```
# NOT RUN {
DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3),
C = rep(1:2, 6), key = "A,B")
duplicated(DT)
unique(DT)
duplicated(DT, by="B")
unique(DT, by="B")
duplicated(DT, by=c("A", "C"))
unique(DT, by=c("A", "C"))
DT = data.table(a=c(2L,1L,2L), b=c(1L,2L,1L)) # no key
unique(DT) # rows 1 and 2 (row 3 is a duplicate of row 1)
DT = data.table(a=c(3.142, 4.2, 4.2, 3.142, 1.223, 1.223), b=rep(1,6))
unique(DT) # rows 1,2 and 5
DT = data.table(a=tan(pi*(1/4 + 1:10)), b=rep(1,10)) # example from ?all.equal
length(unique(DT$a)) # 10 strictly unique floating point values
all.equal(DT$a,rep(1,10)) # TRUE, all within tolerance of 1.0
DT[,which.min(a)] # row 10, the strictly smallest floating point value
identical(unique(DT),DT[1]) # TRUE, stable within tolerance
identical(unique(DT),DT[10]) # FALSE
# fromLast=TRUE
DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3),
C = rep(1:2, 6), key = "A,B")
duplicated(DT, by="B", fromLast=TRUE)
unique(DT, by="B", fromLast=TRUE)
# anyDuplicated
anyDuplicated(DT, by=c("A", "B")) # 3L
any(duplicated(DT, by=c("A", "B"))) # TRUE
# uniqueN, unique rows on key columns
uniqueN(DT, by = key(DT))
# uniqueN, unique rows on all columns
uniqueN(DT)
# uniqueN while grouped by "A"
DT[, .(uN=uniqueN(.SD)), by=A]
# uniqueN's na.rm=TRUE
x = sample(c(NA, NaN, runif(3)), 10, TRUE)
uniqueN(x, na.rm = FALSE) # 5, default
uniqueN(x, na.rm=TRUE) # 3
# }
```

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