[R] duplicated() on zero-column data frames returns empty vector

[Mark Webster]

Hello,
I found what looks to me like an odd edge case for duplicated(), unique() etc. on data frames with zero columns, due to duplicated() returning a zero-length vector for them, regardless of the number of rows:
df <- data.frame(a = 1:5)df$a <- NULLnrow(df) # 5 (row count preserved by row.names)duplicated(df) # logical(0), should be c(FALSE, TRUE, TRUE, TRUE, TRUE)anyDuplicated(df) # 0, should be 2nrow(unique(df)) # 0, should be 1
This behaviour isn't mentioned in the documentation; is there a reason for it to work like this?I'm struggling to see this as anything other than unintended behaviour, as a consequence of the do.call(Map, `names<-(c(list, x), NULL)`) expression in duplicated.data.frame returning an empty list instead of a list of empty lists.
Other data frame libraries have similar behaviour: tibble does the same; data.table, Python's pandas and Rust's polars drop all the rows as soon as there are zero columns, because they don't preserve the row count via the row names.
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I admit this is a case we rarely care about.However, for an example of this being an issue, I've been running into it when treating data frames as database relations, where they have one or more candidate keys (irreducible subsets of the columns for which every row must have a unique value set).Sometimes, a generated relation can have an empty candidate key, which limits it to only having zero or one rows.Usually, I can check a relation contains no duplicated key values by using anyDuplicated:
df2 <- unique(ChickWeight[, c("Chick", "Diet")])keycols <- "Chick" # Each chick only has one diet (Chick -> Diet)!anyDuplicated(df2[, keycols, drop = FALSE]) # TRUE, so Chick values are unique
When the key is empty, any row after the first must be a duplicate, but anyDuplicated doesn't detect these because of the above edge case, so I have to add special handling:
df3 <- data.frame(a = rep(1, 5)) # relations shouldn't have duplicate rowskeycols <- character(0) # a is constant, so key is empty!anyDuplicated(df3[, keycols, drop = FALSE]) # TRUE because equivalent to !any(logical(0)) by above, should be FALSE
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Best Regards,Mark
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