Reputation: 43255
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 that indicate the presence of each number.
I can do this like this:
new.names <- sort(unique(DT$x))
DT[, paste0('col', new.names) := lapply(new.names, function(i) DT$x==i), with=FALSE]
But this uses a pesky lapply
which is probably slower than the data.table alternative and this solutions strikes me as not very "data.table-ish".
Is there a better and/or faster way to create these new columns?
Upvotes: 7
Views: 1577
Reputation: 1
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)
Upvotes: 0
Reputation: 115392
There is also nnet::class.ind
library(nnet)
cbind(DT, setnames(as.data.table(DT[, class.ind(x)]),paste0('col', unique(DT$x))))
Upvotes: 2
Reputation: 19454
How about model.matrix
?
model.matrix(~factor(x)-1,data=DT)
factor(x)1 factor(x)2 factor(x)3 factor(x)4 factor(x)5
1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 1 0
5 0 0 0 0 1
6 1 0 0 0 0
7 0 1 0 0 0
8 0 0 1 0 0
9 0 0 0 1 0
10 0 0 0 0 1
attr(,"assign")
[1] 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`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)]
Upvotes: 9