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.
# S3 method for trackr_df
slice_sample(
.data,
...,
.messages = c("{.count.in} before", "{.count.out} after"),
.headline = "slice 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.
For slice(): <data-masking>
Integer row values.
Provide either positive values to keep, or negative values to drop. The values provided must be either all positive or all negative. Indices beyond the number of rows in the input are silently ignored.
For slice_*(), these arguments are passed on to methods.
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
a glue spec. The glue code can use any global variable, {.count.in}, {.count.out} for the input and output dataframes sizes respectively.
the sliced dataframe with the history graph updated.
dplyr::slice_sample()
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"