dplyr (version 1.0.10)

arrange: Arrange rows by column values

Description

arrange() orders the rows of a data frame by the values of selected columns.

Unlike other dplyr verbs, arrange() largely ignores grouping; you need to explicitly mention grouping variables (or use .by_group = TRUE) in order to group by them, and functions of variables are evaluated once per data frame, not once per group.

Usage

arrange(.data, ..., .by_group = FALSE)

Value

An object of the same type as .data. The output has the following properties:

• All rows appear in the output, but (usually) in a different place.

• Columns are not modified.

• Groups are not modified.

• Data frame attributes are preserved.

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> Variables, or functions of variables. Use desc() to sort a variable in descending order.

.by_group

If TRUE, will sort first by grouping variable. Applies to grouped data frames only.

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: dplyr:::methods_rd("arrange").

Details

Locales

The sort order for character vectors will depend on the collating sequence of the locale in use: see locales().

Missing values

Unlike base sorting with sort(), NA are:

• always sorted to the end for local data, even when wrapped with desc().

• treated differently for remote data, depending on the backend.

See Also

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

Examples

arrange(mtcars, cyl, disp)
arrange(mtcars, desc(disp))

# grouped arrange ignores groups
by_cyl <- mtcars %>% group_by(cyl)
by_cyl %>% arrange(desc(wt))
# Unless you specifically ask:
by_cyl %>% arrange(desc(wt), .by_group = TRUE)

# use embracing when wrapping in a function;
# see ?dplyr_data_masking for more details
tidy_eval_arrange <- function(.data, var) {
  .data %>%
    arrange({{ var }})
}
tidy_eval_arrange(mtcars, mpg)

# use across() access select()-style semantics
iris %>% arrange(across(starts_with("Sepal")))
iris %>% arrange(across(starts_with("Sepal"), desc))