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, ...)Input data frame.
<tidy-select> Variables to be preserved
when calling summarise(). This is typically a set of variables whose
combination uniquely identify each row.
NB: unlike group_by() you can not create new variables here but
instead you can select multiple variables with (e.g.) everything().
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)))
#> # A tibble: 6 × 4
#> # Rowwise:
#> x y z m
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.316 0.126 0.899 0.447
#> 2 0.917 0.572 0.105 0.531
#> 3 0.509 0.799 0.823 0.711
#> 4 0.779 0.434 0.959 0.724
#> 5 0.693 0.00830 0.874 0.525
#> 6 0.359 0.944 0.433 0.579
# use c_across() to more easily select many variables
df %>% rowwise() %>% mutate(m = mean(c_across(x:z)))
#> # A tibble: 6 × 4
#> # Rowwise:
#> x y z m
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.316 0.126 0.899 0.447
#> 2 0.917 0.572 0.105 0.531
#> 3 0.509 0.799 0.823 0.711
#> 4 0.779 0.434 0.959 0.724
#> 5 0.693 0.00830 0.874 0.525
#> 6 0.359 0.944 0.433 0.579
# Compute the minimum of x and y in each row
df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
#> # A tibble: 6 × 4
#> # Rowwise:
#> x y z m
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.316 0.126 0.899 0.126
#> 2 0.917 0.572 0.105 0.105
#> 3 0.509 0.799 0.823 0.509
#> 4 0.779 0.434 0.959 0.434
#> 5 0.693 0.00830 0.874 0.00830
#> 6 0.359 0.944 0.433 0.359
# In this case you can use an existing vectorised function:
df %>% mutate(m = pmin(x, y, z))
#> # A tibble: 6 × 4
#> x y z m
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.316 0.126 0.899 0.126
#> 2 0.917 0.572 0.105 0.105
#> 3 0.509 0.799 0.823 0.509
#> 4 0.779 0.434 0.959 0.434
#> 5 0.693 0.00830 0.874 0.00830
#> 6 0.359 0.944 0.433 0.359
# 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))
#> `summarise()` has grouped output by 'sim'. You can override using the `.groups`
#> argument.
#> # A tibble: 6 × 2
#> # Groups: sim [3]
#> sim z
#> <dbl> <dbl>
#> 1 1 1.26
#> 2 2 0.939
#> 3 2 3.91
#> 4 3 0.199
#> 5 3 -1.64
#> 6 3 -0.638
# If you want one row per simulation, put the results in a list()
params %>%
rowwise(sim) %>%
summarise(z = list(rnorm(n, mean, sd)))
#> `summarise()` has grouped output by 'sim'. You can override using the `.groups`
#> argument.
#> # A tibble: 3 × 2
#> # Groups: sim [3]
#> sim z
#> <dbl> <list>
#> 1 1 <dbl [1]>
#> 2 2 <dbl [2]>
#> 3 3 <dbl [3]>