One-hot encoding in R: three simple methods

By Data Tricks, 3 July 2019

Cleaning and preparing data is one of the most effective ways of boosting the accuracy of predictions through machine learning. If you’re working with categorical variables, you’ll probably want to recode them to a format more friendly to machine learning algorithms.

What is one-hot encoding?

One-hot encoding is the process of converting a categorical variable with multiple categories into multiple variables, each with a value of 1 or 0.

For example, it involves taking this:


and converting it into this:


For the methods outlined below, the following simple dataframe will be required:

data <- data.frame(
Outcome = seq(1,100,by=1),
Variable = sample(c("Red","Green","Blue"), 100, replace = TRUE)

Method 1: one_hot in mltools package


newdata <- one_hot(

Method 2: dummyVars in caret package


dummy <- dummyVars(" ~ .", data=data)
newdata <- data.frame(predict(dummy, newdata = data)) 

Method 3: dcast in reshape2 package


newdata <- dcast(data = data, Outcome ~ Variable, length)

Applying one-hot encoding to multiple variables at the same time?

For the following examples, we’ll modify the dataframe to introduce another variable:

data <- data.frame(ID = seq(1,100,by=1),
  Colour = sample(c("Red","Green","Blue"), 100, replace = TRUE),
  Quality = sample(c("Poor","Average","Good"), 100, replace = TRUE)

If you’re using the one_hot function in the mltools package:

newdata <- one_hot(

For the dummyVars function in the caret package:

dummy <- dummyVars(" ~ .", data=data)
newdata <- data.frame(predict(dummy, newdata = data))

For the dcast function in the reshape2 package:

newdata <- dcast(data = melt(data, id.vars = "ID"), ID ~ variable + value, length)

Note that we have to melt the data before we cast it.

I hope you found this quick tutorial helpful. Happy encoding!

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4 thoughts on “One-hot encoding in R: three simple methods”

  1. Abzetdin Adamov says:

    In the data frame generation code the factor function is missing. It should be
    Variable = factor(sample(c(“Red”,”Green”,”Blue”), 100, replace = TRUE))

  2. SG says:

    Yes, indeed, it is necessary to convert columns to factor for Method 1.

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