# scatterbar: an R package for visualizing proportional data across spatially resolved coordinates

## TL;DR

The scatterbar R package improves the visualization of proportional data across spatial coordinates, especially for spatial transcriptomics, by using scatter stacked bar plots instead of scatter pie plots.

## Contribution

Introduces scatterbar, an R package that enhances the visualization of proportional data using scatter stacked bar plots for better interpretability.

## Key findings

- Scatter stacked bar plots improve the distinguishability of proportional distributions compared to scatter pie plots.
- The package is demonstrated using deconvolved cell-type proportions from a mouse brain spatial transcriptomics dataset.
- The package is available on CRAN with documentation and tutorials for users.

## Abstract

Displaying proportional data across many spatially resolved coordinates is a challenging but important data visualization task, particularly for spatially resolved transcriptomics data. Scatter pie plots are one type of commonly used data visualization for such data but present perceptual challenges that may lead to difficulties in interpretation. Increasing the visual saliency of such data visualizations can help viewers more accurately identify proportional trends and compare proportional differences across spatial locations.

We developed scatterbar, an open-source R package that extends ggplot2, to visualize proportional data across many spatially resolved coordinates using scatter stacked bar plots. We apply scatterbar to visualize deconvolved cell-type proportions from a spatial transcriptomics dataset of the adult mouse brain to demonstrate how scatter stacked bar plots can enhance the distinguishability of proportional distributions compared to scatter pie plots.

scatterbar is available on CRAN https://cran.r-project.org/package=scatterbar with additional documentation and tutorials at https://jef.works/scatterbar/.