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 from n = n() to n = sum(wt). add_count() and add_tally() are equivalent 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)

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

Arguments

x

A data.frame.

...

Variables to group by.

wt

If omitted, will count the number of rows. If specified, will perform a "weighted" count by summing the (non-missing) values of variable wt. If omitted, and column n exists, it will automatically be used as a weighting variable, although you will have to specify name to provide a new name for the output.

sort

logical(1). If TRUE, will show the largest groups at the top.

name

character(1). 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.

Value

A data.frame. count() and add_count() have the same groups as the input.

Examples

# count() is a convenient way to get a sense of the distribution of # values in a dataset mtcars %>% count(cyl)
#> cyl n #> 1 4 11 #> 2 6 7 #> 3 8 14
mtcars %>% count(cyl, sort = TRUE)
#> cyl n #> 1 8 14 #> 2 4 11 #> 3 6 7
mtcars %>% count(cyl, am, sort = TRUE)
#> `summarise()` has grouped output by 'cyl'. You can override using the `.groups` argument.
#> cyl am n #> 1 8 0 12 #> 2 4 1 8 #> 3 6 0 4 #> 4 4 0 3 #> 5 6 1 3 #> 6 8 1 2
# Note that if the data are already grouped, count() adds an additional grouping variable # which is removed afterwards mtcars %>% group_by(gear) %>% count(cyl)
#> `summarise()` has grouped output by 'gear'. You can override using the `.groups` argument.
#> gear cyl n #> 1 3 4 1 #> 2 3 6 2 #> 3 3 8 12 #> 4 4 4 8 #> 5 4 6 4 #> 6 5 4 2 #> 7 5 6 1 #> 8 5 8 2
# tally() is a lower-level function that assumes you've done the grouping mtcars %>% tally()
#> n #> 1 32
mtcars %>% group_by(cyl) %>% tally()
#> cyl n #> 1 4 11 #> 2 6 7 #> 3 8 14
# both count() and tally() have add_ variants that work like mutate() instead of summarise mtcars %>% add_count(cyl, wt = am)
#> mpg cyl disp hp drat wt qsec vs am gear carb n #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 3 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 3 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 8 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 3 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 3 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 2 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 8 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 8 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 3 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 3 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 2 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 2 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 2 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 2 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 2 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 2 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 8 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 8 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 8 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 8 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 2 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 8 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 8 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 8 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 2 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 3 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 2 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 8
mtcars %>% add_tally(wt = am)
#> mpg cyl disp hp drat wt qsec vs am gear carb n #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 13 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 13 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 13 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 13 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 13 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 13 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 13 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 13 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 13 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 13 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 13 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 13 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 13 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 13 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 13 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 13 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 13 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 13 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 13 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 13 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 13 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 13 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 13 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 13 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 13 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 13 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 13 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 13 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 13 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 13 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 13 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 13