Get all the goodies of
UpSetR, but with full extensibility of
Click here to display the source code
library(ggplot2) library(ComplexUpset) if(!require(ggplot2movies)) install.packages('ggplot2movies') movies = ggplot2movies::movies genres = c('Action', 'Animation', 'Comedy', 'Drama', 'Documentary', 'Romance') upset( movies, genres, annotations = list( 'Length'=ggplot(mapping=aes(x=intersection, y=length)) + geom_boxplot(), 'Rating'=ggplot(mapping=aes(x=intersection, y=rating)) # if you do not want to install ggbeeswarm, you can use geom_jitter + ggbeeswarm::geom_quasirandom(aes(color=log10(votes))) + geom_violin(width=1.1, alpha=0.5) ), queries=list( upset_query( intersect=c('Drama', 'Comedy'), color='red', fill='red', only_components=c('intersections_matrix', 'Intersection size') ), upset_query( set='Drama', fill='blue' ), upset_query( intersect=c('Romance', 'Drama'), fill='yellow', only_components=c('Length') ) ), min_size=10, width_ratio=0.1 )
The full list of examples is available in the documentation; it also contains instructions for the use from Python.
To get the most recent version, open
R and run:
if(!require(devtools)) install.packages("devtools") devtools::install_github("krassowski/complex-upset")
Alternatively, to get a stable CRAN release (which may be one version behind at times):
Or, if you use conda/mamba:
-c conda-forge r-complexupsetconda install
scale_x_upset- a really nice approach, recommended for simple ggplot.
Use whichever tool you find the most useful for your particular use. The rational of making this repository public is not only to share the code, but also to demonstrate how simple it is now to create complex visualisations with patchwork (without the need to learn the ggplot internals).
Here are example publications and preprints including figures generated with this library:
The list is not meant to be exhaustive, but representative of applications to different fields, and of different usage ideas. You are welcome to add your own publication by suggesting an edit.
The unit tests are run with testhat, and the visual “doppelganger” tests use vdiffr.
The test cases for visual tests are auto-generated from the examples in the documentation; after changing or adding an example, please run:
scripts/prepare_for_tests.shto generate updated test cases, and
scripts/manage_visual_tests.shto validate the generated images.
Originally developed in course of a DPhil programme in Women’s & Reproductive Health at OxfordNuffieldWRH.