count: Count observations by group

Description Usage Arguments Value Examples

View source: R/count-tally.R

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

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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)

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 error, and require you to specify the name.

.drop

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.

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

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# 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)

# 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)

Example output

Attaching package:dplyrThe following objects are masked frompackage:stats:

    filter, lag

The following objects are masked frompackage:base:

    intersect, setdiff, setequal, union

# A tibble: 38 x 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
# A tibble: 38 x 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
# A tibble: 6 x 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
# A tibble: 15 x 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
# A tibble: 2 x 2
  gender     n
  <chr>  <int>
1 female     2
2 male       1
# A tibble: 2 x 2
  gender     n
  <chr>  <dbl>
1 female     5
2 male      10
# A tibble: 1 x 1
      n
  <int>
1    87
# A tibble: 38 x 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
# A tibble: 3 x 4
  name   gender  runs     n
  <chr>  <chr>  <dbl> <dbl>
1 Max    male      10    10
2 Sandra female     1     5
3 Susan  female     4     5
# A tibble: 3 x 4
  name   gender  runs     n
  <chr>  <chr>  <dbl> <dbl>
1 Max    male      10    15
2 Sandra female     1    15
3 Susan  female     4    15

dplyr documentation built on June 19, 2021, 1:07 a.m.