In the book Software for Data Analysis: Programming with R, John Chambers emphasizes that functions should generally not be written for their side effect; rather, that a function should return a value without modifying any variables in its calling environment. Conversely, writing good script using data.table objects should specifically avoid the use of object assignment with <-, typically used to store the result of a function.

First, is a technical question. Imagine an R function called proc1 that accepts a data.table object x as its argument (in addition to, maybe, other parameters). proc1 returns NULL but modifies x using :=. From what I understand, proc1 calling proc1(x=x1) makes a copy of x1 just because of the way that promises work. However, as demonstrated below, the original object x1 is still modified by proc1. Why/how is this? 

> require(data.table)
> x1 <- CJ(1:2, 2:3)
> x1
   V1 V2
1:  1  2
2:  1  3
3:  2  2
4:  2  3
> proc1 <- function(x){
+ x[,y:= V1*V2]
+ NULL
+ }
> proc1(x1)
NULL
> x1
   V1 V2 y
1:  1  2 2
2:  1  3 3
3:  2  2 4
4:  2  3 6
>

Furthermore, it seems that using proc1(x=x1) isn't any slower than doing the procedure directly on x, indicating that my vague understanding of promises are wrong and that they work in a pass-by-reference sort of way:

> x1 <- CJ(1:2000, 1:500)
> x1[, paste0("V",3:300) := rnorm(1:nrow(x1))]
> proc1 <- function(x){
+ x[,y:= V1*V2]
+ NULL
+ }
> system.time(proc1(x1))
   user  system elapsed 
   0.00    0.02    0.02 
> x1 <- CJ(1:2000, 1:500)
> system.time(x1[,y:= V1*V2])
   user  system elapsed 
   0.03    0.00    0.03

So, given that passing a data.table argument to a function doesn't add time, that mak

r data.table
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