Visualisation of Tree-based Phylogenetic Networks • ggret

ggret is an R package for the visualization and annotation of Ancestral Recombination Graphs (ARGs) and other tree-based phylogenetic networks. It extends the functionality of ggtree and ggplot2.

Installation

ggret requires ggplot2, ggtree and ape to function.

#install ggplot2
install.packages("ggplot2")

#install ggtree
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ggtree")

#install ggret
if (!requireNamespace("remotes", quietly = TRUE))
  install.packages("remotes")
remotes::install_github("grdspcht/ggret", dependencies = TRUE, build_vignettes = TRUE)

Documentation

In the Web

A website version of the documentation, containing function references and an in-depth vignette about customizing phylogenetic networks (see ‘Articles’) can be found on: https://grdspcht.github.io/ggret/

In R

Function documentation can be accessed by typing ?function_name. Additionally, you can find multiple examples on how to use ggret effectively in the vignette. To access it call:

vignette(topic = "intro_to_ggret", package = "ggret")

Contributing & Reporting bugs

Make sure to check out our Contribution guidelines (see CONTRIBUTING.md) for instructions on how to report bugs, suggest improvements or contributing new features to ggret.

Citing ggret

If you use ggret, please cite the associated publication as well as the original ggtree and ape publication:

Specht G, Schmid C, Kühnert D, Kocher A (2025). ggret: An R package for visualising and manipulating tree‑based phylogenetic networks. Journal of Open Source Software, 10(110), 7773, https://doi.org/10.21105/joss.07773
G Yu, DK Smith, H Zhu, Y Guan, TTY Lam^*. “ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data”. Methods in Ecology and Evolution. 2017, 8(1):28-36. doi: 10.1111/2041-210X.12628.
Paradis E, Schliep K . “ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R.” Bioinformatics, 2019, 35, 526-528. doi: 10.1093/bioinformatics/bty633.