dplyr grouping for PKNCA

Usage

# S3 method for class 'PKNCAresults'
group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))

# S3 method for class 'PKNCAconc'
group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))

# S3 method for class 'PKNCAdose'
group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))

# S3 method for class 'PKNCAresults'
ungroup(x, ...)

# S3 method for class 'PKNCAconc'
ungroup(x, ...)

# S3 method for class 'PKNCAdose'
ungroup(x, ...)

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.

...

In group_by(), variables or computations to group by. Computations are always done on the ungrouped data frame. To perform computations on the grouped data, you need to use a separate mutate() step before the group_by(). Computations are not allowed in nest_by(). In ungroup(), variables to remove from the grouping.

.add

When FALSE, the default, group_by() will override existing groups. To add to the existing groups, use .add = TRUE.

This argument was previously called add, but that prevented creating a new grouping variable called add, and conflicts with our naming conventions.

.drop

Drop groups formed by factor levels that don't appear in the data? The default is TRUE except when .data has been previously grouped with .drop = FALSE. See group_by_drop_default() for details.

x

A tbl()

dplyr grouping for PKNCA

Usage

Arguments

See also