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.
Usage
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 columnnexists, it will automatically be used as a weighting variable, although you will have to specifynameto provide a new name for the output.- sort
logical(1). IfTRUE, 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 ton. If there's already a column calledn, it will error, and require you to specify the name.
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