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(
.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.
Arguments passed on to dplyr::slice
.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.
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()
library(dplyr)
library(dtrackr)
# an arbitrary 50 items from the iris dataframe is selected. The
# history is tracked
iris %>% track() %>% slice(51:100) %>% history()
#> dtrackr history:
#> number of flowchart steps: 2 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> └ "slice data", "150 before", "50 after"