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Implementations of UpSet

The basic idea of UpSet has been implemented many times. Here we list the available implementations and describe their main benefits and drawbacks.

We distinguish between three-types of implementations:

Interactive Tools that are meant for uploading data and customizing the plot dynamically, without programming.
Plotting Libraries that are designed to be customized in a programming language, e.g., in R or Python and can be used as part of a computational notebook, or can be integrated in a larger system. Some of these tools also have interactive wrappers, using e.g., R-Shiny so that they can be used without programming.
Sample Implementations demonstrate how to implement an UpSet plot in a framework (e.g., Tableau) but are not libraries and hence typically don't have data loading capabilities and are tested only with a single dataset. It's usually possible to copy a template and customize it to work with your own data.

Interactive Tools

{% include version_list_entry.html key="upset_original" %}

{% include version_list_entry.html key="upset_2" %}

{% include version_list_entry.html key="upset.js" %}

{% include version_list_entry.html key="upsetr" %}

{% include version_list_entry.html key="complex_upset" %}

{% include version_list_entry.html key="upset_complex_heatmap" %}

{% include version_list_entry.html key="py-upset" %}

{% include version_list_entry.html key="upsetplot" %}

{% include version_list_entry.html key="upset-altair" %}

{% include version_list_entry.html key="upset-tableau" %}

{% include version_list_entry.html key="upset_observable" %}

{% include version_list_entry.html key="upset-excel" %}