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.
count(x, ..., wt = NULL, sort = FALSE, name = NULL)
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)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.
<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.
If TRUE, will show the largest groups at the top.
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 error, and require you to specify the name.
For count(): if FALSE will include counts for empty groups
(i.e. for levels of factors that don't exist in the data). Deprecated in
add_count() since it didn't actually affect the output.
An object of the same type as .data. count() and add_count()group transiently, so the output has the same groups as the input.
# count() is a convenient way to get a sense of the distribution of
# values in a dataset
starwars %>% count(species)
#> # A tibble: 38 × 2
#> species n
#> <chr> <int>
#> 1 Aleena 1
#> 2 Besalisk 1
#> 3 Cerean 1
#> 4 Chagrian 1
#> 5 Clawdite 1
#> 6 Droid 6
#> 7 Dug 1
#> 8 Ewok 1
#> 9 Geonosian 1
#> 10 Gungan 3
#> # … with 28 more rows
starwars %>% count(species, sort = TRUE)
#> # A tibble: 38 × 2
#> species n
#> <chr> <int>
#> 1 Human 35
#> 2 Droid 6
#> 3 NA 4
#> 4 Gungan 3
#> 5 Kaminoan 2
#> 6 Mirialan 2
#> 7 Twi'lek 2
#> 8 Wookiee 2
#> 9 Zabrak 2
#> 10 Aleena 1
#> # … with 28 more rows
starwars %>% count(sex, gender, sort = TRUE)
#> # A tibble: 6 × 3
#> sex gender n
#> <chr> <chr> <int>
#> 1 male masculine 60
#> 2 female feminine 16
#> 3 none masculine 5
#> 4 NA NA 4
#> 5 hermaphroditic masculine 1
#> 6 none feminine 1
starwars %>% count(birth_decade = round(birth_year, -1))
#> # A tibble: 15 × 2
#> birth_decade n
#> <dbl> <int>
#> 1 10 1
#> 2 20 6
#> 3 30 4
#> 4 40 6
#> 5 50 8
#> 6 60 4
#> 7 70 4
#> 8 80 2
#> 9 90 3
#> 10 100 1
#> 11 110 1
#> 12 200 1
#> 13 600 1
#> 14 900 1
#> 15 NA 44
# 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)
#> # A tibble: 2 × 2
#> gender n
#> <chr> <int>
#> 1 female 2
#> 2 male 1
# counts runs:
df %>% count(gender, wt = runs)
#> # A tibble: 2 × 2
#> gender n
#> <chr> <dbl>
#> 1 female 5
#> 2 male 10
# tally() is a lower-level function that assumes you've done the grouping
starwars %>% tally()
#> # A tibble: 1 × 1
#> n
#> <int>
#> 1 87
starwars %>% group_by(species) %>% tally()
#> # A tibble: 38 × 2
#> species n
#> <chr> <int>
#> 1 Aleena 1
#> 2 Besalisk 1
#> 3 Cerean 1
#> 4 Chagrian 1
#> 5 Clawdite 1
#> 6 Droid 6
#> 7 Dug 1
#> 8 Ewok 1
#> 9 Geonosian 1
#> 10 Gungan 3
#> # … with 28 more rows
# both count() and tally() have add_ variants that work like
# mutate() instead of summarise
df %>% add_count(gender, wt = runs)
#> # A tibble: 3 × 4
#> name gender runs n
#> <chr> <chr> <dbl> <dbl>
#> 1 Max male 10 10
#> 2 Sandra female 1 5
#> 3 Susan female 4 5
df %>% add_tally(wt = runs)
#> # A tibble: 3 × 4
#> name gender runs n
#> <chr> <chr> <dbl> <dbl>
#> 1 Max male 10 15
#> 2 Sandra female 1 15
#> 3 Susan female 4 15