mutate.TidySet {BaseSet} | R Documentation |
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
.data |
The TidySet object. |
... |
The logical predicates in terms of the variables of the sets. |
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
))