Avoid overwriting existing columns in data.table functions - r

Question

I'm writing a function that takes a data.table as an argument. The column names of data.table are partially specified as arguments, but not all columns names are specified and all original columns need to be maintained. Inside the function, some columns need to be added to the data.table. Even if the data.table is copied inside the function, I want to add these columns in a way that is guaranteed not to overwrite existing columns. What's the best way to ensure I'm not overwriting columns given that column names are not known?
Here's one approach:
#x is a data.table and knownvar is a column name of that data.table
f <- function(x,knownvar){
x <- copy(x)
tempcol <- "z"
while(tempcol %in% names(x))
tempcol <- paste0("i.",tempcol)
tempcol2 <- "q"
while(tempcol2 %in% names(x))
tempcol2 <- paste0("i.",tempcol2)
x[, (tempcol):=3]
eval(parse(text=paste0("x[,(tempcol2):=",tempcol,"+4]")))
x
}
Note that even though I'm copying x here, I still need this process to be memory efficient. Is there an easier way of doing this? Possibly without using eval(parse(text=?
Obviously I could just create a local variable (e.g. a vector) in the function environment (rather than adding it explicitly as column of the data.table), but this wouldn't work if I then need to sort/join the data.table. Plus I may want to explicitly return a data.table that contains both the original variables and the new column.

Answer 1

Here is one way to write the function using set and non-standard evaluation with substitute() + eval().
Note 1: if new columns are created based on the column names in newcols (instead of the column name in knownvar), the character names in newcols are converted to symbols with as.name() (or equivalently as.symbol()).
Note 2: new columns in newvals can only be added in a sensible order, i.e. if column q requires column z, column z should be added before column q.
library(data.table)
f <- function(x, knownvar) {
## remove if x should be modified in-place
x <- copy(x)
## new column names
newcols <- setdiff(make.unique(c(names(x), c("z", "q"))), names(x))
## new column values based on knownvar or new column names
zcol <- as.name(newcols[1])
newvals <- list(substitute(3 * knownvar), substitute(zcol + 4))
for(i in seq_along(newvals)) {
set(x, j = newcols[i], value = eval(newvals[[i]], envir = x))
}
return(x)
}
## example data
x <- as.data.table(mtcars)
x[, c("q", "q.1") := .(mpg, 2 * mpg)]
head(f(x, mpg))
#> mpg cyl disp hp drat wt qsec vs am gear carb q q.1 z q.2
#> 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 21.0 42.0 63.0 67.0
#> 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 21.0 42.0 63.0 67.0
#> 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 22.8 45.6 68.4 72.4
#> 4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 21.4 42.8 64.2 68.2
#> 5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 18.7 37.4 56.1 60.1
#> 6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 18.1 36.2 54.3 58.3