count {dplyr}

Count the observations in each group

Description

count() lets you quickly count the unique values of one or more variables: df %>% count(a, b) is roughly equivalent to df %>% group_by(a, b) %>% summarise(n = n()). count() is paired with tally(), a lower-level helper that is equivalent to df %>% summarise(n = n()). Supply wt to perform weighted counts, switching the summary from n = n() to n = sum(wt).

add_count() and add_tally() are equivalents to count() and tally() but use mutate() instead of summarise() so that they add a new column with group-wise counts.

Usage

count(x, ..., wt = NULL, sort = FALSE, name = NULL)

## S3 method for class 'data.frame'
count(
  x,
  ...,
  wt = NULL,
  sort = FALSE,
  name = NULL,
  .drop = group_by_drop_default(x)
)

tally(x, wt = NULL, sort = FALSE, name = NULL)

add_count(x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = deprecated())

add_tally(x, wt = NULL, sort = FALSE, name = NULL)

Arguments

x

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

...

<data-masking> Variables to group by.

wt

<data-masking> 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.

name

The name of the new column in the output.

If omitted, it will default to n. 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.

.drop

Handling of factor levels that don't appear in the data, passed on to group_by().

For count(): if FALSE will include counts for empty groups (i.e. for levels of factors that don't exist in the data).

For add_count(): deprecated since it can't actually affect the output.

Value

An object of the same type as .data. count() and add_count() group transiently, so the output has the same groups as the input.

Examples

# count() is a convenient way to get a sense of the distribution of
# values in a dataset
starwars %>% count(species)
starwars %>% count(species, sort = TRUE)
starwars %>% count(sex, gender, sort = TRUE)
starwars %>% count(birth_decade = round(birth_year, -1))

# use the `wt` argument to perform a weighted count. This is useful
# when the data has already been aggregated once
df <- tribble(
  ~name,    ~gender,   ~runs,
  "Max",    "male",       10,
  "Sandra", "female",      1,
  "Susan",  "female",      4
)
# counts rows:
df %>% count(gender)
# counts runs:
df %>% count(gender, wt = runs)

# When factors are involved, `.drop = FALSE` can be used to retain factor
# levels that don't appear in the data
df2 <- tibble(
  id = 1:5,
  type = factor(c("a", "c", "a", NA, "a"), levels = c("a", "b", "c"))
)
df2 %>% count(type)
df2 %>% count(type, .drop = FALSE)

# Or, using `group_by()`:
df2 %>% group_by(type, .drop = FALSE) %>% count()

# tally() is a lower-level function that assumes you've done the grouping
starwars %>% tally()
starwars %>% group_by(species) %>% tally()

# both count() and tally() have add_ variants that work like
# mutate() instead of summarise
df %>% add_count(gender, wt = runs)
df %>% add_tally(wt = runs)