How to programmatically create binary columns based on a categorical variable in data.table?

Question

I have a big (12 million rows) data.table which looks like this:

library(data.table)
set.seed(123)
dt <- data.table(id=rep(1:3, each=5),y=sample(letters[1:5],15,replace = T))
> dt
    id y
 1:  1 b
 2:  1 d
 3:  1 c
 4:  1 e
 5:  1 e
 6:  2 a
 7:  2 c
 8:  2 e
 9:  2 c
10:  2 c
11:  3 e
12:  3 c
13:  3 d
14:  3 c
15:  3 a

I want to create a new data.table containing my variable id (which will be the unique key of this new data.table) and 5 other binary variables each one corresponding to each category of y which take value 1 if the id has that value for y, 0 otherwise.
The output data.table should look like this:

   id a b c d e
1:  1 0 1 1 1 1
2:  2 1 0 1 0 1
3:  3 1 0 1 1 1

I tried doing this in a loop but it's quite slow and also I don't know how to pass the binary variable names programmatically, as they depend on the variable I'm trying to "split".

EDIT: as @mtoto pointed out, a similar question has already been asked and answered here, but the solution is using the reshape2 package.
I was wondering if there's another (faster) way to do so by maybe using the := operator in data.table, as I have a massive dataset and I'm working quite a lot with this package.

EDIT2: benchmark of the functions in @Arun's post on my data (~12 million rows, ~3,5 million different ids and 490 different labels for the y variable (resulting in 490 dummy variables)):

system.time(ans1 <- AnsFunction())   # 194s
system.time(ans2 <- dcastFunction()) # 55s
system.time(ans3 <- TableFunction()) # Takes forever and blocked my PC

Answer 1

data.table has its own dcast implementation using data.table's internals and should be fast. Give this a try:

dcast(dt, id ~ y, fun.aggregate = function(x) 1L, fill=0L)
#    id a b c d e
# 1:  1 0 1 1 1 1
# 2:  2 1 0 1 0 1
# 3:  3 1 0 1 1 1

---

Just thought of another way to handle this by preallocating and updating by reference (perhaps dcast's logic should be done like this to avoid intermediates).

ans = data.table(id = unique(dt$id))[, unique(dt$y) := 0L][]

All that's left is to fill existing combinations with 1L.

dt[, {set(ans, i=.GRP, j=unique(y), value=1L); NULL}, by=id]
ans
#    id b d c e a
# 1:  1 1 1 1 1 0
# 2:  2 0 0 1 1 1
# 3:  3 0 1 1 1 1

---

Okay, I've gone ahead on benchmarked on OP's data dimensions with ~10 million rows and 10 columns.

require(data.table)
set.seed(45L)
y = apply(matrix(sample(letters, 10L*20L, TRUE), ncol=20L), 1L, paste, collapse="")
dt = data.table(id=sample(1e5,1e7,TRUE), y=sample(y,1e7,TRUE))

system.time(ans1 <- AnsFunction())   # 2.3s
system.time(ans2 <- dcastFunction()) # 2.2s
system.time(ans3 <- TableFunction()) # 6.2s

setcolorder(ans1, names(ans2))
setcolorder(ans3, names(ans2))
setorder(ans1, id)
setkey(ans2, NULL)
setorder(ans3, id)

identical(ans1, ans2) # TRUE
identical(ans1, ans3) # TRUE

where,

AnsFunction <- function() {
    ans = data.table(id = unique(dt$id))[, unique(dt$y) := 0L][]
    dt[, {set(ans, i=.GRP, j=unique(y), value=1L); NULL}, by=id]
    ans
    # reorder columns outside
}

dcastFunction <- function() {
    # no need to load reshape2. data.table has its own dcast as well
    # no need for setDT
    df <- dcast(dt, id ~ y, fun.aggregate = function(x) 1L, fill=0L,value.var = "y")
}

TableFunction <- function() {
    # need to return integer results for identical results
    # fixed 1 -> 1L; as.numeric -> as.integer
    df <- as.data.frame.matrix(table(dt$id, dt$y))
    df[df > 1L] <- 1L
    df <- cbind(id = as.integer(row.names(df)), df)
    setDT(df)
}