Given data that looks like:
library(data.table)
DT <- data.table(x=rep(1:5, 2))
I would like to split this data into 5 boolean columns t
library(data.table)
DT <- data.table(x=rep(1:5, 2))
# add column with id
DT[, id := seq.int(nrow(DT))]
# cast long table into wide
DT.wide <- dcast(DT, id ~ x, value.var = "x", fill = 0, fun = function(x) 1)
How about model.matrix
?
model.matrix(~factor(x)-1,data=DT)
factor(x)1 factor(x)2 factor(x)3 factor(x)4 factor(x)5
1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 1 0
5 0 0 0 0 1
6 1 0 0 0 0
7 0 1 0 0 0
8 0 0 1 0 0
9 0 0 0 1 0
10 0 0 0 0 1
attr(,"assign")
[1] 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`factor(x)`
[1] "contr.treatment"
Apparently, you can put model.matrix
into [.data.table
to give the same results. Not sure if it would be faster:
DT[,model.matrix(~factor(x)-1)]
There is also nnet::class.ind
library(nnet)
cbind(DT, setnames(as.data.table(DT[, class.ind(x)]),paste0('col', unique(DT$x))))