Title:	Working with Sets the Tidy Way
Version:	1.0.0
Description:	Implements a class and methods to work with sets, doing intersection, union, complementary sets, power sets, cartesian product and other set operations in a "tidy" way. These set operations are available for both classical sets and fuzzy sets. Import sets from several formats or from other several data structures.
License:	MIT + file LICENSE
URL:	https://github.com/ropensci/BaseSet, https://docs.ropensci.org/BaseSet/
BugReports:	https://github.com/ropensci/BaseSet/issues
Depends:	R (≥ 4.1.0)
Imports:	dplyr (≥ 1.0.0), methods, rlang, utils
Suggests:	Biobase, covr, forcats, ggplot2, GO.db, GSEABase, knitr, org.Hs.eg.db, reactome.db, rmarkdown, spelling, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.3.2
Collate:	'validity.R' 'AllClasses.R' 'AllGenerics.R' 'BaseSet-package.R' 'GMT.R' 'GeneSetCollection.R' 'activate.R' 'add.R' 'add_column.R' 'add_relation.R' 'adjacency.R' 'arrange.R' 'c.R' 'cardinality.R' 'cartesian.R' 'complement.R' 'data_frame.R' 'deactivate.R' 'droplevels.R' 'elements.R' 'extract.R' 'filter.R' 'group.R' 'group_by.R' 'head.R' 'incidence.R' 'independent.R' 'operations.R' 'intersection.R' 'length.R' 'list.R' 'move_to.R' 'mutate.R' 'names.R' 'naming.R' 'nested.R' 'obo.R' 'power_set.R' 'print.R' 'pull.R' 'relations.R' 'remove.R' 'remove_column.R' 'rename.R' 'select.R' 'set.R' 'size.R' 'subtract.R' 'tidy-set.R' 'union.R' 'union_closed.R' 'utils.R' 'zzz.R'
NeedsCompilation:	no
Packaged:	2025-02-17 17:59:43 UTC; lluis
Author:	Lluís Revilla Sancho [aut, cre, cph], Zebulun Arendsee [rev], Jennifer Chang [rev]
Maintainer:	Lluís Revilla Sancho <lluis.revilla@gmail.com>
Repository:	CRAN
Date/Publication:	2025-02-17 20:10:11 UTC

BaseSet: Working with Sets the Tidy Way

Description

Implements a class and methods to work with sets, doing intersection, union, complementary sets, power sets, cartesian product and other set operations in a "tidy" way. These set operations are available for both classical sets and fuzzy sets. Import sets from several formats or from other several data structures.

Details

It provides a class TidySet with methods to do operations with sets.

Author(s)

Maintainer: Lluís Revilla Sancho lluis.revilla@gmail.com (ORCID) [copyright holder]

Other contributors:

• Zebulun Arendsee [reviewer]

• Jennifer Chang [reviewer]

See Also

Useful links:

• https://github.com/ropensci/BaseSet

• https://docs.ropensci.org/BaseSet/

• Report bugs at https://github.com/ropensci/BaseSet/issues

Examples

set <- list("A" = letters[1:5], "B" = letters[4:7])
TS <- tidySet(set)
cardinality(TS)
intersection(TS, c("A", "B"))

A tidy class to represent a set

Description

A set is a group of unique elements it can be either a fuzzy set, where the relationship is between 0 or 1 or nominal.

Details

When printed if an element or a set do not have any relationship is not shown. They can be created from lists, matrices or data.frames. Check tidySet() constructor for more information.

Slots

relations

A data.frame with elements and the sets were they belong.

elements

A data.frame of unique elements and related information.

sets

A data.frame of unique sets and related information.

See Also

tidySet

Other methods: activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

x <- list("A" = letters[1:5], "B" = LETTERS[3:7])
a <- tidySet(x)
a
x <- list("A" = letters[1:5], "B" = character())
b <- tidySet(x)
b
name_sets(b)

Determine the context of subsequent manipulations.

Description

Functions to help to perform some action to just some type of data: elements, sets or relations. activate: To table the focus of future manipulations: elements, sets or relations. active: To check the focus on the TidySet. deactivate: To remove the focus on a specific TidySet-

Usage

activate(.data, what)

active(.data)

deactivate(.data)

Arguments

Value

A TidySet object.

See Also

Other methods: TidySet-class, add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b", rep("a2", 5), "b2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12)
)
a <- tidySet(relations)
elements(a) <- cbind(elements(a),
    type = c(rep("Gene", 4), rep("lncRNA", 2))
)
# Filter in the whole TidySet
filter(a, elements == "a")
filter(a, elements == "a", type == "Gene")
# Equivalent to filter_elements
filter_element(a, type == "Gene")
a <- activate(a, "elements")
active(a)
filter(a, type == "Gene")
a <- deactivate(a)
active(a)
filter(a, type == "Gene")

Add column

Description

Add column to a slot of the TidySet object.

Usage

add_column(object, slot, columns)

## S4 method for signature 'TidySet,character'
add_column(object, slot, columns)

Arguments

Value

A TidySet object.

Methods (by class)

• add_column(object = TidySet, slot = character): Add a column to any slot

See Also

rename_set()

Other column: remove_column()

Other methods: TidySet-class, activate(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
add_column(TS, "relations", data.frame(well = c(
    "GOOD", "BAD", "WORSE",
    "UGLY", "FOE", "HEY"
)))

Add elements to a TidySet

Description

Functions to add elements. If the elements are new they are added, otherwise they are omitted.

Usage

add_elements(object, elements, ...)

Arguments

Value

A TidySet object with the new elements.

Note

add_element doesn't set up any other information about the elements. Remember to add/modify them if needed with mutate or mutate_element

See Also

Other add_*: add_relations(), add_sets()

Examples

x <- list("a" = letters[1:5], "b" = LETTERS[3:7])
a <- tidySet(x)
b <- add_elements(a, "fg")
elements(b)

Add relations

Description

Given a TidySet adds new relations between elements and sets.

Usage

add_relation(object, relations, ...)

## S4 method for signature 'TidySet,data.frame'
add_relation(object, relations)

Arguments

Value

A TidySet object.

Methods (by class)

• add_relation(object = TidySet, relations = data.frame): Adds relations

See Also

Other methods: TidySet-class, activate(), add_column(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
relations <- data.frame(
    sets = c(rep("A2", 5), "B2"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6),
    new = runif(6)
)
add_relation(TS, relations)

Add relations to a TidySet

Description

Adds new relations to existing or new sets and elements. If the sets or elements do not exist they are added.

Usage

add_relations(object, elements, sets, fuzzy, ...)

Arguments

Value

A TidySet object with the new relations.

Note

add_relations doesn't set up any other information about the relationship. Remember to add/modify them if needed with mutate or mutate_relation

See Also

add_relation() to add relations with new sets or/and new elements.

Other add_*: add_elements(), add_sets()

Examples

x <- list("a" = letters[1:5], "b" = LETTERS[3:7])
a <- tidySet(x)
add_relations(a, elements = c("a", "b", "g"), sets = "d")
add_relations(a, elements = c("a", "b"), sets = c("d", "g"))
add_relations(a, elements = c("a", "b"), sets = c("d", "g"), fuzzy = 0.5)
add_relations(a,
    elements = c("a", "b"), sets = c("d", "g"),
    fuzzy = c(0.5, 0.7)
)

Add sets to a TidySet

Description

Functions to add sets. If the sets are new they are added, otherwise they are omitted.

Usage

add_sets(object, sets, ...)

Arguments

Value

A TidySet object with the new sets.

Note

add_sets doesn't set up any other information about the sets. Remember to add/modify them if needed with mutate or mutate_set

See Also

Other add_*: add_elements(), add_relations()

Examples

x <- list("a" = letters[1:5], "b" = LETTERS[3:7])
a <- tidySet(x)
b <- add_sets(a, "fg")
sets(b)

Adjacency

Description

Are two elements connected ?

Usage

## S3 method for class 'TidySet'
adjacency(object)

adjacency_element(object)

adjacency_set(object)

## S3 method for class 'TidySet'
adjacency(object)

Arguments

Value

A square matrix, 1 if two nodes are connected, 0 otherwise.

See Also

incidence()

Examples

x <- list("SET1" = letters[1:5], "SET2" = LETTERS[3:7])
a <- tidySet(x)
adjacency_element(a)
adjacency_set(a)

Arrange the order of a TidySet

Description

Use arrange to extract the columns of a TidySet object. You can use activate with filter or use the specific function. The S3 method filters using all the information on the TidySet.

Usage

## S3 method for class 'TidySet'
arrange(.data, ...)

arrange_set(.data, ...)

arrange_element(.data, ...)

arrange_relation(.data, ...)

Arguments

Value

A TidySet object

See Also

dplyr::arrange() and activate()

Other methods: TidySet-class, activate(), add_column(), add_relation(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B", rep("A2", 5), "B2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12)
)
a <- tidySet(relations)
a <- mutate_element(a,
    type = c(rep("Gene", 4), rep("lncRNA", 2))
)

b <- arrange(a, desc(type))
elements(b)
b <- arrange_element(a, elements)
elements(b)
# Arrange sets
arrange_set(a, sets)

Transforms a TidySet to a data.frame

Description

Flattens the three slots to a single big table

Usage

## S3 method for class 'TidySet'
as.data.frame(x, ...)

Arguments

Value

A data.frame table.

Convert to list

Description

Converts a TidySet to a list.

Usage

## S3 method for class 'TidySet'
as.list(x, ...)

Arguments

Value

A list.

Examples

r <- data.frame(sets = c("A", "A", "A", "B", "C"),
             elements = c(letters[1:3], letters[2:3]),
             fuzzy = runif(5),
             info = rep_len(c("important", "very important"), 5))
TS <- tidySet(r)
TS
as.list(TS)

Combine Values into a Vector or List

Description

This method combines TidySets. It only works if the first element is a TidySet.

Usage

## S4 method for signature 'TidySet'
c(x, ...)

Arguments

Examples

TS <- tidySet(list(A = letters[1:5], B = letters[6]))
TS2 <- c(TS, data.frame(sets = "C", elements = "gg"))

Cardinality or membership of sets

Description

Calculates the membership of sets according to the logic defined in FUN.

Usage

cardinality(object, sets = NULL, ...)

## S4 method for signature 'TidySet'
cardinality(object, sets, FUN = "sum", ...)

Arguments

Methods (by class)

• cardinality(TidySet): Cardinality of sets

See Also

size()

Examples

rel <- list(A = letters[1:3], B = letters[1:2])
TS <- tidySet(rel)
cardinality(TS, "A")

Create the cartesian product of two sets

Description

Given two sets creates new sets with one element of each set

Usage

cartesian(object, set1, set2, name = NULL, ...)

## S3 method for class 'TidySet'
cartesian(
  object,
  set1,
  set2,
  name = NULL,
  keep = TRUE,
  keep_relations = keep,
  keep_elements = keep,
  keep_sets = keep,
  ...
)

Arguments

Value

A TidySet object with the new set

See Also

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b"),
    elements = letters[seq_len(6)]
)
TS <- tidySet(relations)
cartesian(TS, "a", "b")

Complement TidySet

Description

Use complement to find elements or sets the TidySet object. You can use activate with complement or use the specific function. You must specify if you want the complements of sets or elements.

Usage

complement(.data, ...)

Arguments

Value

A TidySet object

See Also

activate()

Other complements: complement_element(), complement_set(), subtract()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

rel <- data.frame(
    sets = c("A", "A", "B", "B", "C", "C"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(rel)
TS |>
    activate("elements") |>
    complement("a")
TS |>
    activate("elements") |>
    complement("a", "C_a", keep = FALSE)
TS |>
    activate("set") |>
    complement("A")
TS |>
    activate("set") |>
    complement("A", keep = FALSE)
TS |>
    activate("set") |>
    complement("A", FUN = function(x){abs(x - 0.2)}, keep = FALSE)

Complement of elements

Description

Return the objects without the elements listed

Usage

complement_element(object, elements, ...)

## S4 method for signature 'TidySet,characterORfactor'
complement_element(
  object,
  elements,
  name = NULL,
  FUN = NULL,
  keep = TRUE,
  keep_relations = keep,
  keep_elements = keep,
  keep_sets = keep
)

Arguments

Value

A TidySet object.

Methods (by class)

• complement_element(object = TidySet, elements = characterORfactor): Complement of the elements.

See Also

Other complements: complement(), complement_set(), subtract()

Other methods that create new sets: complement_set(), intersection(), subtract(), union()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c("A", "A", "B", "B", "C", "C"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
complement_element(TS, "a", "C_a")
complement_element(TS, "a", "C_a", keep = FALSE)

Complement of a set

Description

Return the complement for a set

Usage

complement_set(object, sets, ...)

## S4 method for signature 'TidySet,characterORfactor'
complement_set(
  object,
  sets,
  name = NULL,
  FUN = NULL,
  keep = TRUE,
  keep_relations = keep,
  keep_elements = keep,
  keep_sets = keep
)

Arguments

Value

A TidySet object.

Methods (by class)

• complement_set(object = TidySet, sets = characterORfactor): Complement of the sets.

See Also

filter()

Other complements: complement(), complement_element(), subtract()

Other methods that create new sets: complement_element(), intersection(), subtract(), union()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c("A", "A", "B", "B", "C", "C"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
complement_set(TS, "A")

The opposite of as.data.frame

Description

Convert a data.frame to a TidySet by first using the relations. It requires the original TidySet in order to convert it back to resemble the position of the columns.

Usage

df2TS(.data = NULL, df)

Arguments

Value

A TidySet object

See Also

tidySet.data.frame()

Dimnames of a TidySet

Description

Retrieve the column names of the slots of a TidySet.

Usage

## S3 method for class 'TidySet'
dimnames(x)

Arguments

Value

A list with the names of the columns of the sets, elements and relations.

See Also

names()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
dimnames(TS)

Drop unused elements and sets

Description

Drop elements and sets without any relation.

Usage

## S3 method for class 'TidySet'
droplevels(x, elements = TRUE, sets = TRUE, relations = TRUE, ...)

Arguments

Value

A TidySet object.

Examples

rel <- list(A = letters[1:3], B = character())
TS <- tidySet(rel)
TS
sets(TS)
TS2 <- droplevels(TS)
TS2
sets(TS2)

Calculates the size of the elements

Description

Assuming that the fuzzy values are probabilities, calculates the probability of being of different sizes for a given set.

Usage

element_size(object, elements = NULL)

## S4 method for signature 'TidySet'
element_size(object, elements = NULL)

Arguments

Value

A list with the size of the elements or the probability of having that size.

Methods (by class)

• element_size(TidySet): Calculates the number of sets an element appears with length_set()

See Also

cardinality

Other sizes: set_size()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B", "C"),
    elements = c(letters[seq_len(6)], letters[6]),
    fuzzy = runif(7)
)
a <- tidySet(relations)
element_size(a)

Elements of the TidySet

Description

Given TidySet retrieve the elements or substitute them.

Usage

elements(object)

elements(object) <- value

## S4 method for signature 'TidySet'
elements(object)

## S4 replacement method for signature 'TidySet'
elements(object) <- value

replace_elements(object, value)

## S4 method for signature 'TidySet,missing'
nElements(object)

## S4 method for signature 'TidySet,logical'
nElements(object, all)

Arguments

Value

A data.frame with information about the elements

Methods (by class)

• elements(TidySet): Retrieve the elements

• elements(TidySet) <- value: Modify the elements

• nElements(object = TidySet, all = missing): Return the number of elements

• nElements(object = TidySet, all = logical): Return the number of elements

See Also

nElements()

Other slots: relations(), sets()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

TS <- tidySet(list(A = letters[1:5], B = letters[2:10]))
elements(TS)
elements(TS) <- data.frame(elements = letters[10:1])
TS2 <- replace_elements(TS, data.frame(elements = letters[1:11]))
nElements(TS)
nElements(TS2)

Extract

Description

Operators acting on TidySet to extract or replace parts. They are designed to resemble the basic operators.

Usage

## S4 method for signature 'TidySet'
x$name

## S4 replacement method for signature 'TidySet'
x$name <- value

## S4 method for signature 'TidySet'
x[i, j, k, ..., drop = TRUE]

## S4 replacement method for signature 'TidySet'
x[i, j, k, ...] <- value

## S4 method for signature 'TidySet'
x[[i, j, ..., exact = TRUE]]

## S4 replacement method for signature 'TidySet'
x[[i]] <- value

Arguments

Value

Always returns a valid TidySet.

Examples

TS <- tidySet(list(A = letters[1:5], B = letters[6]))
TS[, "sets", "origin"] <- sample(c("random", "non-random"), 2, replace = TRUE)
TS[, "sets", "type"] <- c("Fantastic", "Wonderful")
# This produces a warning
# TS$description <- c("What", "can", "I", "say", "now", "?")
# Better to be explicit:
TS[, "relations", "description"] <- c("What", "can", "I", "say", "now", "?")
relations(TS)
TS[, "elements", "description"] <- rev(c("What", "can", "I", "say", "now", "?"))
elements(TS)
# Which will be deleted?
# TS$description <- NULL
TS$type
TS$origin <- c("BCN", "BDN")
# Different subsets
TS[1, "elements"]
TS[1, "sets"]
# Always print
TS
TS[, "sets", c("type", "origin")] # Same
TS[, "sets", "origin"] # Drop column type
is(TS[, "sets", "origin"])
TS[, "sets"]
TS[["A"]]
TS[["B"]]
TS[["C"]] # Any other set is the empty set

Apply to fuzzy

Description

Simplify and returns unique results of the object.

Usage

fapply(relations, FUN, ...)

Arguments

Value

A modified TidySet object

Filter TidySet

Description

Use filter to subset the TidySet object. You can use activate with filter or use the specific function. The S3 method filters using all the information on the TidySet.

Usage

## S3 method for class 'TidySet'
filter(.data, ...)

filter_set(.data, ...)

filter_element(.data, ...)

filter_relation(.data, ...)

Arguments

Value

A TidySet object.

See Also

dplyr::filter() and activate()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b", rep("a2", 5), "b2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12),
    type = c(rep("Gene", 4), rep("lncRNA", 2))
)
TS <- tidySet(relations)
TS <- move_to(TS, from = "relations", to = "elements", column = "type")
filter(TS, elements == "a")
# Equivalent to filter_relation
filter(TS, elements == "a", sets == "a")
filter_relation(TS, elements == "a", sets == "a")
# Filter element
filter_element(TS, type == "Gene")
# Filter sets and by property of elements simultaneously
filter(TS, sets == "b", type == "lncRNA")
# Filter sets
filter_set(TS, sets == "b")

Read a GAF file

Description

Read a GO Annotation File (GAF) formatted file

Usage

getGAF(x)

Arguments

Value

A TidySet object

References

The format is defined here.

See Also

Other IO functions: getGMT(), getOBO()

Examples

gafFile <- system.file(
    package = "BaseSet", "extdata",
    "go_human_rna_valid_subset.gaf"
)
gs <- getGAF(gafFile)
head(gs)

Import GMT (Gene Matrix Transposed) files

Description

The GMT (Gene Matrix Transposed) file format is a tab delimited file format that describes groups of genes. In this format, each row represents a group. Each group is described by a name, a description, and the genes in it.

Usage

getGMT(con, sep = "\t", ...)

Arguments

Value

A TidySet object.

References

The file format is defined by the Broad Institute here

See Also

Other IO functions: getGAF(), getOBO()

Examples

gmtFile <- system.file(
    package = "BaseSet", "extdata",
    "hallmark.gene.symbol.gmt"
)
gs <- getGMT(gmtFile)
nRelations(gs)
nElements(gs)
nSets(gs)

Read an OBO file

Description

Read an Open Biological and Biomedical Ontologies (OBO) formatted file

Usage

getOBO(x)

Arguments

Value

A TidySet object.

References

The format is described here

See Also

Other IO functions: getGAF(), getGMT()

Examples

oboFile <- system.file(
    package = "BaseSet", "extdata",
    "go-basic_subset.obo"
)
gs <- getOBO(oboFile)
head(gs)

Create a new set from existing elements

Description

It allows to create a new set given some condition. If no element meet the condition an empty set is created.

Usage

group(object, name, ...)

## S3 method for class 'TidySet'
group(object, name, ...)

Arguments

Value

A TidySet object with the new set.

See Also

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

x <- list("A" = c("a" = 0.1, "b" = 0.5), "B" = c("a" = 0.2, "b" = 1))
TS <- tidySet(x)
TS1 <- group(TS, "C", fuzzy < 0.5)
TS1
sets(TS1)
TS2 <- group(TS, "D", fuzzy < 0)
sets(TS2)
r <- data.frame(
    sets = c(rep("A", 5), "B", rep("A2", 5), "B2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12),
    type = c(rep("Gene", 2), rep("Protein", 2), rep("lncRNA", 2))
)
TS3 <- tidySet(r)
group(TS3, "D", sets %in% c("A", "A2"))

group_by TidySet

Description

Use group_by to group the TidySet object. You can use activate with group_by or with the whole data.

Usage

## S3 method for class 'TidySet'
group_by(.data, ...)

Arguments

Value

A grouped data.frame (See The dplyr help page)

See Also

dplyr::group_by() and activate()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b", rep("a2", 5), "b2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12)
)
a <- tidySet(relations)
elements(a) <- cbind(elements(a),
    type = c(rep("Gene", 4), rep("lncRNA", 2))
)
group_by(a, elements)

Incidence

Description

Check which elements are in which sets.

Usage

incidence(object)

## S4 method for signature 'TidySet'
incidence(object)

Arguments

Value

A matrix with elements in rows and sets in columns where the values indicate the relationship between the element and the set.

Methods (by class)

• incidence(TidySet): Incidence of the TidySet

See Also

adjacency(), tidySet()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

x <- list("a" = letters[1:5], "b" = LETTERS[3:7])
a <- tidySet(x)
incidence(a)

Independence of the sets

Description

Checks if the elements of the sets are present in more than one set.

Usage

independent(object, sets)

Arguments

Value

A logical value indicating if the sets are independent (TRUE) or not.

Examples

x <- list("A" = letters[1:5], "B" = letters[3:7], "C" = letters[6:10])
TS <- tidySet(x)
independent(TS)
independent(TS, c("A", "B"))
independent(TS, c("A", "C"))
independent(TS, c("B", "C"))

Intersection of two or more sets

Description

Given a TidySet creates a new set with the elements on the both of them following the logic defined on FUN.

Usage

intersection(object, sets, ...)

## S4 method for signature 'TidySet,character'
intersection(
  object,
  sets,
  name = NULL,
  FUN = "min",
  keep = FALSE,
  keep_relations = keep,
  keep_elements = keep,
  keep_sets = keep,
  ...
)

Arguments

Details

#' The default uses the min function following the standard fuzzy definition, but it can be changed.

Value

A TidySet object.

Methods (by class)

• intersection(object = TidySet, sets = character): Applies the standard intersection

See Also

Other methods that create new sets: complement_element(), complement_set(), subtract(), union()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

rel <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = c("a", "b", "c", "d", "f", "f")
)
TS <- tidySet(rel)
intersection(TS, c("A", "B")) # Default Name
intersection(TS, c("A", "B"), "C") # Set the name
# Fuzzy set
rel <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = c("a", "b", "c", "d", "f", "f"),
    fuzzy = runif(6)
)
TS2 <- tidySet(rel)
intersection(TS2, c("A", "B"), "C")
intersection(TS2, c("A", "B"), "C", FUN = function(x){max(sqrt(x))})

Check if a TidySet is fuzzy.

Description

Check if there are fuzzy sets. A fuzzy set is a set where the relationship between elements is given by a probability (or uncertainty).

Usage

is.fuzzy(object)

## S4 method for signature 'TidySet'
is.fuzzy(object)

Arguments

Value

A logical value.

Methods (by class)

• is.fuzzy(TidySet): Check if it is fuzzy

See Also

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

TS <- tidySet(list(A = letters[1:5], B = letters[2:10]))
is.fuzzy(TS)

Are some sets as elements of other sets?

Description

Check if some elements are also sets of others. This is also known as hierarchical sets.

Usage

is_nested(object)

## S3 method for class 'TidySet'
is_nested(object)

Arguments

Value

A logical value: TRUE if there are some sets included as elements of others.

See Also

adjacency

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- list(A = letters[1:3], B = c(letters[4:5]))
TS <- tidySet(relations)
is_nested(TS)
TS2 <- add_relation(TS, data.frame(elements = "A", sets = "B"))
# Note that A is both a set and an element of B
TS2
is_nested(TS2)

Length of the TidySet

Description

Returns the number of sets in the object.

Usage

## S3 method for class 'TidySet'
length(x)

Arguments

Value

A numeric value.

See Also

dim(), ncol() and nrow(). Also look at lengths() for the number of relations of sets.

Examples

TS <- tidySet(list(A = letters[1:5], B = letters[6]))
length(TS)

Calculates the probability

Description

Given several probabilities it looks for how probable is to have a vector of each length

Usage

length_set(probability)

Arguments

Value

A vector with the probability of each set.

See Also

length_probability() to calculate the probability of a specific length.

Examples

length_set(c(0.5, 0.1, 0.3, 0.5, 0.25, 0.23))

Lengths of the TidySet

Description

Returns the number of relations of each set in the object.

Usage

## S4 method for signature 'TidySet'
lengths(x, use.names = TRUE)

Arguments

Value

A vector with the number of different relations for each set.

See Also

length(), Use set_size() if you are using fuzzy sets.

Examples

TS <- tidySet(list(A = letters[1:5], B = letters[6]))
lengths(TS)

Move columns between slots

Description

Moves information from one slot to other slots. For instance from the sets to the relations.

Usage

move_to(object, from, to, columns)

## S4 method for signature 
## 'TidySet,characterORfactor,characterORfactor,character'
move_to(object, from, to, columns)

Arguments

Value

A TidySet object where the content is moved from one slot to other.

Methods (by class)

• move_to( object = TidySet, from = characterORfactor, to = characterORfactor, columns = character ): Move columns

See Also

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

x <- list("A" = c("a" = 0.1, "b" = 0.5), "B" = c("a" = 0.2, "b" = 1))
TS <- tidySet(x)
TS <- mutate_element(TS, b = runif(2))
TS2 <- move_to(TS, from = "elements", to = "relations", "b")
# Note that apparently we haven't changed anything:
TS2

Probability of a vector of probabilities

Description

Calculates the probability that all probabilities happened simultaneously. independent_probabilities() just multiply the probabilities of the index passed.

Usage

multiply_probabilities(p, i)

independent_probabilities(p, i)

Arguments

Value

A numeric value of the probability.

See Also

length_probability()

Examples

multiply_probabilities(c(0.5, 0.1, 0.3, 0.5, 0.25, 0.23), c(1, 3))
independent_probabilities(c(0.5, 0.1, 0.3, 0.5, 0.25, 0.23), c(1, 3))

Mutate

Description

Use mutate to alter the TidySet object. You can use activate with mutate or use the specific function. The S3 method filters using all the information on the TidySet.

Usage

## S3 method for class 'TidySet'
mutate(.data, ...)

mutate_set(.data, ...)

mutate_element(.data, ...)

mutate_relation(.data, ...)

Arguments

Value

A TidySet object

See Also

dplyr::mutate() and activate()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b", rep("a2", 5), "b2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12)
)
a <- tidySet(relations)
a <- mutate_element(a, Type = c(rep("Gene", 4), rep("lncRNA", 2)))
a
b <- mutate_relation(a, Type = sample(c("PPI", "PF", "MP"), 12,
    replace = TRUE
))

Number of elements

Description

Check the number of elements of the TidySet.

Usage

nElements(object, all)

Arguments

Value

A numeric value with the number of elements.

See Also

Other count functions: nRelations(), nSets()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

TS <- tidySet(list(A = letters[1:2], B = letters[5:7]))
nElements(TS)

Number of relations

Description

Check the number of relations of the TidySet.

Usage

nRelations(object)

Arguments

Value

A numeric value with the number of the relations.

See Also

Other count functions: nElements(), nSets()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

TS <- tidySet(list(A = letters[1:2], B = letters[5:7]))
nRelations(TS)

Number of sets

Description

Check the number of sets of the TidySet

Usage

nSets(object, all)

Arguments

Value

The number of sets present.

See Also

Other count functions: nElements(), nRelations()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

TS <- tidySet(list(A = letters[1:2], B = letters[5:7]))
nSets(TS)

Name elements

Description

Retrieve the name of the elements.

Usage

name_elements(object, all, ...)

## S4 method for signature 'TidySet,logical'
name_elements(object, all = TRUE)

## S4 method for signature 'TidySet,missing'
name_elements(object, all)

## S4 replacement method for signature 'TidySet,logical,characterORfactor'
name_elements(object, all) <- value

## S4 replacement method for signature 'TidySet,missing,characterORfactor'
name_elements(object) <- value

Arguments

Value

A TidySet object.

Methods (by class)

• name_elements(object = TidySet, all = logical): Name elements

• name_elements(object = TidySet, all = missing): Name elements

• name_elements(object = TidySet, all = logical) <- value: Rename elements

• name_elements(object = TidySet, all = missing) <- value: Rename elements

See Also

Other names: name_elements<-(), name_sets(), name_sets<-(), rename_elements(), rename_set()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
name_elements(TS)

Rename elements

Description

Rename elements.

Usage

name_elements(object, all, ...) <- value

Arguments

Value

A TidySet object.

See Also

rename_elements()

Other names: name_elements(), name_sets(), name_sets<-(), rename_elements(), rename_set()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
TS
name_elements(TS) <- letters[1:6]

Name sets

Description

Retrieve the name of the sets.

Usage

name_sets(object, all, ...)

## S4 method for signature 'TidySet,logical'
name_sets(object, all = TRUE)

## S4 method for signature 'TidySet,missing'
name_sets(object, all)

## S4 replacement method for signature 'TidySet,logical,characterORfactor'
name_sets(object, all) <- value

## S4 replacement method for signature 'TidySet,missing,characterORfactor'
name_sets(object, all) <- value

Arguments

Value

A TidySet object.

Methods (by class)

• name_sets(object = TidySet, all = logical): Name sets

• name_sets(object = TidySet, all = missing): Name sets

• name_sets(object = TidySet, all = logical) <- value: Rename sets

• name_sets(object = TidySet, all = missing) <- value: Rename sets

See Also

Other names: name_elements(), name_elements<-(), name_sets<-(), rename_elements(), rename_set()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
name_sets(TS)

Rename sets

Description

Rename sets.

Usage

name_sets(object, all, ...) <- value

Arguments

Value

A TidySet object.

See Also

rename_set()

Other names: name_elements(), name_elements<-(), name_sets(), rename_elements(), rename_set()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
TS
name_sets(TS) <- LETTERS[1:2]

Names of a TidySet

Description

Retrieve the column names of a slots of a TidySet.

Usage

## S3 method for class 'TidySet'
names(x)

Arguments

Value

A vector with the names of the present columns of the sets, elements and relations. If a slot is active it only returns the names of that slot.

See Also

dimnames()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
names(TS)
names(activate(TS, "sets"))

Name an operation

Description

Helps setting up the name of an operation.

Usage

naming(
  start = NULL,
  sets1,
  middle = NULL,
  sets2 = NULL,
  collapse_symbol = "union"
)

Arguments

Value

A character vector combining the sets

See Also

set_symbols()

Examples

naming(sets1 = c("a", "b"))
naming(sets1 = "a", middle = "union", sets2 = "b")
naming(sets1 = "a", middle = "intersection", sets2 = c("b", "c"))
naming(sets1 = "a", middle = "intersection", sets2 = c("b", "c"))
naming(
    start = "complement", sets1 = "a", middle = "intersection",
    sets2 = c("b", "c"), collapse_symbol = "intersection"
)

Create the power set

Description

Create the power set of the object: All the combinations of the elements of the sets.

Usage

power_set(object, set, name, ...)

Arguments

Value

A TidySet object with the new set.

See Also

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b"),
    elements = letters[seq_len(6)]
)
TS <- tidySet(relations)
power_set(TS, "a", name = "power_set")

Pull from a TidySet

Description

Use pull to extract the columns of a TidySet object. You can use activate with filter or use the specific function. The S3 method filters using all the information on the TidySet.

Usage

## S3 method for class 'TidySet'
pull(.data, var = -1, name = NULL, ...)

pull_set(.data, var = -1, name = NULL, ...)

pull_element(.data, var = -1, name = NULL, ...)

pull_relation(.data, var = -1, name = NULL, ...)

Arguments

Value

A TidySet object

See Also

dplyr::pull() and activate()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b", rep("a2", 5), "b2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12)
)
a <- tidySet(relations)
a <- mutate_element(a, type = c(rep("Gene", 4), rep("lncRNA", 2)))
pull(a, type)
# Equivalent to pull_relation
b <- activate(a, "relations")
pull_relation(b, elements)
pull_element(b, elements)
# Filter element
pull_element(a, type)
# Filter sets
pull_set(a, sets)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

arrange, filter, group_by, mutate, pull, select

Relations of the TidySet

Description

Given TidySet retrieve the relations or substitute them. TidySet() object

Usage

relations(object)

relations(object) <- value

## S4 method for signature 'TidySet'
relations(object)

replace_relations(object, value)

## S4 replacement method for signature 'TidySet'
relations(object) <- value

## S4 method for signature 'TidySet'
nRelations(object)

Arguments

Value

A data.frame with information about the relations between elements and sets.

Methods (by class)

• relations(TidySet): Retrieve the relations

• relations(TidySet) <- value: Modify the relations

• nRelations(TidySet): Return the number of unique relations

See Also

nRelations()

Other slots: elements(), sets()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

TS <- tidySet(list(A = letters[1:2], B = letters[5:7]))
relations(TS)

Remove column

Description

Removes column from a slot of the TidySet object.

Usage

remove_column(object, slot, column_names)

## S4 method for signature 'TidySet,character,character'
remove_column(object, slot, column_names)

Arguments

Value

A TidySet object.

Methods (by class)

• remove_column(object = TidySet, slot = character, column_names = character): Remove columns to any slot

See Also

rename_set()

Other column: add_column()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

x <- data.frame(sets = c(rep("A", 5), rep("B", 5)),
                elements = c(letters[1:5], letters[3:7]),
                extra = sample(c("YES", "NO"), 10, replace = TRUE))
TS <- tidySet(x)
TS
remove_column(TS, "relations", "extra")

Remove elements

Description

Given a TidySet remove elements and the related relations and if required also the sets.

Usage

remove_element(object, elements, ...)

## S4 method for signature 'TidySet,characterORfactor'
remove_element(object, elements)

Arguments

Value

A TidySet object.

Methods (by class)

• remove_element(object = TidySet, elements = characterORfactor): Removes everything related to an element

See Also

Other remove functions: remove_relation(), remove_set()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
remove_element(TS, "c")

Remove a relation

Description

Given a TidySet removes relations between elements and sets

Usage

remove_relation(object, elements, sets, ...)

## S4 method for signature 'TidySet,characterORfactor,characterORfactor'
remove_relation(object, elements, sets)

Arguments

Value

A TidySet object.

Methods (by class)

• remove_relation( object = TidySet, elements = characterORfactor, sets = characterORfactor ): Removes a relation between elements and sets.

See Also

Other remove functions: remove_element(), remove_set()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
remove_relation(TS, "A", "a")

Remove sets

Description

Given a TidySet remove sets and the related relations and if required also the elements

Usage

remove_set(object, sets, ...)

## S4 method for signature 'TidySet,characterORfactor'
remove_set(object, sets)

Arguments

Value

A TidySet object.

Methods (by class)

• remove_set(object = TidySet, sets = characterORfactor): Removes everything related to a set

See Also

Other remove functions: remove_element(), remove_relation()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c("A", "A", "B", "B", "C", "C"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
remove_set(TS, "B")

Rename elements

Description

Change the default names of sets and elements.

Usage

rename_elements(object, old, new)

## S4 method for signature 'TidySet'
rename_elements(object, old, new)

Arguments

Value

A TidySet object.

Methods (by class)

• rename_elements(TidySet): Rename elements

See Also

name_elements()

Other renames: rename_set()

Other names: name_elements(), name_elements<-(), name_sets(), name_sets<-(), rename_set()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_set(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

x <- list("A" = letters[1:5], "B" = letters[3:7])
TS <- tidySet(x)
name_elements(TS)
TS2 <- rename_elements(TS, "a", "first")
name_elements(TS2)

Rename sets

Description

Change the default names of sets and elements.

Usage

rename_set(object, old, new)

## S4 method for signature 'TidySet'
rename_set(object, old, new)

Arguments

Value

A TidySet object.

Methods (by class)

• rename_set(TidySet): Rename sets

See Also

name_sets()

Other renames: rename_elements()

Other names: name_elements(), name_elements<-(), name_sets(), name_sets<-(), rename_elements()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), select.TidySet(), set_size(), sets(), subtract(), union()

Examples

x <- list("A" = letters[1:5], "B" = letters[3:7])
TS <- tidySet(x)
name_sets(TS)
TS2 <- rename_set(TS, "A", "C")
name_sets(TS2)

select from a TidySet

Description

Use select to extract the columns of a TidySet object. You can use activate with filter or use the specific function. The S3 method filters using all the information on the TidySet.

Usage

## S3 method for class 'TidySet'
select(.data, ...)

select_set(.data, ...)

select_element(.data, ...)

select_relation(.data, ...)

Arguments

Value

A TidySet object

See Also

dplyr::select() and activate()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), set_size(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("a", 5), "b", rep("a2", 5), "b2"),
    elements = rep(letters[seq_len(6)], 2),
    fuzzy = runif(12)
)
a <- tidySet(relations)
a <- mutate_element(a,
    type = c(rep("Gene", 4), rep("lncRNA", 2))
)
a <- mutate_set(a, Group = c("UFM", "UAB", "UPF", "MIT"))
b <- select(a, -type)
elements(b)
b <- select_element(a, elements)
elements(b)
# Select sets
select_set(a, sets)

Calculates the size of a set

Description

Assuming that the fuzzy values are probabilities, calculates the probability of being of different sizes for a given set.

Usage

set_size(object, sets = NULL)

## S4 method for signature 'TidySet'
set_size(object, sets = NULL)

Arguments

Value

A list with the size of the set or the probability of having that size.

Methods (by class)

• set_size(TidySet): Calculates the size of a set using length_set()

See Also

cardinality

Other sizes: element_size()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), sets(), subtract(), union()

Examples

relations <- data.frame(
    sets = c(rep("A", 5), "B", "C"),
    elements = c(letters[seq_len(6)], letters[6]),
    fuzzy = runif(7)
)
a <- tidySet(relations)
set_size(a)

A subset of symbols related to sets

Description

Name and symbol of operations related to sets, including intersection and union among others:

Usage

set_symbols

Format

An object of class character of length 16.

References

https://www.fileformat.info/info/unicode/category/Sm/list.htm

Examples

set_symbols

Sets of the TidySet

Description

Given TidySet retrieve the sets or substitute them.

Usage

sets(object)

sets(object) <- value

## S4 method for signature 'TidySet'
sets(object)

## S4 replacement method for signature 'TidySet'
sets(object) <- value

replace_sets(object, value)

## S4 method for signature 'TidySet,missing'
nSets(object)

## S4 method for signature 'TidySet,logical'
nSets(object, all)

Arguments

Value

A data.frame with information from the sets.

Methods (by class)

• sets(TidySet): Retrieve the sets information

• sets(TidySet) <- value: Modify the sets information

• nSets(object = TidySet, all = missing): Return the number of sets

• nSets(object = TidySet, all = logical): Return the number of sets

See Also

nSets()

Other slots: elements(), relations()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), subtract(), union()

Examples

TS <- tidySet(list(A = letters[1:5], B = letters[2:10]))
sets(TS)
sets(TS) <- data.frame(sets = c("B", "A"))
TS2 <- replace_sets(TS, data.frame(sets = c("A", "B", "C")))
sets(TS2)
nSets(TS)
nSets(TS2)

Method to show the TidySet object

Description

Prints the resulting table of a TidySet object. Does not shown elements or sets without any relationship (empty sets). To see them use sets() or elements().

Usage

## S4 method for signature 'TidySet'
show(object)

Arguments

Value

A table with the information of the relationships.

Size

Description

Calculate the size of the elements or sets, using the fuzzy values as probabilities. First it must have active either sets or elements.

Usage

size(object, ...)

Arguments

Value

The size of the elements or sets. If there is no active slot or it is the relations slot returns the TidySet object with a warning.

See Also

A related concept cardinality(). It is calculated using length_set().

Examples

rel <- data.frame(
    sets = c(rep("A", 5), "B", "C"),
    elements = c(letters[seq_len(6)], letters[6])
)
TS <- tidySet(rel)
TS <- activate(TS, "elements")
size(TS)
TS <- activate(TS, "sets")
size(TS)
# With fuzzy sets
relations <- data.frame(
    sets = c(rep("A", 5), "B", "C"),
    elements = c(letters[seq_len(6)], letters[6]),
    fuzzy = runif(7)
)
TS <- tidySet(relations)
TS <- activate(TS, "elements")
size(TS)
TS <- activate(TS, "sets")
size(TS)

Subtract

Description

Elements in a set not present in the other set. Equivalent to setdiff().

Usage

subtract(object, set_in, not_in, ...)

## S4 method for signature 'TidySet,characterORfactor,characterORfactor'
subtract(
  object,
  set_in,
  not_in,
  name = NULL,
  keep = TRUE,
  keep_relations = keep,
  keep_elements = keep,
  keep_sets = keep
)

Arguments

Value

A TidySet object.

Methods (by class)

• subtract( object = TidySet, set_in = characterORfactor, not_in = characterORfactor ): Elements present in sets but not in other sets

See Also

setdiff()

Other complements: complement(), complement_element(), complement_set()

Other methods that create new sets: complement_element(), complement_set(), intersection(), union()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), union()

Examples

relations <- data.frame(
    sets = c("A", "A", "B", "B", "C", "C"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
subtract(TS, "A", "B")
subtract(TS, "A", "B", keep = FALSE)

Create a TidySet object

Description

These functions help to create a TidySet object from data.frame, list, matrix, and GO3AnnDbBimap. They can create both fuzzy and standard sets.

Usage

## S3 method for class 'GeneSetCollection'
tidySet(relations)

## S3 method for class 'GeneColorSet'
tidySet(relations)

## S3 method for class 'GeneSet'
tidySet(relations)

tidySet(relations)

## S3 method for class 'data.frame'
tidySet(relations)

## S3 method for class 'list'
tidySet(relations)

## S3 method for class 'matrix'
tidySet(relations)

## S3 method for class 'Go3AnnDbBimap'
tidySet(relations)

## S3 method for class 'TidySet'
tidySet(relations)

Arguments

Details

Elements or sets without any relation are not shown when printed.

Value

A TidySet object.

Methods (by class)

• tidySet(GeneSetCollection): Converts to a tidySet given a GeneSetCollection

• tidySet(GeneColorSet): Converts to a tidySet given a GeneColorSet

• tidySet(GeneSet): Converts to a tidySet given a GeneSet

• tidySet(data.frame): Given the relations in a data.frame

• tidySet(list): Convert to a TidySet from a list.

• tidySet(matrix): Convert an incidence matrix into a TidySet

• tidySet(Go3AnnDbBimap): Convert Go3AnnDbBimap into a TidySet object.

• tidySet(TidySet): Convert TidySet into a TidySet object.

See Also

TidySet

Examples

# Needs GSEABase package from Bioconductor
if (requireNamespace("GSEABase", quietly = TRUE)) {
    library("GSEABase")
    gs <- GeneSet()
    gs
    tidySet(gs)
    fl <- system.file("extdata", "Broad.xml", package="GSEABase")
    gs2 <- getBroadSets(fl) # actually, a list of two gene sets
    TS <- tidySet(gs2)
    dim(TS)
    sets(TS)
}
relations <- data.frame(
    sets = c(rep("a", 5), "b"),
    elements = letters[seq_len(6)]
)
tidySet(relations)
relations2 <- data.frame(
    sets = c(rep("A", 5), "B"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
tidySet(relations2)
# A
x <- list("A" = letters[1:5], "B" = LETTERS[3:7])
tidySet(x)
# A fuzzy set taken encoded as a list
A <- runif(5)
names(A) <- letters[1:5]
B <- runif(5)
names(B) <- letters[3:7]
relations <- list(A, B)
tidySet(relations)
# Will error
# x <- list("A" = letters[1:5], "B" = LETTERS[3:7], "c" = runif(5))
# a <- tidySet(x) # Only characters or factors are allowed as elements.
M <- matrix(c(1, 0.5, 1, 0), ncol = 2,
            dimnames = list(c("A", "B"), c("a", "b")))
tidySet(M)

Join sets

Description

Given a TidySet merges several sets into the new one using the logic defined on FUN.

Usage

union(object, ...)

## S3 method for class 'TidySet'
union(
  object,
  sets,
  name = NULL,
  FUN = "max",
  keep = FALSE,
  keep_relations = keep,
  keep_elements = keep,
  keep_sets = keep,
  ...
)

Arguments

Details

The default uses the max function following the standard fuzzy definition, but it can be changed. See examples below.

Value

A TidySet object.

See Also

union_probability()

Other methods that create new sets: complement_element(), complement_set(), intersection(), subtract()

Other methods: TidySet-class, activate(), add_column(), add_relation(), arrange.TidySet(), cartesian(), complement(), complement_element(), complement_set(), element_size(), elements(), filter.TidySet(), group(), group_by.TidySet(), incidence(), intersection(), is.fuzzy(), is_nested(), move_to(), mutate.TidySet(), nElements(), nRelations(), nSets(), name_elements<-(), name_sets(), name_sets<-(), power_set(), pull.TidySet(), relations(), remove_column(), remove_element(), remove_relation(), remove_set(), rename_elements(), rename_set(), select.TidySet(), set_size(), sets(), subtract()

Examples

# Classical set
rel <- data.frame(
    sets = c(rep("A", 5), "B", "B"),
    elements = c(letters[seq_len(6)], "a")
)
TS <- tidySet(rel)
union(TS, c("B", "A"))
# Fuzzy set
rel <- data.frame(
    sets = c(rep("A", 5), "B", "B"),
    elements = c(letters[seq_len(6)], "a"),
    fuzzy = runif(7)
)
TS2 <- tidySet(rel)
# Standard default logic
union(TS2, c("B", "A"), "C")
# Probability logic
union(TS2, c("B", "A"), "C", FUN = union_probability)

Union closed sets

Description

Tests if a given object is union-closed.

Usage

union_closed(object, ...)

## S3 method for class 'TidySet'
union_closed(object, sets = NULL, ...)

Arguments

Value

A logical value: TRUE if the combinations of sets produce already existing sets, FALSE otherwise.

Examples

l <- list(A = "1",
     B = c("1", "2"),
     C = c("2", "3", "4"),
     D = c("1", "2", "3", "4")
)
TS <- tidySet(l)
union_closed(TS)
union_closed(TS, sets = c("A", "B", "C"))
union_closed(TS, sets = c("A", "B", "C", "D"))

Calculates the probability of a single length

Description

Creates all the possibilities and then add them up. union_probability Assumes independence between the probabilities to calculate the final size.

Usage

union_probability(p)

length_probability(p, size)

Arguments

Value

A numeric value of the probability of the given size.

See Also

multiply_probabilities() and length_set()

Examples

length_probability(c(0.5, 0.75), 2)
length_probability(c(0.5, 0.75, 0.66), 1)
length_probability(c(0.5, 0.1, 0.3, 0.5, 0.25, 0.23), 2)
union_probability(c(0.5, 0.1, 0.3))