dplyr grouping for PKNCA

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.

...

<data-masking> 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.

.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()

See also

Other dplyr verbs: filter.PKNCAresults(), inner_join.PKNCAresults(), mutate.PKNCAresults()