Should be able to refer to i's .SD during a join.

[arunsrinivasan]

set.seed(45L)
dt1= data.table(x=rep(1:2, each=10), y=sample(8,20,TRUE), key="x")
dt2 = data.table(x=1:2, y1=1:2, y2=3:4, y3=5:6, y4=7:8, key="x")

Now if I'd like to join and for each match get the count of values y >= y_i. Using by=.EACHI this is quite simple:

dt1[dt2, .(sum(y>=y1), sum(y>=y2), sum(y>=y3), sum(y>=y4)), by=.EACHI]

Having a i.SD would allow usage of:

dt1[dt2, lapply(i.SD, function(x) sum(y > x)), by=.EACHI]

[matthieugomez]

That's be nice. i.SD would also be useful if one wants to merge in place dt1[dt2, :=] and keeps dt2 columns

[MichaelChirico]

Adding my hat to the ring for this, as it would help solve a problem I encountered recently, basically what @matthieugomez mentioned--

Namely, updating many columns by reference during a join operation, especially when the inner table may have hard-to-predict column names.

That is, DT1[DT2, (names(DT2) := mget(paste0("i.", names(DT2)))] works (though looks clunky), but this approach doesn't work if we're doing something like:

DT1[DT2[,(summarize some variables),by=list(other_variables)],
        (what_to_use?):=(hard/inconvenient to predict/control names),on=other_variables]

At least with i.SD, we'd only have to keep track of the number of variables to be joined.

Here's an example:

trts <- LETTERS[1:20]

DT1 <- data.table(trts, location = 1:20)

set.seed(1023)
nn <- 10000L
DT2 <- data.table(id = sample(1:25, nn, T),
                  trts = sample(trts, nn, T),
                  val1 = runif(nn),
                  val2 = (runif(nn) > .2))

DT1[dcast(DT2[, .(sum(val1), mean(val2)), by = .(id, trts)],
          trts ~ id, value.var = c("V1", "V2")), on = "trts"]

It's easy to merge, but not to update DT1 by reference.

[aryoda]

Any news on that FR?

Since the i.col and x.col syntax is now supported in joins since 1.9.7 this may be a good code base for implementing x.SD and i.SD variables?

Allow x's cols to be referred to using 'x.' prefix

I'd love to see this feature in the data.table!

[UweBlock]

There is a related question on SO: How can I access all columns of i when using .EACHI in data.table

[MichaelChirico]

A bit strange that get works on 'i.col' strings but eval does not (as pointed out in #1180).

This answer is very clunky; revisit when this is closed

[jangorecki]

I spotted it is already possible to refer to i's .SD, but using .iSD name rather than i.SD, and only when using by=.EACHI. But it doesn't really give any meaningful answer.

d1 = data.table(id=1:3)
d2 = data.table(id=2:4, v1=5:7, v2=6:8)

d1[d2, .iSD, on="id", by=.EACHI]
#Empty data.table (0 rows and 3 cols): id,v1,v2

d1[d2, .SD, on="id", by=.EACHI]
#Empty data.table (0 rows and 3 cols): id,v1,v2

[ColeMiller1]

Adding to Jan's observation, we can trick jisvars and jiscols by just including quote(mget) in a {...} expression:

library(data.table)
set.seed(45L)
dt1= data.table(x=rep(1:2, each=10), y=sample(8,20,TRUE), key="x")
dt2 = data.table(x=1:2, y1=1:2, y2=3:4, y3=5:6, y4=7:8, key="x")

dt1[dt2, .(sum(y>=y1), sum(y>=y2), sum(y>=y3), sum(y>=y4)), by=.EACHI]
#>    x V1 V2 V3 V4
#> 1: 1 10  9  5  3
#> 2: 2  9  9  4  1
dt1[dt2, {quote(mget)
  lapply(.iSD, function(x) sum(y >= x))}, by=.EACHI]
#>    x  x y1 y2 y3 y4
#> 1: 1 10 10  9  5  3
#> 2: 2  9  9  9  4  1

Here, I also have the x column allocated but it's pretty close. So, it seems like this would be easy to implement if we used some of what is done with .SD.

[UweBlock]

For the sake of completeness, we can get the same result as in ColeMiler1's comment with

dt1[dt2, lapply(mget(names(dt2)), function(x) sum(y >= x)), by = .EACHI]

or

dt1[dt2, lapply(mget(names(.iSD)), function(x) sum(y >= x)), by = .EACHI]

   x  x y1 y2 y3 y4
1: 1 10 10  9  5  3
2: 2  9  9  9  4  1

BTW, we can get rid of the redundant second x column by

dt1[dt2, lapply(mget(setdiff(names(.iSD), names(.BY))), function(x) sum(y >= x)), by=.EACHI]
   x y1 y2 y3 y4
1: 1 10  9  5  3
2: 2  9  9  4  1

data.table version 1.12.9 used.

[ColeMiller1]

@UweBlock that seems more logical 😀

I assume .iSD would by default remove the joining columns. I think the other question is whether .iSD is always allocated or whether it depends on NSE detecting the use of get, mget, eval, or .iSD. To be consistent with .SD memory allocation approach, I assume we would use NSE.