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()
.
Usage
compare(
x,
y,
...,
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,
quote_strings = TRUE
)
Arguments
- x, y
Objects to compare.
x
is treated as the reference object so messages describe howy
is different tox
.- ...
A handful of other arguments are supported with a warning for backward comparability. These include:
all.equal()
argumentscheckNames
andcheck.attributes
testthat::compare()
argumenttol
All other arguments are ignored with a warning.
- x_arg, y_arg
Name of
x
andy
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.- tolerance
If non-
NULL
, used as threshold for ignoring small floating point difference when comparing numeric vectors. Using any non-NULL
value will cause integer and double vectors to be compared based on their values, not their types, and will ignore the difference betweenNaN
andNA_real_
.It uses the same algorithm as
all.equal()
, i.e., first we generatex_diff
andy_diff
by subsettingx
andy
to look only locations with differences. Then we check thatmean(abs(x_diff - y_diff)) / mean(abs(y_diff))
(or justmean(abs(x_diff - y_diff))
ify_diff
is small) is less thantolerance
.- max_diffs
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_srcref
Ignore differences in function
srcref
s?TRUE
by default since thesrcref
does not change the behaviour of a function, only its printed representation.- ignore_attr
Ignore differences in specified attributes? Supply a character vector to ignore differences in named attributes. By default the
"waldo_opts"
attribute is listed inignore_attr
so that changes to it are not reported; if you customizeignore_attr
, you will probably want to do this yourself.For backward compatibility with
all.equal()
, you can also useTRUE
, 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_encoding
Ignore string encoding?
TRUE
by default, because this is R's default behaviour. UseFALSE
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.- list_as_map
Compare lists as if they are mappings between names and values. Concretely, this drops
NULLs
in both objects and sorts named components.- quote_strings
Should strings be surrounded by quotes? If
FALSE
, only side-by-side and line-by-line comparisons will be used, and there's no way to distinguish betweenNA
and"NA"
.
Value
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:
Defaults from
compare()
.The
waldo_opts
for the parents ofx
.The
waldo_opts
for the parents ofy
.The
waldo_opts
forx
.The
waldo_opts
fory
.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.
Examples
# 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
compare(
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"