Filtering data

Filter acts in the same way as in dplyr where predicates which evaluate to TRUE act to select items to include, and items for which the predicate cannot be evaluated are excluded. For tracking prior to the filter operation the size of each group is calculated {.count.in} and after the operation the output size of each group {.count.out}. The grouping {.strata} is also available (if grouped) for reporting. See dplyr::filter().

Usage

p_filter(
  .data,
  ...,
  .messages = "excluded {.excluded} items",
  .headline = .defaultHeadline(),
  .type = "exclusion",
  .asOffshoot = (.type == "exclusion"),
  .stage = (if (is.null(.tag)) "" else .tag),
  .tag = NULL
)

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 value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept. Named arguments passed on to dplyr::filter

.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.

.messages

a set of glue specs. The glue code can use any global variable, or {.strata},{.count.in},and {.count.out}

.headline

a headline glue spec. The glue code can use any global variable, or {.strata},{.count.in},and {.count.out}

.type

the format type of the action typically an exclusion

.asOffshoot

if the type is exclusion, .asOffshoot places the information box outside of the main flow, as an exclusion.

.stage

a name for this step in the pathway

.tag

if you want the summary data from this step in the future then give it a name with .tag.

Value

the filtered .data dataframe with history graph updated

See also

dplyr::filter()

Examples

library(dplyr)
library(dtrackr)

tmp = iris %>% track() %>% group_by(Species)
tmp %>% filter(Petal.Length > 5) %>% history()
#> dtrackr history:
#> number of flowchart steps: 2 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> └ "stratify by Species"