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These functions return information about the "current" group or "current"
variable, so only work inside specific contexts like summarise()
and
mutate()
n()
gives the current group size.
cur_data()
gives the current data for the current group (excluding
grouping variables).
cur_data_all()
gives the current data for the current group (including
grouping variables)
cur_group()
gives the group keys, a tibble with one row and one column
for each grouping variable.
cur_group_id()
gives a unique numeric identifier for the current group.
cur_group_rows()
gives the row indices for the current group.
cur_column()
gives the name of the current column (in across()
only).
See group_data()
for equivalent functions that return values for all
groups.
n() cur_data() cur_data_all() cur_group() cur_group_id() cur_group_rows() cur_column()
If you're familiar with data.table:
cur_data()
<-> .SD
cur_group_id()
<-> .GRP
cur_group()
<-> .BY
cur_group_rows()
<-> .I
df <- tibble( g = sample(rep(letters[1:3], 1:3)), x = runif(6), y = runif(6) ) gf <- df %>% group_by(g) gf %>% summarise(n = n()) gf %>% mutate(id = cur_group_id()) gf %>% summarise(row = cur_group_rows()) gf %>% summarise(data = list(cur_group())) gf %>% summarise(data = list(cur_data())) gf %>% summarise(data = list(cur_data_all())) gf %>% mutate(across(everything(), ~ paste(cur_column(), round(.x, 2))))
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