Slice operations

Slice operations behave as in dplyr, except the history graph can be updated with tracked dataframe with the before and after sizes of the dataframe. See dplyr::slice(), dplyr::slice_head(), dplyr::slice_tail(), dplyr::slice_min(), dplyr::slice_max(), dplyr::slice_sample(), for more details on the underlying functions.

p_slice_sample(
  .data,
  ...,
  .messages = c("{.count.in} before", "{.count.out} after"),
  .headline = "slice data"
)

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.

...

Arguments passed on to dplyr::slice_sample

n,prop

Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used. If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. prop will be rounded towards zero to generate an integer number of rows.

A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows.

weight_by

<data-masking> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.

replace

Should sampling be performed with (TRUE) or without (FALSE, the default) replacement.

.messages

a set of glue specs. The glue code can use any global variable, {.count.in}, {.count.out} for the input and output dataframes sizes respectively and {.excluded} for the difference

.headline

a glue spec. The glue code can use any global variable, {.count.in}, {.count.out} for the input and output dataframes sizes respectively.

Value

the sliced dataframe with the history graph updated.

See also

dplyr::slice_sample()

Examples

library(dplyr)
library(dtrackr)

# In this example the iris dataframe is resampled 100 times with replacement
# within each group and the
iris %>%
  track() %>%
  group_by(Species) %>%
  slice_sample(n=100, replace=TRUE,
               .messages="{.count.out} / {.count.in} = {n}",
               .headline="100 {Species}") %>%
  history()
#> dtrackr history:
#> number of flowchart steps: 3 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> ├ [Species:setosa]: "100 setosa", "100 / 50 = 100"
#> ├ [Species:versicolor]: "100 versicolor", "100 / 50 = 100"
#> └ [Species:virginica]: "100 virginica", "100 / 50 = 100"