species')

Wine recognition dataset

Classification of different cultivars of wine.

heat_tree(wine, target_lab = 'Type', target_lab_disp = 'Cultivar')

Citing treeheatr

If you use treeheatr in a scientific publication, please consider citing the following paper:

Le TT, Moore JH. treeheatr: an R package for interpretable decision tree visualizations. Bioinformatics. 2020 Jan 1.

BibTeX entry:

  title={treeheatr: an R package for interpretable decision tree visualizations},
  author={Le, Trang T and Moore, Jason H},

How to Use

treeheatr incorporates a heatmap at the terminal node of your decision tree. The basic building blocks to a treeheatr plot are (yes, you guessed it!) a decision tree and a heatmap.

Make sure to check out the vignette for detailed information on the usage of treeheatr.

Please open an issue for questions related to treeheatr usage, bug reports or general inquiries.

Thank you very much for your support!