Test Objects for Exact Equality - R Documentation

`identical` 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,
          ignore.srcref = TRUE)

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.
`ignore.srcref`	logical indicating if their `"srcref"` attributes 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 NaNs 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.

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))
identical(TRUE, E)
isTRUE(E) # alternative test
## 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 ("closure"s): ----------------------------------------------
###     ~~~~~~~~~
f <- function(x) x
f
g <- compiler::cmpfun(f)
g
identical(f, g)                        # TRUE, as bytecode is ignored by default
identical(f, g, ignore.bytecode=FALSE) # FALSE: bytecode differs

## GLM families contain several functions, some of which share an environment:
p1 <- poisson() ; p2 <- poisson()
identical(p1, p2)                          # FALSE
identical(p1, p2, ignore.environment=TRUE) # TRUE

## in interactive use, the 'keep.source' option is typically true:
op <- options(keep.source = TRUE) # and so, these have differing "srcref" :
f1 <- function() {}
f2 <- function() {}
identical(f1,f2)# ignore.srcref= TRUE : TRUE
identical(f1,f2,  ignore.srcref=FALSE)# FALSE
options(op) # revert to previous state

identical Test Objects for Exact Equality