group_by {dplyr} | R Documentation

## Group by one or more variables

### Description

Most data operations are done on groups defined by variables. `group_by()` takes an existing tbl and converts it into a grouped tbl where operations are performed "by group". `ungroup()` removes grouping.

### Usage

    group_by(.data, ..., add = FALSE)

    ungroup(x, ...)

### Arguments

`.data` | a tbl
`...` | Variables to group by. All tbls accept variable names. Some tbls will accept functions of variables. Duplicated groups will be silently dropped.
`add` | When `add = FALSE`, the default, `group_by()` will override existing groups. To add to the existing groups, use `add = TRUE`.
`x` | A `tbl()`

### Tbl types

`group_by()` is an S3 generic with methods for the three built-in tbls. See the help for the corresponding classes and their manip methods for more details:

- data.frame: grouped_df

- data.table: dtplyr::grouped_dt

- SQLite: `src_sqlite()`

- PostgreSQL: `src_postgres()`

- MySQL: `src_mysql()`

### Scoped grouping

The three scoped variants (`group_by_all()`, `group_by_if()` and `group_by_at()`) make it easy to group a dataset by a selection of variables.

### Examples

    by_cyl <- mtcars %>% group_by(cyl)

    # grouping doesn't change how the data looks (apart from listing
    # how it's grouped):
    by_cyl

    # It changes how it acts with the other dplyr verbs:
    by_cyl %>% summarise(
      disp = mean(disp),
      hp = mean(hp)
    )
    by_cyl %>% filter(disp == max(disp))

    # Each call to summarise() removes a layer of grouping
    by_vs_am <- mtcars %>% group_by(vs, am)
    by_vs <- by_vs_am %>% summarise(n = n())
    by_vs
    by_vs %>% summarise(n = sum(n))

    # To removing grouping, use ungroup
    by_vs %>%
      ungroup() %>%
      summarise(n = sum(n))

    # You can group by expressions: this is just short-hand for
    # a mutate/rename followed by a simple group_by
    mtcars %>% group_by(vsam = vs + am)

    # By default, group_by overrides existing grouping
    by_cyl %>%
      group_by(vs, am) %>%
      group_vars()

    # Use add = TRUE to instead append
    by_cyl %>%
      group_by(vs, am, add = TRUE) %>%
      group_vars()

[Package dplyr version 0.7.0 Index]