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 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.
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
#> elements sets fuzzy Type
#> 1 a a 0.16456925 Gene
#> 2 b a 0.66320658 Gene
#> 3 c a 0.85657500 Gene
#> 4 d a 0.92654645 Gene
#> 5 e a 0.55237759 lncRNA
#> 6 f b 0.57706569 lncRNA
#> 7 a a2 0.68744775 Gene
#> 8 b a2 0.24471823 Gene
#> 9 c a2 0.04461716 Gene
#> 10 d a2 0.90985456 Gene
#> 11 e a2 0.07068122 lncRNA
#> 12 f b2 0.99689147 lncRNA
b <- mutate_relation(a, Type = sample(c("PPI", "PF", "MP"), 12,
replace = TRUE
))