Create an ElementaryTable from a data.frame
Details
If row names are not defined in df (or they are simple numbers), then the row names are taken from the column
label_name, if it exists. If label_name exists, then it is also removed from the original data. This behavior
is compatible with as_result_df(), when as_is = TRUE and the row names are not unique.
See also
as_result_df() for the inverse operation.
Examples
df_to_tt(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> ---------------------------------------------------------------------------------------------
#> Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4
#> Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
#> Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.2 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 19.9 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2
#> Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1
#> Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
#> Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2