fcount {timeplyr}

A fast replacement to dplyr::count()

Description

Near-identical alternative to dplyr::count().

Usage

fcount(
  data,
  ...,
  wt = NULL,
  sort = FALSE,
  order = df_group_by_order_default(data),
  name = NULL,
  .by = NULL,
  .cols = NULL
)

fadd_count(
  data,
  ...,
  wt = NULL,
  sort = FALSE,
  order = df_group_by_order_default(data),
  name = NULL,
  .by = NULL,
  .cols = NULL
)

Arguments

data

A data frame.

...

Variables to group by.

wt

Frequency weights. Can be NULL or a variable:

• If NULL (the default), counts the number of rows in each group.

• If a variable, computes sum(wt) for each group.

sort

If TRUE, will show the largest groups at the top.

order

Should the groups be calculated as ordered groups? If FALSE, this will return the groups in order of first appearance, and in many cases is faster. If TRUE (the default), the groups are returned in sorted order, exactly the same way as dplyr::count.

name

The name of the new column in the output. If there's already a column called n, it will use nn. If there's a column called n and nn, it'll use nnn, and so on, adding ns until it gets a new name.

.by

(Optional). A selection of columns to group by for this operation. Columns are specified using tidy-select.

.cols

(Optional) alternative to ... that accepts a named character vector or numeric vector. If speed is an expensive resource, it is recommended to use this.

Details

This is a fast and near-identical alternative to dplyr::count() using the collapse package. Unlike collapse::fcount(), this works very similarly to dplyr::count(). The only main difference is that anything supplied to wt is recycled and added as a data variable. Other than that everything works exactly as the dplyr equivalent.

fcount() and fadd_count() can be up to >100x faster than the dplyr equivalents.

Value

A data.frame of frequency counts by group.

Examples

library(timeplyr)
library(dplyr)

iris %>%
  fcount()
iris %>%
  fadd_count(name = "count") %>%
  fslice_head(n = 10)
iris %>%
  group_by(Species) %>%
  fcount()
iris %>%
  fcount(Species)
iris %>%
  fcount(across(where(is.numeric), mean))

### Sorting behaviour

# Sorted by group
starwars %>%
  fcount(hair_color)
# Sorted by frequency
starwars %>%
  fcount(hair_color, sort = TRUE)
# Groups sorted by order of first appearance (faster)
starwars %>%
  fcount(hair_color, order = FALSE)