ggalign

This package extends ggplot2 by providing advanced tools for aligning and organizing multiple plots, particularly those that automatically reorder observations, such as dendrogram. It offers fine control over layout adjustment and plot annotations, enabling you to create complex, publication-quality visualizations while still using the familiar grammar of ggplot2.

Why use ggalign?

ggalign focuses on aligning observations across multiple plots. It leverages the "number of observations" in the vctrs package or NROW() function to maintain consistency in plot organization.

If you’ve ever struggled with aligning plots with self-contained ordering (like dendrogram), or applying consistent grouping or ordering across multiple plots (e.g., with k-means clustering), ggalign is designed to make this easier. The package integrates seamlessly with ggplot2, providing the flexibility to use its geoms, scales, and other components for complex visualizations.

Installation

You can install ggalign from CRAN using:

install.packages("ggalign")

Alternatively, install the development version from r-universe with:

install.packages("ggalign",
    repos = c("https://yunuuuu.r-universe.dev", "https://cloud.r-project.org")
)

or from GitHub with:

# install.packages("remotes")
remotes::install_github("Yunuuuu/ggalign")

Learning ggalign

1. The complete tutorial is available at: https://yunuuuu.github.io/ggalign-book/

2. For the full reference documentation, visit: https://yunuuuu.github.io/ggalign/

Compare with other ggplot2 heatmap extension

ggalign offers advantages over extensions like ggheatmap by providing full compatibility with ggplot2. With ggalign, you can:

• Seamlessly integrate ggplot2 geoms, stats, scales et al. into your layouts.
• Align dendrograms even in facetted plots.
• Easily create complex layouts, including multiple heatmaps arranged vertically or horizontally.

Compare with ComplexHeatmap

Pros

• Full integration with the ggplot2 ecosystem.
• Heatmap annotation axes and legends are automatically generated.
• Dendrogram can be easily customized and colored.
• Flexible control over plot size and spacing.
• Can easily align with other ggplot2 plots by panel area.
• Can easily extend for other clustering algorithm, or annotation plot.

Cons

Fewer Built-In Annotations: May require additional coding for specific annotations or customization compared to the extensive built-in annotation function in ComplexHeatmap.