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)))#> # A tibble: 6 x 4 #> # Rowwise: #> x y z m #> <dbl> <dbl> <dbl> <dbl> #> 1 0.169 0.768 0.569 0.502 #> 2 0.265 0.0762 0.762 0.367 #> 3 0.0638 0.292 0.0537 0.137 #> 4 0.635 0.456 0.225 0.439 #> 5 0.635 0.0706 0.317 0.341 #> 6 0.0981 0.975 0.723 0.599# use c_across() to more easily select many variables df %>% rowwise() %>% mutate(m = mean(c_across(x:z)))#> # A tibble: 6 x 4 #> # Rowwise: #> x y z m #> <dbl> <dbl> <dbl> <dbl> #> 1 0.169 0.768 0.569 0.502 #> 2 0.265 0.0762 0.762 0.367 #> 3 0.0638 0.292 0.0537 0.137 #> 4 0.635 0.456 0.225 0.439 #> 5 0.635 0.0706 0.317 0.341 #> 6 0.0981 0.975 0.723 0.599#> # A tibble: 6 x 4 #> # Rowwise: #> x y z m #> <dbl> <dbl> <dbl> <dbl> #> 1 0.169 0.768 0.569 0.169 #> 2 0.265 0.0762 0.762 0.0762 #> 3 0.0638 0.292 0.0537 0.0537 #> 4 0.635 0.456 0.225 0.225 #> 5 0.635 0.0706 0.317 0.0706 #> 6 0.0981 0.975 0.723 0.0981#> # A tibble: 6 x 4 #> x y z m #> <dbl> <dbl> <dbl> <dbl> #> 1 0.169 0.768 0.569 0.169 #> 2 0.265 0.0762 0.762 0.0762 #> 3 0.0638 0.292 0.0537 0.0537 #> 4 0.635 0.456 0.225 0.225 #> 5 0.635 0.0706 0.317 0.0706 #> 6 0.0981 0.975 0.723 0.0981# 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))#>#> # A tibble: 6 x 2 #> # Groups: sim [3] #> sim z #> <dbl> <dbl> #> 1 1 1.11 #> 2 2 1.55 #> 3 2 -3.34 #> 4 3 -0.649 #> 5 3 -0.513 #> 6 3 -0.306# If you want one row per simulation, put the results in a list() params %>% rowwise(sim) %>% summarise(z = list(rnorm(n, mean, sd)))#>#> # A tibble: 3 x 2 #> # Groups: sim [3] #> sim z #> <dbl> <list> #> 1 1 <dbl [1]> #> 2 2 <dbl [2]> #> 3 3 <dbl [3]>