Upset Plots

Upset plots can be generated using the fig_upset() function, which returns a Plotly Go Figure object.

The plot below was generated using a synthetic dataset of patient treatment complications for Dengue, consisting of five complications and their patient counts, as well as counts for the intersections (conjoint occurrences) of the complication subsets.

The dataset is given below as a table (but can also be loaded from the docs/sources/plot-gallery/examples/csv/upset.csv file).

Dengue patient treatment complications - patient counts

Complication	Patient Count
Shock	891
Meningitis	781
Acute renal injury / acute renal failure	757
Cardiac arrest	709
Focal neurological signs	702

and the second showing the intersections between these subsets:

Dengue patient treatment complications - intersection counts

Complication Intersections	Patient Count
Shock, Meningitis, Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs	256
Shock, Meningitis, Acute renal injury / acute renal failure, Cardiac arrest	121
Shock, Meningitis, Acute renal injury / acute renal failure, Focal neurological signs	115
Shock, Meningitis, Cardiac arrest, Focal neurological signs	86
Shock, Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs	67
Shock, Meningitis, Focal neurological signs	37
Shock, Acute renal injury / acute renal failure, Cardiac arrest	37
Shock, Acute renal injury / acute renal failure, Focal neurological signs	34
Shock, Meningitis, Cardiac arrest	31
Shock, Meningitis, Acute renal injury / acute renal failure	30
Meningitis, Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs	30
Shock, Cardiac arrest, Focal neurological signs	26
Meningitis, Acute renal injury / acute renal failure, Focal neurological signs	18
Shock, Meningitis	15
Meningitis, Acute renal injury / acute renal failure, Cardiac arrest	15
Shock, Acute renal injury / acute renal failure	14
Shock, Cardiac arrest	11
Shock, Focal neurological signs	9
Meningitis, Cardiac arrest, Focal neurological signs	8
Meningitis, Acute renal injury / acute renal failure	7
Meningitis, Cardiac arrest	6
Acute renal injury / acute renal failure, Cardiac arrest, Focal neurological signs	6
Acute renal injury / acute renal failure, Cardiac arrest	5
Meningitis, Focal neurological signs	3
Meningitis,	3
Cardiac arrest, Focal neurological signs	3
Shock,	2
Acute renal injury / acute renal failure, Focal neurological signs	2
Focal neurological signs,	2
Cardiac arrest,	1

The fig_upset() function expects these tables in the form of a pair of dataframes, the first dataframe containing the complication counts data with the following columns (in no particular order):

• "index" - a label internal to the function denoting the complication, and prefixed with "compl", e.g. "compl_shock" for shock, "compl_meningitis" for meningitis, "compl_acuterenal" for acute renal injury / failure etc.

• "label" - a descriptive label for the complication

• "short_label" - a more concise descriptive label for the complication, which could be the same as the value of "label"

• "count" - the complication count

and the second dataframe containing the complication intersection data with the following columns (in no particular order):

• "index" - a string form of a tuple of the labels described above for the complications, e.g. "('compl_shock', 'compl_meningitis', 'compl_acuterenal', 'compl_cardiarrest', 'compl_focalneuro')" for the intersection of shock, meningitis, acute renal injury / failure, cardiac arrest, focal neurological signs.

• "label" - a string form of the descriptive labels associated with the complications, e.g. "('Shock ', 'Meningitis', 'Acute renal injury / acute renal failure', 'Cardiac arrest', 'Focal neurological signs')"

• "count" - the complication intersection count

Here are the Python steps you need to generate the plot using the fig_upset() function:

import pandas as pd
from isaricanalytics.visualisation import fig_upset

# Load the CSV data from a string buffer
counts = pd.read_csv("./csv/upset_counts.csv")
intersections = pd.read_csv("./csv/upset_intersections.csv")

# Create and display the figure
fig = fig_upset(
   (counts, intersections),
   title="Upset Plot of Dengue Patient Treatment Complications",
)
fig.update_layout(autosize=True)
fig

Note

Note that the counts and intersections dataframes are provided in a tuple object.

Note

Any dataframe or CSV column names, or dictionary field labels, in the example above that are not specific to the dataset must be as given, otherwise the function may throw an exception or return an incorrect figure.

The figure height and width parameters can be set using the height and width parameters, but it may be more convenient to let Plotly handle this using the figure layout autosize parameter. Refer to the fig_upset() function docstring for more information.