duplicated {data.table} | R Documentation

## Determine Duplicate Rows

### Description

`duplicated` returns a logical vector indicating which rows of a `data.table` are duplicates of a row with smaller subscripts.

`unique` returns a `data.table` with duplicated rows removed, by columns specified in `by` argument. When no `by` then duplicated rows by all columns are removed.

`anyDuplicated` returns the index `i` of the first duplicated entry if there is one, and 0 otherwise.

`uniqueN` is equivalent to `length(unique(x))` when x is an `atomic vector`, and `nrow(unique(x))` when x is a `data.frame` or `data.table`. The number of unique rows are computed directly without materialising the intermediate unique data.table and is therefore faster and memory efficient.

### Usage

    ## S3 method for class 'data.table'
    duplicated(x, incomparables=FALSE, fromLast=FALSE, by=seq_along(x), ...)

    ## S3 method for class 'data.table'
    unique(x, incomparables=FALSE, fromLast=FALSE, by=seq_along(x), ...)

    ## S3 method for class 'data.table'
    anyDuplicated(x, incomparables=FALSE, fromLast=FALSE, by=seq_along(x), ...)

    uniqueN(x, by=if (is.list(x)) seq_along(x) else NULL, na.rm=FALSE)

### Arguments

`x` | A data.table. `uniqueN` accepts atomic vectors and data.frames as well.
`...` | Not used at this time.
`incomparables` | Not used. Here for S3 method consistency.
`fromLast` | logical indicating if duplication should be considered from the reverse side, i.e., the last (or rightmost) of identical elements would correspond to `duplicated = FALSE`.
`by` | `character` or `integer` vector indicating which combinations of columns from `x` to use for uniqueness checks. By default all columns are are being used. The was changed recently for consistency to data.frame methods. In version `< 1.9.8` default was `key(x)`.
`na.rm` | Logical (default is `FALSE`). Should missing values (including `NaN`) be removed?

### Details

Because data.tables are usually sorted by key, tests for duplication are especially quick when only the keyed columns are considered. Unlike `unique.data.frame`, `paste` is not used to ensure equality of floating point data. It is instead accomplished directly and is therefore quite fast. data.table provides `setNumericRounding` to handle cases where limitations in floating point representation is undesirable.

`v1.9.4` introduces `anyDuplicated` method for data.tables and is similar to base in functionality. It also implements the logical argument `fromLast` for all three functions, with default value `FALSE`.

### Value

`duplicated` returns a logical vector of length `nrow(x)` indicating which rows are duplicates.

`unique` returns a data table with duplicated rows removed.

`anyDuplicated` returns a integer value with the index of first duplicate. If none exists, 0L is returned.

`uniqueN` returns the number of unique elements in the vector, `data.frame` or `data.table`.

### See Also

`setNumericRounding`, `data.table`, `duplicated`, `unique`, `all.equal`, `fsetdiff`, `funion`, `fintersect`, `fsetequal`

### Examples

    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

[Package data.table version 1.10.0 Index]