Separating positive and negative values in R data.table (many columns)

I was wondering how I could split many columns by their sign in a data.table. To be concrete, suppose that we have:

library(data.table)
DT = data.table(x = c(-1,-2,1,3),
                z = c(-1,-1,-1,-1))

I am looking to create a new data.table called DT_new such that it looks like:


 DT_new
    x  z x_pos x_neg z_pos z_neg
1: -1 -1     0     1     0     1
2: -2 -1     0     2     0     1
3:  1 -1     1     0     0     1
4:  3 -1     3     0     0     1

The reason I am doing this is that I want to separate out the positive and negative variables in a regression. Doing a few of these manually is easy enough. But I have hundreds of variables that I want to apply this technique to. So I am hoping that there is a "SDcols" solutions.

Thanks!

Solution

No need to use .SDcols ;-) Please find below a reprex:

Code

DT[,`:=` (x_pos = fifelse(x>0, x, 0),
          x_neg = fifelse(x<0, abs(x), 0),
          z_pos = fifelse(z>0, z, 0),
          z_neg = fifelse(z<0, abs(z), 0))][]

Output

    x  z x_pos x_neg z_pos z_neg
1: -1 -1     0     1     0     1
2: -2 -1     0     2     0     1
3:  1 -1     1     0     0     1
4:  3 -1     3     0     0     1

AS A FOLLOW-UP TO YOUR COMMENT

Please find the reprex below.

Code

vars <- c("x","z")
suffix <- c("_pos", "_neg")

DT[, CJ(vars, suffix, sorted = FALSE)[, paste0(vars, suffix)] := .(fifelse(x>0, x, 0),
                                                                   fifelse(x<0, abs(x), 0), 
                                                                   fifelse(z>0, z, 0),
                                                                   fifelse(z<0, abs(z), 0))][]

Output

#>     x  z x_pos x_neg z_pos z_neg
#> 1: -1 -1     0     1     0     1
#> 2: -2 -1     0     2     0     1
#> 3:  1 -1     1     0     0     1
#> 4:  3 -1     3     0     0     1

^{Created on 2021-11-28 by the reprex package (v2.0.1)}