Use group_by to group the TidySet object. You can use activate with group_by or with the whole data.
Usage
# S3 method for TidySet
group_by(.data, ...)See also
dplyr::group_by() 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(),
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
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)
elements(a) <- cbind(elements(a),
type = c(rep("Gene", 4), rep("lncRNA", 2))
)
group_by(a, elements)
#> # A tibble: 12 × 4
#> # Groups: elements [6]
#> elements sets fuzzy type
#> <chr> <chr> <dbl> <chr>
#> 1 a a 0.642 Gene
#> 2 b a 0.0180 Gene
#> 3 c a 0.190 Gene
#> 4 d a 0.0194 Gene
#> 5 e a 0.617 lncRNA
#> 6 f b 0.181 lncRNA
#> 7 a a2 0.793 Gene
#> 8 b a2 0.424 Gene
#> 9 c a2 0.123 Gene
#> 10 d a2 0.310 Gene
#> 11 e a2 0.604 lncRNA
#> 12 f b2 0.940 lncRNA