identical {base} | R Documentation

## Test Objects for Exact Equality

### Description

The safe and reliable way to test two objects for being exactly equal. It returns `TRUE` in this case, `FALSE` in every other case.

### Usage

    identical(x, y, num.eq = TRUE, single.NA = TRUE, attrib.as.set = TRUE,
              ignore.bytecode = TRUE, ignore.environment = FALSE)

### Arguments

`x, y` | any R objects.
`num.eq` | logical indicating if (`double` and `complex` non-`NA`) numbers should be compared using `==` (‘equal’), or by bitwise comparison. The latter (non-default) differentiates between `-0` and `+0`.
`single.NA` | logical indicating if there is conceptually just one numeric `NA` and one `NaN`; `single.NA = FALSE` differentiates bit patterns.
`attrib.as.set` | logical indicating if `attributes` of `x` and `y` should be treated as unordered tagged pairlists (“sets”); this currently also applies to `slot`s of S4 objects. It may well be too strict to set `attrib.as.set = FALSE`.
`ignore.bytecode` | logical indicating if byte code should be ignored when comparing closures.
`ignore.environment` | logical indicating if their environments should be ignored when comparing closures.

### Details

A call to `identical` is the way to test exact equality in `if` and `while` statements, as well as in logical expressions that use `&&` or `||`. In all these applications you need to be assured of getting a single logical value.

Users often use the comparison operators, such as `==` or `!=`, in these situations. It looks natural, but it is not what these operators are designed to do in R. They return an object like the arguments. If you expected `x` and `y` to be of length 1, but it happened that one of them was not, you will not get a single `FALSE`. Similarly, if one of the arguments is `NA`, the result is also `NA`. In either case, the expression `if(x == y)....` won't work as expected.

The function `all.equal` is also sometimes used to test equality this way, but was intended for something different: it allows for small differences in numeric results.

The computations in `identical` are also reliable and usually fast. There should never be an error. The only known way to kill `identical` is by having an invalid pointer at the C level, generating a memory fault. It will usually find inequality quickly. Checking equality for two large, complicated objects can take longer if the objects are identical or nearly so, but represent completely independent copies. For most applications, however, the computational cost should be negligible.

If `single.NA` is true, as by default, `identical` sees `NaN` as different from `NA_real_`, but all `NaN`s are equal (and all `NA` of the same type are equal).

Character strings are regarded as identical if they are in different marked encodings but would agree when translated to UTF-8.

If `attrib.as.set` is true, as by default, comparison of attributes view them as a set (and not a vector, so order is not tested).

If `ignore.bytecode` is true (the default), the compiled bytecode of a function (see `cmpfun`) will be ignored in the comparison. If it is false, functions will compare equal only if they are copies of the same compiled object (or both are uncompiled). To check whether two different compiles are equal, you should compare the results of `disassemble()`.

You almost never want to use `identical` on datetimes of class `"POSIXlt"`: not only can different times in the different time zones represent the same time and time zones have multiple names, but several of the components are optional.

Note that `identical(x, y, FALSE, FALSE, FALSE, FALSE)` pickily tests for exact equality.

### Value

A single logical value, `TRUE` or `FALSE`, never `NA` and never anything other than a single value.

### Author(s)

John Chambers and R Core

### References

Chambers, J. M. (1998) Programming with Data. A Guide to the S Language. Springer.

### See Also

`all.equal` for descriptions of how two objects differ; Comparison for operators that generate elementwise comparisons. `isTRUE` is a simple wrapper based on `identical`.

### Examples

    identical(1, NULL) ## FALSE -- don't try this with ==
    identical(1, 1.)   ## TRUE in R (both are stored as doubles)
    identical(1, as.integer(1)) ## FALSE, stored as different types

    x <- 1.0; y <- 0.99999999999
    ## how to test for object equality allowing for numeric fuzz :
    (E <- all.equal(x, y))
    isTRUE(E) # which is simply defined to just use
    identical(TRUE, E)
    ## If all.equal thinks the objects are different, it returns a
    ## character string, and the above expression evaluates to FALSE

    ## even for unusual R objects :
    identical(.GlobalEnv, environment())

    ### ------- Pickyness Flags : -----------------------------

    ## the infamous example:
    identical(0., -0.) # TRUE, i.e. not differentiated
    identical(0., -0., num.eq = FALSE)
    ## similar:
    identical(NaN, -NaN) # TRUE
    identical(NaN, -NaN, single.NA = FALSE) # differ on bit-level
    ## for functions:
    f <- function(x) x
    f
    g <- compiler::cmpfun(f)
    g
    identical(f, g)
    identical(f, g, ignore.bytecode = FALSE)

[Package base version 3.3.2 Index]