f_slice: Faster 'dplyr::slice()'
In fastplyr: Fast Alternatives to 'tidyverse' Functions

f_slice

R Documentation

Faster `dplyr::slice()`

Description

When there are lots of groups, the f_slice() functions are much faster.

Usage

f_slice(
  data,
  i = 0L,
  ...,
  .by = NULL,
  .order = group_by_order_default(data),
  keep_order = FALSE
)

f_slice_head(
  data,
  n,
  prop,
  .by = NULL,
  .order = group_by_order_default(data),
  keep_order = FALSE
)

f_slice_tail(
  data,
  n,
  prop,
  .by = NULL,
  .order = group_by_order_default(data),
  keep_order = FALSE
)

f_slice_min(
  data,
  order_by,
  n,
  prop,
  .by = NULL,
  with_ties = TRUE,
  na_rm = FALSE,
  .order = group_by_order_default(data),
  keep_order = FALSE
)

f_slice_max(
  data,
  order_by,
  n,
  prop,
  .by = NULL,
  with_ties = TRUE,
  na_rm = FALSE,
  .order = group_by_order_default(data),
  keep_order = FALSE
)

f_slice_sample(
  data,
  n,
  replace = FALSE,
  prop,
  .by = NULL,
  .order = group_by_order_default(data),
  keep_order = FALSE,
  weights = NULL
)

Arguments

`data`	A data frame.
`i`	An integer vector of slice locations. Please see the details below on how `i` works as it only accepts simple integer vectors.
`...`	A temporary argument to give the user an error if dots are used.
`.by`	(Optional). A selection of columns to group by for this operation. Columns are specified using tidy-select.
`.order`	Should the groups be returned in sorted order? If `FALSE`, this will return the groups in order of first appearance, and in many cases is faster.
`keep_order`	Should the sliced data frame be returned in its original order? The default is `FALSE`.
`n`	Number of rows.
`prop`	Proportion of rows.
`order_by`	Variables to order by.
`with_ties`	Should ties be kept together? The default is `TRUE`.
`na_rm`	Should missing values in `f_slice_max()` and `f_slice_min()` be removed? The default is `FALSE`.
`replace`	Should `f_slice_sample()` sample with or without replacement? Default is `FALSE`, without replacement.
`weights`	Probability weights used in `f_slice_sample()`.

Details

Important note about the `i` argument in `f_slice`

i is first evaluated on an un-grouped basis and then searches for those locations in each group. Thus if you supply an expression of slice locations that vary by-group, this will not be respected nor checked. For example,
do f_slice(data, 10:20, .by = group)
not f_slice(data, sample(1:10), .by = group).

The former results in slice locations that do not vary by group but the latter will result in different within-group slice locations which f_slice cannot correctly compute.

To do the the latter type of by-group slicing, use f_filter, e.g.
f_filter(data, row_number() %in% slices, .by = groups) or even faster:
library(cheapr)
f_filter(data, row_number() %in_% slices, .by = groups)