Recode categorical factor with N categories into N binary columns
There is a function in caret's package that does what you require, dummyVars. Here is the example of it's usage taken from the authors documentation: http://topepo.github.io/caret/preprocess.html
library(earth)
data(etitanic)
dummies <- caret::dummyVars(survived ~ ., data = etitanic)
head(predict(dummies, newdata = etitanic))
pclass.1st pclass.2nd pclass.3rd sex.female sex.male age sibsp parch
1 1 0 0 1 0 29.0000 0 0
2 1 0 0 0 1 0.9167 1 2
3 1 0 0 1 0 2.0000 1 2
4 1 0 0 0 1 30.0000 1 2
5 1 0 0 1 0 25.0000 1 2
6 1 0 0 0 1 48.0000 0 0
The model.matrix options could be useful in case you had sparse data and wanted to use Matrix::sparse.model.matrix
Even better with the help of @AnandaMahto's search capabilities,
model.matrix(~ . + 0, data=df, contrasts.arg = lapply(df, contrasts, contrasts=FALSE))
# v1a v1b v1c v2a v2b v2c
# 1 0 1 0 0 0 1
# 2 1 0 0 1 0 0
# 3 0 0 1 0 0 1
# 4 0 1 0 1 0 0
# 5 0 0 1 0 0 1
# 6 0 0 1 0 1 0
# 7 1 0 0 1 0 0
# 8 1 0 0 0 1 0
# 9 1 0 0 0 0 1
# 10 1 0 0 0 1 0
I think this is what you're looking for. I'd be happy to delete if it's not so. Thanks to @G.Grothendieck (once again) for the excellent usage of model.matrix!
cbind(with(df, model.matrix(~ v1 + 0)), with(df, model.matrix(~ v2 + 0)))
# v1a v1b v1c v2a v2b v2c
# 1 0 1 0 0 0 1
# 2 1 0 0 1 0 0
# 3 0 0 1 0 0 1
# 4 0 1 0 1 0 0
# 5 0 0 1 0 0 1
# 6 0 0 1 0 1 0
# 7 1 0 0 1 0 0
# 8 1 0 0 0 1 0
# 9 1 0 0 0 0 1
# 10 1 0 0 0 1 0
Note: Your output is just:
with(df, model.matrix(~ v2 + 0))
Note 2: This gives a matrix. Fairly obvious, but still, wrap it with as.data.frame(.) if you want a data.frame.