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?

Answer 1

Here is an easy and neat solution:

From the superml package: https://www.rdocumentation.org/packages/superml/versions/0.5.3 There is a LabelEncoder class: https://www.rdocumentation.org/packages/superml/versions/0.5.3/topics/LabelEncoder

install.packages("superml")
library(superml)

lbl <- LabelEncoder$new()
lbl$fit(sample_dat$column)
sample_dat$column <- lbl$fit_transform(sample_dat$column)
decode_names <- lbl$inverse_transform(sample_dat$column)

Answer 2

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 3

It's hard to believe why no one has mentioned caret's dummyVars function.

This is a widely searched question, and people don't want to write their own methods or copy and paste other users methods, they want a package, and caret is the closest thing to sklearn in R.

EDIT: I now realize that what the user actually want's is to turn strings into a counting number, which is just as.numeric(as.factor(x)) but I'm going to leave this here because using hot-one encoding is the more accurate method of encoding categorical data.