group_by.PKNCAresults: dplyr grouping for PKNCA
In PKNCA: Perform Pharmacokinetic Non-Compartmental Analysis

group_by.PKNCAresults

R Documentation

dplyr grouping for PKNCA

Description

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()`