rowwise {dplyr} R Documentation

## Group input by rows

### Description

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().

### Usage

rowwise(data, ...)


### Arguments

 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().

### Value

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.

### List-columns

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.

### Examples

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)))


[Package dplyr version 1.0.10 Index]