slice_tail.trackr_df {dtrackr}

Slice operations

Description

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.

Usage

## S3 method for class 'trackr_df'
slice_tail(
  .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.

...

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.

.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_tail()

Examples

library(dplyr)
library(dtrackr)

# the first 50% of the data frame, is taken and the history tracked
iris %>% track() %>% group_by(Species) %>%
  slice_head(prop=0.5,.messages="{.count.out} / {.count.in}",
             .headline="First {sprintf('%1.0f',prop*100)}%") %>%
  history()

# The last 100 items:
iris %>% track() %>% group_by(Species) %>%
  slice_tail(n=100,.messages="{.count.out} / {.count.in}",
             .headline="Last 100") %>%
  history()