It uses 'tidyeval' and 'dplyr' to create dummy variables based for categorical variables.
add_dummy_variables(
df,
x,
values = c(),
auto_values = FALSE,
remove_original = TRUE
)A Local or remote data frame
Categorical variable
Possible known values of the categorical variable. If not passed then the function will take an additional step to figure the unique values of the variable.
Safeguard argument to prevent the function from figuring the unique values if the values argument is empty. If it is ok for this function to obtain the unique values, set to TRUE. Defaults to FALSE.
It removes the original variable from the returned table. Defaults to TRUE.
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
mtcars %>%
add_dummy_variables(cyl, values = c(4, 6, 8))
#> mpg disp hp drat wt qsec vs am gear carb cyl_6 cyl_8
#> Mazda RX4 21.0 160.0 110 3.90 2.620 16.46 0 1 4 4 1 0
#> Mazda RX4 Wag 21.0 160.0 110 3.90 2.875 17.02 0 1 4 4 1 0
#> Datsun 710 22.8 108.0 93 3.85 2.320 18.61 1 1 4 1 0 0
#> Hornet 4 Drive 21.4 258.0 110 3.08 3.215 19.44 1 0 3 1 1 0
#> Hornet Sportabout 18.7 360.0 175 3.15 3.440 17.02 0 0 3 2 0 1
#> Valiant 18.1 225.0 105 2.76 3.460 20.22 1 0 3 1 1 0
#> Duster 360 14.3 360.0 245 3.21 3.570 15.84 0 0 3 4 0 1
#> Merc 240D 24.4 146.7 62 3.69 3.190 20.00 1 0 4 2 0 0
#> Merc 230 22.8 140.8 95 3.92 3.150 22.90 1 0 4 2 0 0
#> Merc 280 19.2 167.6 123 3.92 3.440 18.30 1 0 4 4 1 0
#> Merc 280C 17.8 167.6 123 3.92 3.440 18.90 1 0 4 4 1 0
#> Merc 450SE 16.4 275.8 180 3.07 4.070 17.40 0 0 3 3 0 1
#> Merc 450SL 17.3 275.8 180 3.07 3.730 17.60 0 0 3 3 0 1
#> Merc 450SLC 15.2 275.8 180 3.07 3.780 18.00 0 0 3 3 0 1
#> Cadillac Fleetwood 10.4 472.0 205 2.93 5.250 17.98 0 0 3 4 0 1
#> Lincoln Continental 10.4 460.0 215 3.00 5.424 17.82 0 0 3 4 0 1
#> Chrysler Imperial 14.7 440.0 230 3.23 5.345 17.42 0 0 3 4 0 1
#> Fiat 128 32.4 78.7 66 4.08 2.200 19.47 1 1 4 1 0 0
#> Honda Civic 30.4 75.7 52 4.93 1.615 18.52 1 1 4 2 0 0
#> Toyota Corolla 33.9 71.1 65 4.22 1.835 19.90 1 1 4 1 0 0
#> Toyota Corona 21.5 120.1 97 3.70 2.465 20.01 1 0 3 1 0 0
#> Dodge Challenger 15.5 318.0 150 2.76 3.520 16.87 0 0 3 2 0 1
#> AMC Javelin 15.2 304.0 150 3.15 3.435 17.30 0 0 3 2 0 1
#> Camaro Z28 13.3 350.0 245 3.73 3.840 15.41 0 0 3 4 0 1
#> Pontiac Firebird 19.2 400.0 175 3.08 3.845 17.05 0 0 3 2 0 1
#> Fiat X1-9 27.3 79.0 66 4.08 1.935 18.90 1 1 4 1 0 0
#> Porsche 914-2 26.0 120.3 91 4.43 2.140 16.70 0 1 5 2 0 0
#> Lotus Europa 30.4 95.1 113 3.77 1.513 16.90 1 1 5 2 0 0
#> Ford Pantera L 15.8 351.0 264 4.22 3.170 14.50 0 1 5 4 0 1
#> Ferrari Dino 19.7 145.0 175 3.62 2.770 15.50 0 1 5 6 1 0
#> Maserati Bora 15.0 301.0 335 3.54 3.570 14.60 0 1 5 8 0 1
#> Volvo 142E 21.4 121.0 109 4.11 2.780 18.60 1 1 4 2 0 0
mtcars %>%
add_dummy_variables(cyl, auto_values = TRUE)
#> mpg disp hp drat wt qsec vs am gear carb cyl_6 cyl_8
#> Mazda RX4 21.0 160.0 110 3.90 2.620 16.46 0 1 4 4 1 0
#> Mazda RX4 Wag 21.0 160.0 110 3.90 2.875 17.02 0 1 4 4 1 0
#> Datsun 710 22.8 108.0 93 3.85 2.320 18.61 1 1 4 1 0 0
#> Hornet 4 Drive 21.4 258.0 110 3.08 3.215 19.44 1 0 3 1 1 0
#> Hornet Sportabout 18.7 360.0 175 3.15 3.440 17.02 0 0 3 2 0 1
#> Valiant 18.1 225.0 105 2.76 3.460 20.22 1 0 3 1 1 0
#> Duster 360 14.3 360.0 245 3.21 3.570 15.84 0 0 3 4 0 1
#> Merc 240D 24.4 146.7 62 3.69 3.190 20.00 1 0 4 2 0 0
#> Merc 230 22.8 140.8 95 3.92 3.150 22.90 1 0 4 2 0 0
#> Merc 280 19.2 167.6 123 3.92 3.440 18.30 1 0 4 4 1 0
#> Merc 280C 17.8 167.6 123 3.92 3.440 18.90 1 0 4 4 1 0
#> Merc 450SE 16.4 275.8 180 3.07 4.070 17.40 0 0 3 3 0 1
#> Merc 450SL 17.3 275.8 180 3.07 3.730 17.60 0 0 3 3 0 1
#> Merc 450SLC 15.2 275.8 180 3.07 3.780 18.00 0 0 3 3 0 1
#> Cadillac Fleetwood 10.4 472.0 205 2.93 5.250 17.98 0 0 3 4 0 1
#> Lincoln Continental 10.4 460.0 215 3.00 5.424 17.82 0 0 3 4 0 1
#> Chrysler Imperial 14.7 440.0 230 3.23 5.345 17.42 0 0 3 4 0 1
#> Fiat 128 32.4 78.7 66 4.08 2.200 19.47 1 1 4 1 0 0
#> Honda Civic 30.4 75.7 52 4.93 1.615 18.52 1 1 4 2 0 0
#> Toyota Corolla 33.9 71.1 65 4.22 1.835 19.90 1 1 4 1 0 0
#> Toyota Corona 21.5 120.1 97 3.70 2.465 20.01 1 0 3 1 0 0
#> Dodge Challenger 15.5 318.0 150 2.76 3.520 16.87 0 0 3 2 0 1
#> AMC Javelin 15.2 304.0 150 3.15 3.435 17.30 0 0 3 2 0 1
#> Camaro Z28 13.3 350.0 245 3.73 3.840 15.41 0 0 3 4 0 1
#> Pontiac Firebird 19.2 400.0 175 3.08 3.845 17.05 0 0 3 2 0 1
#> Fiat X1-9 27.3 79.0 66 4.08 1.935 18.90 1 1 4 1 0 0
#> Porsche 914-2 26.0 120.3 91 4.43 2.140 16.70 0 1 5 2 0 0
#> Lotus Europa 30.4 95.1 113 3.77 1.513 16.90 1 1 5 2 0 0
#> Ford Pantera L 15.8 351.0 264 4.22 3.170 14.50 0 1 5 4 0 1
#> Ferrari Dino 19.7 145.0 175 3.62 2.770 15.50 0 1 5 6 1 0
#> Maserati Bora 15.0 301.0 335 3.54 3.570 14.60 0 1 5 8 0 1
#> Volvo 142E 21.4 121.0 109 4.11 2.780 18.60 1 1 4 2 0 0