dtrackr (version 0.4.4)

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

Value

the sliced dataframe with the history graph updated.

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.

See Also

dplyr::slice_min()

Examples

library(dplyr)
library(dtrackr)


# Subset the data by the maximum of a given value
iris %>% track() %>% group_by(Species) %>%
  slice_max(prop=0.5, order_by = Sepal.Width,
            .messages="{.count.out} / {.count.in} = {prop} (with ties)",
            .headline="Widest 50% Sepals") %>%
  history()


# The narrowest 25% of the iris data set by group can be calculated in the
# slice_min() function. Recording this is a matter of tracking and
# using glue specs.
iris %>%
  track() %>%
  group_by(Species) %>%
  slice_min(prop=0.25, order_by = Sepal.Width,
            .messages="{.count.out} / {.count.in} (with ties)",
            .headline="narrowest {sprintf('%1.0f',prop*100)}% {Species}") %>%
  history()