Besides using the option as suggested by Matt Dowle, another way of changing the column classes is as follows:
dat[, (cols) := lapply(.SD, factor), .SDcols = cols]
By using the := operator you update the datatable by reference. A check whether this worked:
> sapply(dat,class)
ID Quarter value
"factor" "factor" "numeric"
As suggeted by @MattDowle in the comments, you can also use a combination of for(...) set(...) as follows:
for (col in cols) set(dat, j = col, value = factor(dat[[col]]))
which will give the same result. A third alternative is:
for (col in cols) dat[, (col) := factor(dat[[col]])]
On a smaller datasets, the for(...) set(...) option is about three times faster than the lapply option (but that doesn’t really matter, because it is a small dataset). On larger datasets (e.g. 2 million rows), each of these approaches takes about the same amount of time. For testing on a larger dataset, I used:
dat <- data.table(ID=c(rep("A", 1e6), rep("B",1e6)),
Quarter=c(1:1e6, 1:1e6),
value=rnorm(10))
Sometimes, you will have to do it a bit differently (for example when numeric values are stored as a factor). Then you have to use something like this:
dat[, (cols) := lapply(.SD, function(x) as.integer(as.character(x))), .SDcols = cols]
WARNING: The following explanation is not the data.table-way of doing things. The datatable is not updated by reference because a copy is made and stored in memory (as pointed out by @Frank), which increases memory usage. It is more an addition in order to explain the working of with = FALSE.
When you want to change the column classes the same way as you would do with a dataframe, you have to add with = FALSE as follows:
dat[, cols] <- lapply(dat[, cols, with = FALSE], factor)
A check whether this worked:
> sapply(dat,class)
ID Quarter value
"factor" "factor" "numeric"
If you don’t add with = FALSE, datatable will evaluate dat[, cols] as a vector. Check the difference in output between dat[, cols] and dat[, cols, with = FALSE]:
> dat[, cols]
[1] "ID" "Quarter"
> dat[, cols, with = FALSE]
ID Quarter
1: A 1
2: A 2
3: A 3
4: A 4
5: A 5
6: B 1
7: B 2
8: B 3
9: B 4
10: B 5