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README.Rmd
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README.Rmd
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---
output: rmarkdown::github_document
---
[](https://trangdata.github.io/treeheatr/articles/explore.html)
[](https://trangdata.github.io/treeheatr/reference/)
`r badger::badge_cran_download("treeheatr", "grand-total", "blue")`
`r badger::badge_doi("10.1093/bioinformatics/btaa662", "yellow")`
```{r, include = FALSE}
knitr::opts_chunk$set(
fig.path = "man/figures/"
)
```
# treeheatr <img src="man/figures/logo.png" style="float:right; height:175px;">
*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.
## Install
Please make sure your version of R >= 3.5.0 before installation.
You can install the released version of **treeheatr** from CRAN with:
```{r, eval=FALSE}
install.packages('treeheatr')
```
Or the development version from GitHub with remotes:
```{r, eval=FALSE}
# install.packages('remotes') # uncomment to install devtools
remotes::install_github('trangdata/treeheatr')
```
## Examples
### Penguin dataset
These data were collected and made available by [Dr. Kristen Gorman](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php) and the [Palmer Station, Antarctica LTER](https://pal.lternet.edu/).
Classification of different types of penguin species.
```{r, fig.height=3.5, message=FALSE, warning=FALSE, dpi = 200}
library(treeheatr)
heat_tree(penguins, target_lab = 'species')
```
### Wine recognition dataset
Classification of different cultivars of wine.
```{r fig.height=3.5, dpi = 200}
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](https://doi.org/10.1093/bioinformatics/btaa662).
_Bioinformatics_. 2020 Jan 1.
BibTeX entry:
```bibtex
@article{le2020treeheatr,
title={treeheatr: an R package for interpretable decision tree visualizations},
author={Le, Trang T and Moore, Jason H},
journal={Bioinformatics},
year={2020},
doi="10.1093/bioinformatics/btaa662"
}
```
## 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.
* The decision tree is computed with `partykit::ctree()` and plotted with the well-documented and flexible [**ggparty**](https://cran.r-project.org/package=ggparty/) package.
The tree parameters can be passed to **ggparty** functions via the `heat_tree()` and `draw_tree()` functions of **treeheatr**.
More details on different **ggparty** *geoms* can be found [here](https://github.com/martin-borkovec/ggparty).
* The heatmap is shown with `ggplot2::geom_tile()`.
The user may choose to cluster the samples within each leaf node or the features across all samples.
Make sure to check out the [vignette](https://trangdata.github.io/treeheatr/articles/explore.html) for detailed information on the usage of **treeheatr**.
Please [open an issue](https://github.com/trangdata/treeheatr/issues/new) for questions related to **treeheatr** usage, bug reports or general inquiries.
Thank you very much for your support!
## Acknowledgements
Package hex was made with [Midjourney](https://www.midjourney.com/home/) and thus inherits a [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/legalcode).