R data.table assigning single-row list-type columns

Question

I find the logic of data.table inconsistent for the two below operations:

Operation 1:

df1<-data.table(a=c(1,2))
list1<-list(c(1,2), 1)
df1[,b:=list1]

#> df1
#   a   b
#1: 1 1,2
#2: 2   1

Operation 2: (data.table treats a singleton list as if I supplied a vector)

df2<-data.table(a=c(1))
list2<-list(c(1,2))
df2[, b:=list2]
#Warning message:
#In `[.data.table`(df2, , `:=`(b, list2)) :
#  Supplied 2 items to be assigned to 1 items of column 'b' (1 unused)
#> df2
   a b
#1: 1 1

I would like the output in the second case to be:

#   a   b
#1: 1 1,2

I can do to unify both cases:

df1[, b:=list(list1)]
df2[, b:=list(list2)]

Is this the best solution? Is there no option for data.table to not unnest a singleton list? Are there no extra operations performance-wise in the first case when I use b:=list(list1)?

Answer 1

Copying my answer from https://stackoverflow.com/a/54797914/2490497
I cannot suggests a duplicate because of "This question does not have an upvoted or accepted answer ".

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This is very good question that touches design decision about := operator.

For simple calls using := as an operator, like col := val, we decided to wrap val into a list automatically. This decision was made to make it more convenient for users to assign single column.

When you are using function call form, ":="(col = val) we are not wrapping val into list anymore. It is extended form already. := behaves as an alias to list but updating in-place. You can always check what will be the updated column by changing := into list (or .) like .(col = val).

Not that even when using := as an operator, you still have to provide RHS as list of you are creating 2+ columns, c("col1","col2") := list(val1, val2).