Plyr functions ignore row names, so this function provides a way to preserve
them by converting them to an explicit column in the data frame. After the
plyr operation, you can then apply name_rows again to convert back
from the explicit column to the implicit rownames.
name_rows(df)name_rows(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb .rownames
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Maserati Bora
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 Volvo 142E
name_rows(name_rows(mtcars))
#> 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
df <- data.frame(a = sample(10))
arrange(df, a)
#> a
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
#> 6 6
#> 7 7
#> 8 8
#> 9 9
#> 10 10
arrange(name_rows(df), a)
#> a .rownames
#> 1 1 4
#> 2 2 9
#> 3 3 1
#> 4 4 3
#> 5 5 8
#> 6 6 10
#> 7 7 5
#> 8 8 2
#> 9 9 6
#> 10 10 7
name_rows(arrange(name_rows(df), a))
#> a
#> 4 1
#> 9 2
#> 1 3
#> 3 4
#> 8 5
#> 10 6
#> 5 7
#> 2 8
#> 6 9
#> 7 10