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 TidySet,character
intersection(
object,
sets,
name = NULL,
FUN = "min",
keep = FALSE,
keep_relations = keep,
keep_elements = keep,
keep_sets = keep,
...
)Arguments
- object
A TidySet object.
- sets
The character of sets to be intersect.
- ...
Other named arguments passed to
FUN.- 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 intersection is the "min" 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 originals sets.
- 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 min function following the standard fuzzy definition, but it can
be changed.
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
#> elements sets fuzzy
#> 1 f A∩B 1
intersection(TS, c("A", "B"), "C") # Set the name
#> elements sets fuzzy
#> 1 f C 1
# 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")
#> elements sets fuzzy
#> 1 f C 0.4028812
intersection(TS2, c("A", "B"), "C", FUN = function(x){max(sqrt(x))})
#> elements sets fuzzy
#> 1 f C 0.8772857