rowwise {dplyr} | R Documentation |
rowwise()
allows you to compute on a data frame a row-at-a-time.
This is most useful when a vectorised function doesn't exist.
Most dplyr verbs preserve row-wise grouping. The exception is summarise()
,
which return a grouped_df. You can explicitly ungroup with ungroup()
or as_tibble()
, or convert to a grouped_df with group_by()
.
rowwise(data, ...)
data |
Input data frame. |
... |
< NB: unlike |
A row-wise data frame with class rowwise_df
. Note that a
rowwise_df
is implicitly grouped by row, but is not a grouped_df
.
Because a rowwise has exactly one row per group it offers a small
convenience for working with list-columns. Normally, summarise()
and
mutate()
extract a groups worth of data with [
. But when you index
a list in this way, you get back another list. When you're working with
a rowwise
tibble, then dplyr will use [[
instead of [
to make your
life a little easier.
nest_by()
for a convenient way of creating rowwise data frames
with nested data.
df <- tibble(x = runif(6), y = runif(6), z = runif(6))
# Compute the mean of x, y, z in each row
df %>% rowwise() %>% mutate(m = mean(c(x, y, z)))
# use c_across() to more easily select many variables
df %>% rowwise() %>% mutate(m = mean(c_across(x:z)))
# Compute the minimum of x and y in each row
df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
# In this case you can use an existing vectorised function:
df %>% mutate(m = pmin(x, y, z))
# Where these functions exist they'll be much faster than rowwise
# so be on the lookout for them.
# rowwise() is also useful when doing simulations
params <- tribble(
~sim, ~n, ~mean, ~sd,
1, 1, 1, 1,
2, 2, 2, 4,
3, 3, -1, 2
)
# Here I supply variables to preserve after the summary
params %>%
rowwise(sim) %>%
summarise(z = rnorm(n, mean, sd))
# If you want one row per simulation, put the results in a list()
params %>%
rowwise(sim) %>%
summarise(z = list(rnorm(n, mean, sd)))