group_split() works like base::split() but
it uses the grouping structure from
group_by()and is therefore subject to the data maskit does not name the elements of the list based on the grouping as this typically loses information and is confusing
Arguments
- .data
A
data.frame.- ...
Grouping specification, forwarded to
group_by().- .keep
logical(1). Should the grouping columns be kept (default: TRUE)?
Value
group_split()returns a list ofdata.frames. Eachdata.framecontains the rows of.datawith the associated group and all the columns, including the grouping variables.group_keys()returns adata.framewith one row per group, and one column per grouping variable
Details
Grouped data.frames:
The primary use case for group_split() is with already grouped data.frames, typically a result of group_by().
In this case, group_split() only uses the first argument, the grouped data.frame, and warns when ... is used.
Because some of these groups may be empty, it is best paired with group_keys() which identifies the representatives
of each grouping variable for the group.
Ungrouped data.frames:
When used on ungrouped data.frames, group_split() forwards the ... to group_by() before the split, therefore
the ... are subject to the data mask.
Examples
# Grouped data.frames:
mtcars %>% group_by(cyl, am) %>% group_split()
#> [[1]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 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
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#>
#> [[2]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 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
#>
#> [[3]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 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 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
#> 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
#>
#> [[4]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 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
#> 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
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#>
#> [[5]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Ferrari Dino 19.7 6 145 175 3.62 2.770 15.50 0 1 5 6
#>
#> [[6]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> Maserati Bora 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8
#>
mtcars %>% group_by(cyl, am) %>% group_split(.keep = FALSE)
#> [[1]]
#> mpg disp hp drat wt qsec vs gear carb
#> Merc 240D 24.4 146.7 62 3.69 3.190 20.00 1 4 2
#> Merc 230 22.8 140.8 95 3.92 3.150 22.90 1 4 2
#> Toyota Corona 21.5 120.1 97 3.70 2.465 20.01 1 3 1
#>
#> [[2]]
#> mpg disp hp drat wt qsec vs gear carb
#> Hornet 4 Drive 21.4 258.0 110 3.08 3.215 19.44 1 3 1
#> Valiant 18.1 225.0 105 2.76 3.460 20.22 1 3 1
#> Merc 280 19.2 167.6 123 3.92 3.440 18.30 1 4 4
#> Merc 280C 17.8 167.6 123 3.92 3.440 18.90 1 4 4
#>
#> [[3]]
#> mpg disp hp drat wt qsec vs gear carb
#> Hornet Sportabout 18.7 360.0 175 3.15 3.440 17.02 0 3 2
#> Duster 360 14.3 360.0 245 3.21 3.570 15.84 0 3 4
#> Merc 450SE 16.4 275.8 180 3.07 4.070 17.40 0 3 3
#> Merc 450SL 17.3 275.8 180 3.07 3.730 17.60 0 3 3
#> Merc 450SLC 15.2 275.8 180 3.07 3.780 18.00 0 3 3
#> Cadillac Fleetwood 10.4 472.0 205 2.93 5.250 17.98 0 3 4
#> Lincoln Continental 10.4 460.0 215 3.00 5.424 17.82 0 3 4
#> Chrysler Imperial 14.7 440.0 230 3.23 5.345 17.42 0 3 4
#> Dodge Challenger 15.5 318.0 150 2.76 3.520 16.87 0 3 2
#> AMC Javelin 15.2 304.0 150 3.15 3.435 17.30 0 3 2
#> Camaro Z28 13.3 350.0 245 3.73 3.840 15.41 0 3 4
#> Pontiac Firebird 19.2 400.0 175 3.08 3.845 17.05 0 3 2
#>
#> [[4]]
#> mpg disp hp drat wt qsec vs gear carb
#> Datsun 710 22.8 108.0 93 3.85 2.320 18.61 1 4 1
#> Fiat 128 32.4 78.7 66 4.08 2.200 19.47 1 4 1
#> Honda Civic 30.4 75.7 52 4.93 1.615 18.52 1 4 2
#> Toyota Corolla 33.9 71.1 65 4.22 1.835 19.90 1 4 1
#> Fiat X1-9 27.3 79.0 66 4.08 1.935 18.90 1 4 1
#> Porsche 914-2 26.0 120.3 91 4.43 2.140 16.70 0 5 2
#> Lotus Europa 30.4 95.1 113 3.77 1.513 16.90 1 5 2
#> Volvo 142E 21.4 121.0 109 4.11 2.780 18.60 1 4 2
#>
#> [[5]]
#> mpg disp hp drat wt qsec vs gear carb
#> Mazda RX4 21.0 160 110 3.90 2.620 16.46 0 4 4
#> Mazda RX4 Wag 21.0 160 110 3.90 2.875 17.02 0 4 4
#> Ferrari Dino 19.7 145 175 3.62 2.770 15.50 0 5 6
#>
#> [[6]]
#> mpg disp hp drat wt qsec vs gear carb
#> Ford Pantera L 15.8 351 264 4.22 3.17 14.5 0 5 4
#> Maserati Bora 15.0 301 335 3.54 3.57 14.6 0 5 8
#>
mtcars %>% group_by(cyl, am) %>% group_keys()
#> cyl am
#> 1 4 0
#> 2 4 1
#> 3 6 0
#> 4 6 1
#> 5 8 0
#> 6 8 1
# Ungrouped data.frames:
mtcars %>% group_split(am, cyl)
#> [[1]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 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
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#>
#> [[2]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 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
#> 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
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#>
#> [[3]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 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
#>
#> [[4]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Ferrari Dino 19.7 6 145 175 3.62 2.770 15.50 0 1 5 6
#>
#> [[5]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 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 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
#> 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
#>
#> [[6]]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> Maserati Bora 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8
#>