GForce double brackets

[renkun-ken]

I'm not sure if there's a reason that [[ is not gforce-optimized while [ is?

library(data.table)

dt <- data.table(group = rep(1:10, each = 100))
dt[, x := rnorm(.N)]
dt[, y := rnorm(.N)]

dt[, .(x1 = x[1], y = y[1]), keyby = group, verbose = TRUE]
#> Detected that j uses these columns: x,y 
#> Finding groups using forderv ... 0.008s elapsed (0.034s cpu) 
#> Finding group sizes from the positions (can be avoided to save RAM) ... 0.000s elapsed (0.000s cpu) 
#> lapply optimization is on, j unchanged as 'list(x[1], y[1])'
#> GForce optimized j to 'list(`g[`(x, 1), `g[`(y, 1))'
#> Making each group and running j (GForce TRUE) ... 0.000s elapsed (0.001s cpu)
#>     group          x1          y
#>  1:     1  0.53517123 -0.9882846
#>  2:     2  0.51018979 -0.2830759
#>  3:     3  1.24404772  0.3771081
#>  4:     4  0.04945898  0.2840527
#>  5:     5 -0.94713301  0.5256134
#>  6:     6  1.05691797 -0.7430845
#>  7:     7  0.53789528 -0.4212249
#>  8:     8  0.69661191 -0.0915251
#>  9:     9  1.74234732 -0.6924493
#> 10:    10  1.11381907  0.2091079

dt[, .(x1 = x[[1]], y = y[[1]]), keyby = group, verbose = TRUE]
#> Detected that j uses these columns: x,y 
#> Finding groups using forderv ... 0.000s elapsed (0.000s cpu) 
#> Finding group sizes from the positions (can be avoided to save RAM) ... 0.000s elapsed (0.000s cpu) 
#> lapply optimization is on, j unchanged as 'list(x[[1]], y[[1]])'
#> GForce is on, left j unchanged
#> Old mean optimization is on, left j unchanged.
#> Making each group and running j (GForce FALSE) ... 
#>   memcpy contiguous groups took 0.000s for 10 groups
#>   eval(j) took 0.000s for 10 calls
#> 0.000s elapsed (0.000s cpu)
#>     group          x1          y
#>  1:     1  0.53517123 -0.9882846
#>  2:     2  0.51018979 -0.2830759
#>  3:     3  1.24404772  0.3771081
#>  4:     4  0.04945898  0.2840527
#>  5:     5 -0.94713301  0.5256134
#>  6:     6  1.05691797 -0.7430845
#>  7:     7  0.53789528 -0.4212249
#>  8:     8  0.69661191 -0.0915251
#>  9:     9  1.74234732 -0.6924493
#> 10:    10  1.11381907  0.2091079