How to One Hot Encode Several Categorical Variables in R

How to turn one-hot encoded variables to a single factor in R

Here's a solution ...

First one hot encode carb

mtcars$carb <- factor(mtcars$carb)
df <- as.data.frame(model.matrix(~ carb - 1, mtcars))
head(df)

#>                   carb1 carb2 carb3 carb4 carb6 carb8
#> Mazda RX4             0     0     0     1     0     0
#> Mazda RX4 Wag         0     0     0     1     0     0
#> Datsun 710            1     0     0     0     0     0
#> Hornet 4 Drive        1     0     0     0     0     0
#> Hornet Sportabout     0     1     0     0     0     0
#> Valiant               1     0     0     0     0     0

We could of course select out the hot encode variables

library(dplyr)

df %>% 
   rowwise() %>% 
   mutate(remade = which.max(c_across(starts_with("carb")))) %>%
   ungroup %>%
   mutate(remade = factor(remade))

#> # A tibble: 32 x 7
#>    carb1 carb2 carb3 carb4 carb6 carb8 remade
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> 
#>  1     0     0     0     1     0     0 4     
#>  2     0     0     0     1     0     0 4     
#>  3     1     0     0     0     0     0 1     
#>  4     1     0     0     0     0     0 1     
#>  5     0     1     0     0     0     0 2     
#>  6     1     0     0     0     0     0 1     
#>  7     0     0     0     1     0     0 4     
#>  8     0     1     0     0     0     0 2     
#>  9     0     1     0     0     0     0 2     
#> 10     0     0     0     1     0     0 4     
#> # … with 22 more rows

Here it is as a function with the option to keep or delete the one hot encoded columns a la @KM_83

cold_encode <- function(df, encoded_prefix, keep_dummies = FALSE) {
   var <- sym(encoded_prefix)
   df <- 
      df %>%
      rowwise() %>%
      mutate({{ var }} := which.max(c_across(starts_with(encoded_prefix)))) %>%
      ungroup %>%
      mutate({{ var }} := factor({{ var }})) 
   if (!keep_dummies) {
      df <- 
      df %>% select(-matches(paste0(encoded_prefix,1:9)))
   }
   return(df)
}

cold_encode(df, "carb")
#> # A tibble: 32 x 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear carb 
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4 4    
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4 4    
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4 1    
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3 1    
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3 2    
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3 1    
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3 4    
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4 2    
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4 2    
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4 4    
#> # … with 22 more rows

One-Hot Encoding in [R] | Categorical to Dummy Variables

dd <- read.table(text="
   RACE        AGE.BELOW.21     CLASS
   HISPANIC          0          A
   ASIAN             1          A
   HISPANIC          1          D
   CAUCASIAN         1          B",
  header=TRUE)


  with(dd,
       data.frame(model.matrix(~RACE-1,dd),
                  AGE.BELOW.21,CLASS))
 ##   RACEASIAN RACECAUCASIAN RACEHISPANIC AGE.BELOW.21 CLASS
 ## 1         0             0            1            0     A
 ## 2         1             0            0            1     A
 ## 3         0             0            1            1     D
 ## 4         0             1            0            1     B

The formula ~RACE-1 specifies that R should create dummy variables from the RACE variable, but suppress the intercept (so that each column represents whether an observation comes from a specified category); the default, without -1, is to make the first column an intercept term (all ones), omitting the dummy variable for the baseline level (first level of the factor) from the model matrix.

More generally, you might want something like

 dd0 <- subset(dd,select=-CLASS)
 data.frame(model.matrix(~.-1,dd0),CLASS=dd$CLASS)

Note that when you have multiple categorical variables you will have to something a little bit tricky if you want full sets of dummy variables for each one. I would think of cbind()ing together separate model matrices, but I think there's also some trick for doing this all at once that I forget ...

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