Package 'BaseSet'

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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))
)
# 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")

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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"),
    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"
)))

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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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"),
    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)

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)
)

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)

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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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)
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)

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.

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)

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"))

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")

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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"),
    elements = letters[seq_len(6)]
)
TS <- tidySet(relations)
cartesian(TS, "a", "b")

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_by.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

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)

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_set(), complement(), 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_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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("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)

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_element(), complement(), 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_element(), complement(), element_size(), elements(), filter.TidySet(), group_by.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("A", "A", "B", "B", "C", "C"),
    elements = letters[seq_len(6)],
    fuzzy = runif(6)
)
TS <- tidySet(relations)
complement_set(TS, "A")

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)

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)

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_element(), complement_set(), complement(), elements(), filter.TidySet(), group_by.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", "C"),
    elements = c(letters[seq_len(6)], letters[6]),
    fuzzy = runif(7)
)
a <- tidySet(relations)
element_size(a)

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_element(), complement_set(), complement(), element_size(), filter.TidySet(), group_by.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

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)

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

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_element(), complement_set(), complement(), element_size(), elements(), group_by.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),
    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")

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)

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)

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)

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_element(), complement_set(), complement(), 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"))

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_element(), complement_set(), complement(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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"))

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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))})

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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))

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)

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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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

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))

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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
))

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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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]

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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]

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"))

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"
)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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")

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.TidySet(), group(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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(), 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)

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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_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")

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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_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")

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_element(), complement_set(), complement(), element_size(), elements(), filter.TidySet(), group_by.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_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")