This function completes the subsetting, transforming and ordering triad
with a function that works in a similar way to subset and
transform but for reordering a data frame by its columns.
This saves a lot of typing!
arrange(df, ...)
| df | data frame to reorder |
|---|---|
| ... | expressions evaluated in the context of |
order for sorting function in the base package
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 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 #> 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 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 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 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 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 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4# Same result using arrange: no need to use with(), as the context is implicit # NOTE: plyr functions do NOT preserve row.names arrange(mtcars, cyl, disp)#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 3 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 4 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 5 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 6 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 7 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 8 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 9 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> 10 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 11 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 12 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 13 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 14 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 15 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 16 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 17 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 18 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 19 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 20 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 21 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 22 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 24 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 25 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 26 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 27 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 28 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 29 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 30 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 31 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 32 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4# Let's keep the row.names in this example myCars = cbind(vehicle=row.names(mtcars), mtcars) arrange(myCars, cyl, disp)#> vehicle mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 2 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 3 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 4 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 5 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 6 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 7 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 8 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 9 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> 10 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 11 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 12 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 13 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 14 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 15 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 16 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 17 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 18 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 19 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 20 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 21 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 22 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 24 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 25 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 26 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 27 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 28 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 29 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 30 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 31 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 32 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4#> vehicle mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 3 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> 4 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 5 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 6 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 7 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 8 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 9 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 10 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 11 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 12 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 13 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 14 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 15 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 16 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 17 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 18 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 19 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 20 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 21 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 22 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 23 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 24 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 25 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 26 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 27 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 28 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 29 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 30 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 31 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 32 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3