tally() is a convenient wrapper for summarise that will either call n() or sum(n) depending on whether you're tallying for the first time, or re-tallying. count() is similar but calls group_by() before and ungroup() after. If the data is already grouped, count() adds an additional group that is removed afterwards.

add_tally() adds a column n to a table based on the number of items within each existing group, while add_count() is a shortcut that does the grouping as well. These functions are to tally() and count() as mutate() is to summarise(): they add an additional column rather than collapsing each group.

tally(x, wt, sort = FALSE, name = "n")

count(x, ..., wt = NULL, sort = FALSE, name = "n",
  .drop = group_drops(x))

add_tally(x, wt, sort = FALSE, name = "n")

add_count(x, ..., wt = NULL, sort = FALSE, name = "n")

Arguments

x

a tbl() to tally/count.

wt

(Optional) If omitted (and no variable named n exists in the data), will count the number of rows. If specified, will perform a "weighted" tally by summing the (non-missing) values of variable wt. A column named n (but not nn or nnn) will be used as weighting variable by default in tally(), but not in count(). This argument is automatically quoted and later evaluated in the context of the data frame. It supports unquoting. See vignette("programming") for an introduction to these concepts.

sort

if TRUE will sort output in descending order of n

name

The output column name. If omitted, it will be n.

...

Variables to group by.

.drop

see group_by()

Value

A tbl, grouped the same way as x.

Note

The column name in the returned data is given by the name argument, set to "n" by default.

If the data already has a column by that name, the output column will be prefixed by an extra "n" as many times as necessary.

Examples

# tally() is short-hand for summarise() mtcars %>% tally()
#> n #> 1 32
mtcars %>% group_by(cyl) %>% tally()
#> # A tibble: 3 x 2 #> cyl n #> <dbl> <int> #> 1 4 11 #> 2 6 7 #> 3 8 14
# count() is a short-hand for group_by() + tally() mtcars %>% count(cyl)
#> # A tibble: 3 x 2 #> cyl n #> <dbl> <int> #> 1 4 11 #> 2 6 7 #> 3 8 14
# Note that if the data is already grouped, count() adds # an additional group that is removed afterwards mtcars %>% group_by(gear) %>% count(carb)
#> # A tibble: 11 x 3 #> # Groups: gear [3] #> gear carb n #> <dbl> <dbl> <int> #> 1 3 1 3 #> 2 3 2 4 #> 3 3 3 3 #> 4 3 4 5 #> 5 4 1 4 #> 6 4 2 4 #> 7 4 4 4 #> 8 5 2 2 #> 9 5 4 1 #> 10 5 6 1 #> 11 5 8 1
# add_tally() is short-hand for mutate() mtcars %>% add_tally()
#> # A tibble: 32 x 12 #> mpg cyl disp hp drat wt qsec vs am gear carb n #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 32 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 32 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 32 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 32 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 32 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 32 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 32 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 32 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 32 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 32 #> # … with 22 more rows
# add_count() is a short-hand for group_by() + add_tally() mtcars %>% add_count(cyl)
#> # A tibble: 32 x 12 #> mpg cyl disp hp drat wt qsec vs am gear carb n #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 7 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 7 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 11 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 7 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 14 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 7 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 14 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 11 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 11 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 7 #> # … with 22 more rows
# count() and tally() are designed so that you can call # them repeatedly, each time rolling up a level of detail species <- starwars %>% count(species, homeworld, sort = TRUE) species
#> # A tibble: 58 x 3 #> species homeworld n #> <chr> <chr> <int> #> 1 Human Tatooine 8 #> 2 Human <NA> 5 #> 3 Human Naboo 5 #> 4 Gungan Naboo 3 #> 5 Human Alderaan 3 #> 6 <NA> <NA> 2 #> 7 <NA> Naboo 2 #> 8 Droid <NA> 2 #> 9 Droid Tatooine 2 #> 10 Human Corellia 2 #> # … with 48 more rows
species %>% count(species, sort = TRUE)
#> # A tibble: 38 x 2 #> species n #> <chr> <int> #> 1 Human 16 #> 2 <NA> 3 #> 3 Droid 3 #> 4 Zabrak 2 #> 5 Aleena 1 #> 6 Besalisk 1 #> 7 Cerean 1 #> 8 Chagrian 1 #> 9 Clawdite 1 #> 10 Dug 1 #> # … with 28 more rows
# Change the name of the newly created column: species <- starwars %>% count(species, homeworld, sort = TRUE, name = "n_species_by_homeworld") species
#> # A tibble: 58 x 3 #> species homeworld n_species_by_homeworld #> <chr> <chr> <int> #> 1 Human Tatooine 8 #> 2 Human <NA> 5 #> 3 Human Naboo 5 #> 4 Gungan Naboo 3 #> 5 Human Alderaan 3 #> 6 <NA> <NA> 2 #> 7 <NA> Naboo 2 #> 8 Droid <NA> 2 #> 9 Droid Tatooine 2 #> 10 Human Corellia 2 #> # … with 48 more rows
species %>% count(species, sort = TRUE, name = "n_species")
#> # A tibble: 38 x 2 #> species n_species #> <chr> <int> #> 1 Human 16 #> 2 <NA> 3 #> 3 Droid 3 #> 4 Zabrak 2 #> 5 Aleena 1 #> 6 Besalisk 1 #> 7 Cerean 1 #> 8 Chagrian 1 #> 9 Clawdite 1 #> 10 Dug 1 #> # … with 28 more rows
# add_count() is useful for groupwise filtering # e.g.: show details for species that have a single member starwars %>% add_count(species) %>% filter(n == 1)
#> # A tibble: 29 x 14 #> name height mass hair_color skin_color eye_color birth_year gender #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> #> 1 Gree… 173 74 <NA> green black 44 male #> 2 Jabb… 175 1358 <NA> green-tan… orange 600 herma… #> 3 Yoda 66 17 white green brown 896 male #> 4 Bossk 190 113 none green red 53 male #> 5 Ackb… 180 83 none brown mot… orange 41 male #> 6 Wick… 88 20 brown brown brown 8 male #> 7 Nien… 160 68 none grey black NA male #> 8 Nute… 191 90 none mottled g… red NA male #> 9 Watto 137 NA black blue, grey yellow NA male #> 10 Sebu… 112 40 none grey, red orange NA male #> # … with 19 more rows, and 6 more variables: homeworld <chr>, species <chr>, #> # films <list>, vehicles <list>, starships <list>, n <int>