Determine the groups within a data.frame to perform operations on. ungroup() removes the grouping levels.
Usage
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))
ungroup(x, ...)Arguments
- .data
data.frame. The data to group.- ...
One or more unquoted column names to group/ungroup the data by.
- .add
logical(1). WhenFALSE(the default)group_by()will override existing groups. To add to existing groups, use.add = TRUE.- .drop
logical(1). Drop groups formed by factor levels that don't appear in the data? The default isTRUEexcept when.datahas been previously grouped with.drop = FALSE. Seegroup_by_drop_default()for details.- x
A
data.frame.
Value
When using group_by(), a data.frame, grouped by the grouping variables.
When using ungroup(), a data.frame.
Examples
group_by(mtcars, am, cyl)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
ungroup(mutate(group_by(mtcars, am, cyl), sumMpg = sum(mpg)))
#> mpg cyl disp hp drat wt qsec vs am gear carb sumMpg
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 61.7
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 61.7
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 224.6
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 76.5
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 180.6
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 76.5
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 180.6
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 68.7
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 68.7
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 76.5
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 76.5
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 180.6
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 180.6
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 180.6
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 180.6
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 180.6
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 180.6
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 224.6
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 224.6
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 224.6
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 68.7
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 180.6
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 180.6
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 180.6
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 180.6
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 224.6
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 224.6
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 224.6
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 30.8
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 61.7
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 30.8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 224.6
mtcars %>%
group_by(am, cyl) %>%
mutate(sumMpg = sum(mpg)) %>%
ungroup()
#> mpg cyl disp hp drat wt qsec vs am gear carb sumMpg
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 61.7
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 61.7
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 224.6
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 76.5
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 180.6
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 76.5
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 180.6
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 68.7
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 68.7
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 76.5
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 76.5
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 180.6
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 180.6
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 180.6
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 180.6
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 180.6
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 180.6
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 224.6
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 224.6
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 224.6
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 68.7
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 180.6
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 180.6
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 180.6
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 180.6
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 224.6
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 224.6
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 224.6
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 30.8
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 61.7
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 30.8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 224.6
mtcars %>%
group_by(carb) %>%
filter(any(gear == 5))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# You can group by expressions: this is just short-hand for
# a mutate() followed by a group_by()
mtcars %>% group_by(vsam = vs + am)
#> mpg cyl disp hp drat wt qsec vs am gear carb vsam
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 2
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 0
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 0
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 0
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 0
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 0
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 0
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 0
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 2
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 0
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 0
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 0
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 0
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 2
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 1
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 2