Label Encoder functionality in R?

Question

In python, scikit has a great function called LabelEncoder that maps categorical levels (strings) to integer representation.

Is there anything in R to do this? For example if there is a variable called color with values {'Blue','Red','Green'} the encoder would translate:

Blue => 1
Green => 2
Red => 3

and create an object with this mapping to then use for transforming new data in a similar fashion.

Add: It doesn't seem like just factors will work because there is no persisting of the mapping. If the new data has an unseen level from the training data, the entire structure changes. Ideally I would like the new levels labeled missing or 'other' somehow.

sample_dat <- data.frame(a_str=c('Red','Blue','Blue','Red','Green'))
sample_dat$a_int<-as.integer(as.factor(sample_dat$a_str))
sample_dat$a_int
#[1] 3 1 1 3 2
sample_dat2 <- data.frame(a_str=c('Red','Blue','Blue','Red','Green','Azure'))
sample_dat2$a_int<-as.integer(as.factor(sample_dat2$a_str))
sample_dat2$a_int
# [1] 4 2 2 4 3 1

Answer 1

Create your vector of data:

colors <- c("red", "red", "blue", "green")

Create a factor:

factors <- factor(colors)

Convert the factor to numbers:

as.numeric(factors)

Output: (note that this is in alphabetical order)

# [1] 3 3 1 2

You can also set a custom numbering system: (note that the output now follows the "rainbow color order" that I defined)

rainbow <- c("red","orange","yellow","green","blue","purple")
ordered <- factor(colors, levels = rainbow)
as.numeric(ordered)
# [1] 1 1 5 4

See ?factor.

Answer 2

Try CatEncoders package. It replicates the Python sklearn.preprocessing functionality.

# variable to encode values
colors = c("red", "red", "blue", "green")
lab_enc = LabelEncoder.fit(colors)

# new values are transformed to NA
values = transform(lab_enc, c('red', 'red', 'yellow'))
values

# [1]  3  3 NA


# doing the inverse: given the encoded numbers return the labels
inverse.transform(lab_enc, values)
# [1] "red" "red" NA   

I would add the functionality of reporting the non-matching labels with a warning.

PS: It also has the OneHotEncoder function.

Answer 3

If I correctly understand what do you want:

# function which returns function which will encode vectors with values  of 'vec' 
label_encoder = function(vec){
    levels = sort(unique(vec))
    function(x){
        match(x, levels)
    }
}

colors = c("red", "red", "blue", "green")

color_encoder = label_encoder(colors) # create encoder

encoded_colors = color_encoder(colors) # encode colors
encoded_colors

new_colors = c("blue", "green", "green")  # new vector
encoded_new_colors = color_encoder(new_colors)
encoded_new_colors

other_colors = c("blue", "green", "green", "yellow") 
color_encoder(other_colors) # NA's are introduced

# save and restore to disk
saveRDS(color_encoder, "color_encoder.RDS")
c_encoder = readRDS("color_encoder.RDS")
c_encoder(colors) # same result

# dealing with multiple columns

# create data.frame
set.seed(123) # make result reproducible
color_dataframe = as.data.frame(
    matrix(
        sample(c("red", "blue", "green",  "yellow"), 12, replace = TRUE),
        ncol = 3)
)
color_dataframe

# encode each column
for (column in colnames(color_dataframe)){
    color_dataframe[[column]] = color_encoder(color_dataframe[[column]])
}
color_dataframe