| union {BaseSet} | R Documentation |
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
object |
A TidySet object. |
... |
Other named arguments passed to |
sets |
The name of the sets to be used. |
name |
The name of the new set. By defaults joins the sets with an ∩. |
FUN |
A function to be applied when performing the union. The standard union is the "max" function, but you can provide any other function that given a numeric vector returns a single number. |
keep |
A logical value if you want to keep. |
keep_relations |
A logical value if you wan to keep old relations. |
keep_elements |
A logical value if you wan to keep old elements. |
keep_sets |
A logical value if you wan to keep old sets. |
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
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_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()
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)