It allows to create a new set given some condition. If no element meet the condition an empty set is created.
Arguments
- object
A TidySet object.
- name
The name of the new set.
- ...
A logical condition to subset some elements.
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
#> elements sets fuzzy
#> 1 a A 0.1
#> 2 b A 0.5
#> 3 a B 0.2
#> 4 b B 1.0
#> 5 a C 1.0
sets(TS1)
#> sets
#> 1 A
#> 2 B
#> 3 C
TS2 <- group(TS, "D", fuzzy < 0)
sets(TS2)
#> sets
#> 1 A
#> 2 B
#> 3 D
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"))
#> elements sets fuzzy type
#> 1 a A 0.16486598 Gene
#> 2 b A 0.34676208 Gene
#> 3 c A 0.05620753 Protein
#> 4 d A 0.42469132 Protein
#> 5 e A 0.84043280 lncRNA
#> 6 f B 0.82429102 lncRNA
#> 7 a A2 0.47849854 Gene
#> 8 b A2 0.74406309 Gene
#> 9 c A2 0.77764867 Protein
#> 10 d A2 0.63687125 Protein
#> 11 e A2 0.18039942 lncRNA
#> 12 f B2 0.03654920 lncRNA
#> 13 a D 1.00000000 <NA>
#> 14 b D 1.00000000 <NA>
#> 15 c D 1.00000000 <NA>
#> 16 d D 1.00000000 <NA>
#> 17 e D 1.00000000 <NA>