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dplyr filtering for PKNCA

Source: R/dplyr.R
filter.PKNCAresults.Rd

Filtering a PKNCAconc or PKNCAdose object filters its data. Filtering a PKNCAresults object filters the result table and, when the filter only uses group columns (the columns after the | in the PKNCAconc formula), the concentration data, the dose data, and the intervals of x$data as well, so that the filtered results are consistent with their data. Only those three are filtered; group_ref and units of the PKNCAdata are not.

Usage

# S3 method for class 'PKNCAresults'
filter(.data, ..., .by = NULL, .preserve = FALSE)

# S3 method for class 'PKNCAconc'
filter(.data, ..., .by = NULL, .preserve = FALSE)

# S3 method for class 'PKNCAdose'
filter(.data, ..., .by = NULL, .preserve = FALSE)

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> Expressions that return a logical vector, defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. To combine expressions using | instead, wrap them in when_any(). Only rows for which all expressions evaluate to TRUE are kept (for filter()) or dropped (for filter_out()).

.by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

.preserve

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

Details

The filter is evaluated once, on the result table. The groups that remain there are the groups kept: the rows of the concentration data, dose data, and intervals are kept when their values in the group columns that they share with the result table match a remaining group. A table that has none of those group columns (for example, intervals with no group columns) is left whole, and groups that have no rows in the result table are dropped from the data. Because the filter is evaluated only on the results, any filter that aggregates over rows (such as ID == max(ID)) keeps the same groups in every table.

A filter that uses any other column of the result table (such as PPTESTCD, PPORRES, or exclude), or that uses no column of the result table, filters the result table only. Columns are recognized when the expressions name them (part == "SAD", .data$part == "SAD", and the columns of .by). Variables of the calling environment are not treated as columns. Filters that choose columns without naming them (.data[[var]], across(), if_any(), if_all(), pick(), and tidyselect helpers such as all_of() or starts_with()) filter the result table only, with a message that the data were not filtered. The columns start and end belong to the result table, so a filter on them leaves the data unchanged.

mutate() and the joins change only the result table; assigning into the object directly is the caller's responsibility and is not tracked.

A PKNCAresults object that a PKNCA dplyr verb changes (filter(), mutate(), group_by(), ungroup(), and the joins) no longer matches the hash that checkProvenance() verifies: the hash is replaced by a marker such as "filtered from <hash>" and checkProvenance() returns FALSE. A call that leaves the object identical keeps its provenance.

See also

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