escheR
Bioc currentUnified multi-dimensional visualizations with Gestalt principles
Release Lineage
Entered 3.17 · Apr 26, 2023
Current · Requires R 4.6
Description
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
11 6 exported
Complexity
3.2 avg / 8 max
Call network
11 nodes / 5 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
1,178
Files
31
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
6
Internal functions
5
Recent export changes
Testing & CI
Has tests
No
Test-to-code ratio
0.00
testthat edition
–
CI present
Yes
CI type
["github-actions"]
PR gated
Yes
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
20%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.3
System requirements
–
C++ standard
–
License
MIT + file LICENSE
License flags
SPDX valid, OSI approved
History
Versions
7
First release
2023-04-25
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
2
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 20%
Topics
Depended on by (2)
Bioconductor (2)
People
- Boyi Guo author maintainer
- Stephanie C. Hicks author
- Erik D. Nelson contributor
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("escheR")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.