Toggle row names between explicit and implicit.

Source: R/name-rows.r

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)

Arguments

df

a data.frame, with either rownames, or a column called .rownames.

Examples

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        10
#> 2   2         1
#> 3   3         3
#> 4   4         7
#> 5   5         4
#> 6   6         8
#> 7   7         6
#> 8   8         9
#> 9   9         2
#> 10 10         5
name_rows(arrange(name_rows(df), a))
#>     a
#> 10  1
#> 1   2
#> 3   3
#> 7   4
#> 4   5
#> 8   6
#> 6   7
#> 9   8
#> 2   9
#> 5  10