Examples

library(ggplot2)
library(ComplexUpset)

Prepare the datasets

movies = as.data.frame(ggplot2movies::movies)
head(movies, 3)

A data.frame: 3 × 24
	title	year	length	budget	rating	votes	r1	r2	r3	r4	⋯	r9	r10	mpaa	Action	Animation	Comedy	Drama	Documentary	Romance	Short
	<chr>	<int>	<int>	<int>	<dbl>	<int>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	$	1971	121	NA	6.4	348	4.5	4.5	4.5	4.5	⋯	4.5	4.5		0	0	1	1	0	0	0
2	$1000 a Touchdown	1939	71	NA	6.0	20	0.0	14.5	4.5	24.5	⋯	4.5	14.5		0	0	1	0	0	0	0
3	$21 a Day Once a Month	1941	7	NA	8.2	5	0.0	0.0	0.0	0.0	⋯	24.5	24.5		0	1	0	0	0	0	1

genres = colnames(movies)[18:24]
genres

‘Action’
‘Animation’
‘Comedy’
‘Drama’
‘Documentary’
‘Romance’
‘Short’

Convert the genre indicator columns to use boolean values:

movies[genres] = movies[genres] == 1
t(head(movies[genres], 3))

A matrix: 7 × 3 of type lgl
	1	2	3
Action	FALSE	FALSE	FALSE
Animation	FALSE	FALSE	TRUE
Comedy	TRUE	TRUE	FALSE
Drama	TRUE	FALSE	FALSE
Documentary	FALSE	FALSE	FALSE
Romance	FALSE	FALSE	FALSE
Short	FALSE	FALSE	TRUE

To keep the examples fast to compile we will operate on a subset of the movies with complete data:

movies[movies$mpaa == '', 'mpaa'] = NA
movies = na.omit(movies)

Utility for changing output parameters in Jupyter notebooks (IRKernel kernel), not relevant if using RStudio or scripting R from terminal:

set_size = function(w, h, factor=1.5) {
    s = 1 * factor
    options(
        repr.plot.width=w * s,
        repr.plot.height=h * s,
        repr.plot.res=100 / factor,
        jupyter.plot_mimetypes='image/png',
        jupyter.plot_scale=1
    )
}

0. Basic usage

There are two required arguments:

the first argument is expected to be a dataframe with both group indicator variables and covariates,
the second argument specifies a list with names of column which indicate the group membership.

Additional arguments can be provided, such as name (specifies xlab() for intersection matrix) or width_ratio (specifies how much space should be occupied by the set size panel). Other such arguments are discussed at length later in this document.

set_size(8, 3)
upset(movies, genres, name='genre', width_ratio=0.1)

0.1 Selecting intersections

We will focus on the intersections with at least ten members (min_size=10) and on a few variables which are significantly different between the intersections (see 2. Running statistical tests).

When using min_size, the empty groups will be skipped by default (e.g. Short movies would have no overlap with size of 10). To keep all groups pass keep_empty_groups=TRUE:

set_size(8, 3)
(
    upset(movies, genres, name='genre', width_ratio=0.1, min_size=10, wrap=TRUE, set_sizes=FALSE)
    + ggtitle('Without empty groups (Short dropped)')
    +    # adding plots is possible thanks to patchwork
    upset(movies, genres, name='genre', width_ratio=0.1, min_size=10, keep_empty_groups=TRUE, wrap=TRUE, set_sizes=FALSE)
    + ggtitle('With empty groups')
)

When empty columns are detected a warning will be issued. The silence it, pass warn_when_dropping_groups=FALSE. Complimentary max_size can be used in tandem.

You can also select intersections by degree (min_degree and max_degree):

set_size(8, 3)
upset(
    movies, genres, width_ratio=0.1,
    min_degree=3,
)

Or request a constant number of intersections with n_intersections:

set_size(8, 3)
upset(
    movies, genres, width_ratio=0.1,
    n_intersections=15
)

0.2 Region selection modes

There are four modes defining the regions of interest on corresponding Venn diagram:

exclusive_intersection region: intersection elements that belong to the sets defining the intersection but not to any other set (alias: distinct), default
inclusive_intersection region: intersection elements that belong to the sets defining the intersection including overlaps with other sets (alias: intersect)
exclusive_union region: union elements that belong to the sets defining the union, excluding those overlapping with any other set
inclusive_union region: union elements that belong to the sets defining the union, including those overlapping with any other set (alias: union)

Example: given three sets $A$, $B$ and $C$ with number of elements defined by the Venn diagram below

abc_data = create_upset_abc_example()

abc_venn = (
    ggplot(arrange_venn(abc_data))
    + coord_fixed()
    + theme_void()
    + scale_color_venn_mix(abc_data)
)

(
    abc_venn
    + geom_venn_region(data=abc_data, alpha=0.05)
    + geom_point(aes(x=x, y=y, color=region), size=1)
    + geom_venn_circle(abc_data)
    + geom_venn_label_set(abc_data, aes(label=region))
    + geom_venn_label_region(
        abc_data, aes(label=size),
        outwards_adjust=1.75,
        position=position_nudge(y=0.2)
    )
    + scale_fill_venn_mix(abc_data, guide=FALSE)
)

For the above sets $A$ and $B$ the region selection modes correspond to region of Venn diagram defined as follows:

exclusive intersection: $(A \cap B) \setminus C$
inclusive intersection: $A \cap B$
exclusive union: $(A \cup B) \setminus C$
inclusive union: $A \cup B$

and have the total number of elements as in the table below:

members mode	exclusive int.	inclusive int.	exclusive union	inclusive union
(A, B)	10	11	110	123
(A, C) == (B, C)	6	7	256	273
(A) == (B)	50	67	50	67
(C)	200	213	200	213
(A, B, C)	1	1	323	323
()	2	2	2	2

set_size(6, 6.5)
simple_venn = (
    abc_venn
    + geom_venn_region(data=abc_data, alpha=0.3)
    + geom_point(aes(x=x, y=y), size=0.75, alpha=0.3)
    + geom_venn_circle(abc_data)
    + geom_venn_label_set(abc_data, aes(label=region), outwards_adjust=2.55)
)
highlight = function(regions) scale_fill_venn_mix(
    abc_data, guide=FALSE, highlight=regions, inactive_color='NA'
)

(
    (
        simple_venn + highlight(c('A-B')) + labs(title='Exclusive intersection of A and B')
        | simple_venn + highlight(c('A-B', 'A-B-C')) + labs(title='Inclusive intersection of A and B')
    ) /
    (
        simple_venn + highlight(c('A-B', 'A', 'B')) + labs(title='Exclusive union of A and B')
        | simple_venn + highlight(c('A-B', 'A-B-C', 'A', 'B', 'A-C', 'B-C')) + labs(title='Inclusive union of A and B')
    )
)

When customizing the intersection_size() it is important to adjust the mode accordingly, as it defaults to exclusive_intersection and cannot be automatically deduced when user customizations are being applied:

set_size(8, 4.5)
abc_upset = function(mode) upset(
    abc_data, c('A', 'B', 'C'), mode=mode, set_sizes=FALSE,
    encode_sets=FALSE,
    queries=list(upset_query(intersect=c('A', 'B'), color='orange')),
    base_annotations=list(
        'Size'=(
            intersection_size(
                mode=mode,
                mapping=aes(fill=exclusive_intersection),
                size=0,
                text=list(check_overlap=TRUE)
            ) + scale_fill_venn_mix(
                data=abc_data,
                guide=FALSE,
                colors=c('A'='red', 'B'='blue', 'C'='green3')
            )
        )
    )
)

(
    (abc_upset('exclusive_intersection') | abc_upset('inclusive_intersection'))
    /
    (abc_upset('exclusive_union') | abc_upset('inclusive_union'))
)

0.3 Displaying all intersections

To display all possible intersections (rather than only the observed ones) use intersections='all'.

Note 1: it is usually desired to filter all the possible intersections down with max_degree and/or min_degree to avoid generating all combinations as those can easily use up all available RAM memory when dealing with multiple sets (e.g. all human genes) due to sheer number of possible combinations

Note 2: using intersections='all' is only reasonable for mode different from the default exclusive intersection.

set_size(8, 3)
upset(
    movies, genres,
    width_ratio=0.1,
    min_size=10,
    mode='inclusive_union',
    base_annotations=list('Size'=(intersection_size(counts=FALSE, mode='inclusive_union'))),
    intersections='all',
    max_degree=3
)

Examples - R

Prepare the datasets

0. Basic usage

0.1 Selecting intersections

0.2 Region selection modes

0.3 Displaying all intersections