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Compare - Exact equality with more flexibility

identical2.Rd

Similar to identical() but with more flexibility on how to handle data and attributes.

  • identical2() test two objects for exact equality.

  • identical_flag() runs identical() or identical2() recursively, returning the comparison results in a list with the same structure as x. Useful to flag where x and y differ.

To see if multiple objects are identical, wrap them in a list and use reduce_predicate(). To vectorize over two lists, checking that each pair of elements between them are identical, use Map(identical2, x, y) (see Map()).

Usage

identical2(
  x,
  y,
  single_NA = TRUE,
  single_zero = TRUE,
  ord_data = TRUE,
  ord_attrs = FALSE,
  tol_type = "none",
  tol = sqrt(.Machine$double.eps),
  ignore_data = character(),
  ignore_attrs = list()
)

identical_flag(x, y, ..., fun = "identical2", .is_attrs = FALSE)

Arguments

x, y

[any] Any R object.

single_NA, single_zero

[TRUE | FALSE each] Whether to treat NA values as a identical to each other, and the same for +0 vs. -0.

ord_data, ord_attrs

[TRUE | FALSE each] Whether to keep the order of the data and attributes in x and y.

tol_type

["none" | "abs" | "rel"] Type of tolerance to use when comparing numeric values. One of:

  • "none": no tolerance (default).

  • "abs": absolute tolerance.

  • "rel": relative tolerance.

tol

[numeric(1)] Tolerance value.

ignore_data

[character() | integer()] indexes or names of elements to ignore when comparing x and y. Names are compared against rlang::names2().

ignore_attrs

[list()] Arguments to pass to attrs_rmv() to remove attributes from x and y

...

For identical_flag(): arguments passed to identical2() or identical().

fun

["identical" | "identical2"] For identical_flag(): function to use for comparison.

.is_attrs

[TRUE | FALSE] For internal use only.

Value

  • [TRUE | FALSE] For identical2(): the scalar result of the test.

  • identical_flag() returns a object with the same structure as x and logical elements.

Examples

x <- structure(c(1, 2, 3), a = "a", b = "b", c = "c")
y <- structure(c(3, 2, 1), b = "b", a = "a", c = "d")

identical2(x, y) #> FALSE
#> [1] FALSE

# Pre-sort data and ignore "c" attribute:
identical2(
  x, y, ord_data = FALSE,
  ignore_attrs = list(exact = c("c"))
) #> TRUE
#> [1] TRUE

# Consider attribute order:
identical2(
  x, y, ord_data = FALSE,
  ignore_attrs = list(exact = c("c")), ord_attrs = TRUE
) #> FALSE
#> [1] FALSE

# No sorting but accept up to 2.1 numerical absolute tolerance:
identical2(
  x, y, tol_type = "abs", tol = 2.1,
  ignore_attrs = list(exact = c("c"))
) #> TRUE
#> [1] TRUE

# Understading where the differences are:
identical_flag(x, y)
#> $.data
#> [1] FALSE  TRUE FALSE
#> 
#> $.attrs
#> $.attrs$a
#> $.attrs$a$.data
#> [1] TRUE
#> 
#> 
#> $.attrs$b
#> $.attrs$b$.data
#> [1] TRUE
#> 
#> 
#> $.attrs$c
#> $.attrs$c$.data
#> [1] FALSE
#> 
#> 
#> 
#> $.data
#> c(FALSE, TRUE, FALSE) # First (3 & 1) and third (1 & 3) elements are different
#>
#> $.attrs
#> $.attrs$a
#> $.attrs$a$.data
#> TRUE
#>
#> $.attrs$b
#> $.attrs$b$.data
#> TRUE
#>
#> $.attrs$c
#> $.attrs$c$.data
#> FALSE # Attribute "c" ("c" & "d") is different