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This compares two R objects, identifying the key differences. It:

  • Orders the differences from most important to least important.

  • Displays the values of atomic vectors that are actually different.

  • Carefully uses colour to emphasise changes (while still being readable when colour isn't available).

  • Uses R code (not a text description) to show where differences arise.

  • Where possible, it compares elements by name, rather than by position.

  • Errs on the side of producing too much output, rather than too little.

compare() is an alternative to all.equal().


  x_arg = "old",
  y_arg = "new",
  tolerance = NULL,
  max_diffs = if (in_ci()) Inf else 10,
  ignore_srcref = TRUE,
  ignore_attr = "waldo_opts",
  ignore_encoding = TRUE,
  ignore_function_env = FALSE,
  ignore_formula_env = FALSE,
  list_as_map = FALSE


x, y

Objects to compare. x is treated as the reference object so messages describe how y is different to x.


A handful of other arguments are supported with a warning for backward comparability. These include:

All other arguments are ignored with a warning.

x_arg, y_arg

Name of x and y arguments, used when generated paths to internal components. These default to "old" and "new" since it's most natural to supply the previous value then the new value.


If non-NULL, used as threshold for ignoring small floating point difference when comparing numeric vectors. Setting to any non-NULL value will cause integer and double vectors to be compared based on their values, rather than their types.

It uses the same algorithm as all.equal(), i.e., first we generate x_diff and y_diff by subsetting x and y to look only locations with differences. Then we check that mean(abs(x_diff - y_diff)) / mean(abs(y_diff)) (or just mean(abs(x_diff - y_diff)) if y_diff is small) is less than tolerance.


Control the maximum number of differences shown. The default shows 10 differences when run interactively and all differences when run in CI. Set max_diffs = Inf to see all differences.


Ignore differences in function srcrefs? TRUE by default since the srcref does not change the behaviour of a function, only its printed representation.


Ignore differences in specified attributes? Supply a character vector to ignore differences in named attributes. By default the "waldo_opts" attribute is listed in ignore_attr so that changes to it are not reported; if you customize ignore_attr, you will probably want to do this yourself.

For backward compatibility with all.equal(), you can also use TRUE, to all ignore differences in all attributes. This is not generally recommended as it is a blunt tool that will ignore many important functional differences.


Ignore string encoding? TRUE by default, because this is R's default behaviour. Use FALSE when specifically concerned with the encoding, not just the value of the string.

ignore_function_env, ignore_formula_env

Ignore the environments of functions and formulas, respectively? These are provided primarily for backward compatibility with all.equal() which always ignores these environments.


Compare lists as if they are mappings between names and values. Concretely, this drops NULLs in both objects and sorts named components.


A character vector with class "waldo_compare". If there are no differences it will have length 0; otherwise each element contains the description of a single difference.

Controlling comparisons

There are two ways for an object (rather than the person calling compare() or expect_equal() to control how it is compared to other objects. First, if the object has an S3 class, you can provide a compare_proxy() method that provides an alternative representation of the object; this is particularly useful if important data is stored outside of R, e.g. in an external pointer.

Alternatively, you can attach an attribute called "waldo_opts" to your object. This should be a list of compare options, using the same names and possible values as the arguments to this function. This option is ignored by default (ignore_attr) so that you can set the options in the object that you control. (If you don't want to see the attributes interactively, you could attach them in a compare_proxy() method.)

Options supplied in this way also affect all the children. This means options are applied in the following order, from lowest to highest precedence:

  1. Defaults from compare().

  2. The waldo_opts for the parents of x.

  3. The waldo_opts for the parents of y.

  4. The waldo_opts for x.

  5. The waldo_opts for y.

  6. User-specified arguments to compare().

Use these techniques with care. If you accidentally cover up an important difference you can create a confusing situation where x and y behave differently but compare() reports no differences in the underlying objects.


# Thanks to diffobj package comparison of atomic vectors shows differences
# with a little context
compare(letters, c("z", letters[-26]))
#> `old[1:3]`:     "a" "b" "c"
#> `new[1:4]`: "z" "a" "b" "c"
#> `old[23:26]`: "w" "x" "y" "z"
#> `new[24:26]`: "w" "x" "y"    
compare(c(1, 2, 3), c(1, 3))
#> `old`: 1 2 3
#> `new`: 1   3
compare(c(1, 2, 3), c(1, 3, 4, 5))
#> `old`: 1 2 3  
#> `new`: 1 3 4 5
compare(c(1, 2, 3), c(1, 2, 5))
#> `old`: 1 2 3
#> `new`: 1 2 5

# More complex objects are traversed, stopping only when the types are
# different
  list(x = list(y = list(structure(1, z = 2)))),
  list(x = list(y = list(structure(1, z = "a"))))
#> `attr(old$x$y[[1]], 'z')` is a double vector (2)
#> `attr(new$x$y[[1]], 'z')` is a character vector ('a')

# Where possible, recursive structures are compared by name
compare(iris, rev(iris))
#>     names(old)     | names(new)        
#> [1] "Sepal.Length" - "Species"      [1]
#> [2] "Sepal.Width"  - "Petal.Width"  [2]
#> [3] "Petal.Length" | "Petal.Length" [3]
#> [4] "Petal.Width"  - "Sepal.Width"  [4]
#> [5] "Species"      - "Sepal.Length" [5]

compare(list(x = "x", y = "y"), list(y = "y", x = "x"))
#> `names(old)`: "x" "y"
#> `names(new)`: "y" "x"
# Otherwise they're compared by position
compare(list("x", "y"), list("x", "z"))
#> `old[[2]]`: "y"
#> `new[[2]]`: "z"
compare(list(x = "x", x = "y"), list(x = "x", y = "z"))
#> `names(old)`: "x" "x"
#> `names(new)`: "x" "y"
#> `old[[2]]`: "y"
#> `new[[2]]`: "z"