Pivot on data.table similar to rehape melt function

Question

I have read some references to similar problems here on SO, but haven't been able to find a solution yet and wondering if there is any way to do the following using just data.table.

I'll use a simplified example, but in practice, my data table has > 1000 columns similar to var1, var2, ... var1000, etc.

dt <- data.table(uid=c("a","b"), var1=c(1,2), var2=c(100,200))

I am looking for a solution that will allow me to get an output similar to reshape's melt function --

> melt(dt, id=c("uid"))
uid variable value
1   a     var1     1
2   b     var1     2
3   a     var2   100
4   b     var2   200

That is, all the columns except for uid are listed under a single column with the corresponding values in an adjoining column. I have tried this with a combination of list, etc, but might be missing something that is obvious.

All uids in dt are unique.

Thanks in advance.

Answer 1

For a data.table reshape, try the following:

dt[, list(variable = names(.SD), value = unlist(.SD, use.names = F)), by = uid]

The cost of the syntax is worth it; the function runs very quickly!