reframe {dplyr}

Transform each group to an arbitrary number of rows

Description

While summarise() requires that each argument returns a single value, and mutate() requires that each argument returns the same number of rows as the input, reframe() is a more general workhorse with no requirements on the number of rows returned per group.

reframe() creates a new data frame by applying functions to columns of an existing data frame. It is most similar to summarise(), with two big differences:

• reframe() can return an arbitrary number of rows per group, while summarise() reduces each group down to a single row.

• reframe() always returns an ungrouped data frame, while summarise() might return a grouped or rowwise data frame, depending on the scenario.

We expect that you'll use summarise() much more often than reframe(), but reframe() can be particularly helpful when you need to apply a complex function that doesn't return a single summary value.

Usage

reframe(.data, ..., .by = NULL)

Arguments

.data

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

...

<data-masking>

Name-value pairs of functions. The name will be the name of the variable in the result. The value can be a vector of any length.

Unnamed data frame values add multiple columns from a single expression.

.by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

Value

If .data is a tibble, a tibble. Otherwise, a data.frame.

• The rows originate from the underlying grouping keys.

• The columns are a combination of the grouping keys and the expressions that you provide.

• The output is always ungrouped.

• Data frame attributes are not preserved, because reframe() fundamentally creates a new data frame.

Connection to tibble

reframe() is theoretically connected to two functions in tibble, tibble::enframe() and tibble::deframe():

• enframe(): vector -> data frame

• deframe(): data frame -> vector

• reframe(): data frame -> data frame

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: no methods found.

See Also

Other single table verbs: arrange(), filter(), mutate(), rename(), select(), slice(), summarise()

Examples

table <- c("a", "b", "d", "f")

df <- tibble(
  g = c(1, 1, 1, 2, 2, 2, 2),
  x = c("e", "a", "b", "c", "f", "d", "a")
)

# `reframe()` allows you to apply functions that return
# an arbitrary number of rows
df %>%
  reframe(x = intersect(x, table))

# Functions are applied per group, and each group can return a
# different number of rows.
df %>%
  reframe(x = intersect(x, table), .by = g)

# The output is always ungrouped, even when using `group_by()`
df %>%
  group_by(g) %>%
  reframe(x = intersect(x, table))

# You can add multiple columns at once using a single expression by returning
# a data frame.
quantile_df <- function(x, probs = c(0.25, 0.5, 0.75)) {
  tibble(
    val = quantile(x, probs, na.rm = TRUE),
    quant = probs
  )
}

x <- c(10, 15, 18, 12)
quantile_df(x)

starwars %>%
  reframe(quantile_df(height))

starwars %>%
  reframe(quantile_df(height), .by = homeworld)

starwars %>%
  reframe(
    across(c(height, mass), quantile_df, .unpack = TRUE),
    .by = homeworld
  )