I have a set of columns that each column should contain exactly one distinct value per group.
Therefore, if I am looking at just a single group (eg by=byCol)
  and I subset the data to just those columns  (eg .SDcols=colsToCheck)
  then each given .SD should have only one unique row. (eg  1==nrow(unique(.SD)) )

<note for the next part, we expect `j=` to be a single value, such as `.N` or `NA`
However, notice that if I instead include `colsToCheck` in the `by=c(..)`
    (eg by=c(byCols, colsToCheck) )
  and I were to compare that to when `colsToCheck` was NOT included
    (eg by=c(byCols) )
  then the former would have an extra row in each group for each 
  unique value (rather, set of valies) in each (set of) columns
  of `colsToCheck`
  


nrow(DT[, .N, by=byCols])
nrow(DT[, .N, by=c(byCols, colsToCheck)])



Keep in mind that I want to check EACH COLUMN for uniqueness, 
not just each combinaiton of rows. 

    DT[samp.inds
           , list(singleVal = {1==nrow(unique(.SD, by=colsToCheck))}
               , singleVal2 = {.SD, by=colsToCheck))})
           , .SDcols=colsToCheck  # need .SDcols if (any(colsToCheck %in% byCols))
           , keyby=byCols]



           [!(singleVal)
              , if (!(nrow(.SD)))
                  TRUE
                else
                  .SD[, byCols, with=FALSE]
              ]