Learn R Programming

vetr (version 0.2.18)

alike: Compare Object Structure

Description

Similar to all.equal, but compares object structure rather than value. The target argument defines a template that the current argument must match.

Usage

alike(target, current, env = parent.frame(), settings = NULL)

Value

TRUE if target and current are alike, character(1L) describing why they are not if they are not

Arguments

target

the template to compare the object to

current

the object to determine alikeness of to the template

env

environment used internally when evaluating expressions; currently used only when looking up functions to match.call when testing language objects, note that this will be overridden by the environment specified in settings if any, defaults to the parent frame.

settings

a list of settings generated using vetr_settings, NULL for default

alikeness

Generally speaking two objects are alike if they are of the same type (as determined by type_alike) and length. Attributes on the objects are required to be recursively alike, though the following attributes are treated specially: class, dim, dimnames, names, row.names, levels, tsp, and srcref.

Exactly what makes two objects alike is complex, but should be intuitive. The best way to understand "alikeness" is to review the examples. For a thorough exposition see the vignette.

Note that the semantics of alikeness for language objects, formulas, and functions may change in the future.

See Also

type_alike, type_of, abstract, vetr_settings for more control of settings

Examples

Run this code
## Type comparison
alike(1L, 1.0)         # TRUE, because 1.0 is integer-like
alike(1L, 1.1)         # FALSE, 1.1 is not integer-like
alike(1.1, 1L)         # TRUE, by default, integers are always considered real

alike(1:100, 1:100 + 0.0)  # TRUE

## We do not check numerics for integerness if longer than 100
alike(1:101, 1:101 + 0.0)

## Scalarness can now be checked at same time as type
alike(integer(1L), 1)            # integer-like and length 1?
alike(logical(1L), TRUE)         # logical and length 1?
alike(integer(1L), 1:3)
alike(logical(1L), c(TRUE, TRUE))

## Zero length match any length of same type
alike(integer(), 1:10)
alike(1:10, integer())   # but not the other way around

## Recursive objects compared recursively
alike(
  list(integer(), list(character(), logical(1L))),
  list(1:10, list(letters, TRUE))
)
alike(
  list(integer(), list(character(), logical(1L))),
  list(1:10, list(letters, c(TRUE, FALSE)))
)

## `NULL` is a wild card when nested within recursive objects
alike(list(NULL, NULL), list(iris, mtcars))
alike(NULL, mtcars)    # but not at top level

## Since `data.frame` are lists, we can compare them recursively:
iris.fake <- transform(iris, Species=as.character(Species))
alike(iris, iris.fake)

## we even check attributes (factor levels must match)!
iris.fake2 <- iris
levels(iris.fake2$Species) <- c("setosa", "versicolor", "africana")
alike(iris, iris.fake2)

## We can use partially specified objects as templates
iris.tpl <- abstract(iris)
str(iris.tpl)
alike(iris.tpl, iris)
## any row sample of iris matches our iris template
alike(iris.tpl, iris[sample(1:nrow(iris), 10), ])
## but column order matters
alike(iris.tpl, iris[c(2, 1, 3, 4, 5)])

## 3 x 3 integer
alike(matrix(integer(), 3, 3), matrix(1:9, nrow=3))
## 3 x 3, but not integer!
alike(matrix(integer(), 3, 3), matrix(runif(9), nrow=3))
## partial spec, any 3 row integer matrix
alike(matrix(integer(), 3), matrix(1:12, nrow=3))
alike(matrix(integer(), 3), matrix(1:12, nrow=4))
## Any logical matrix (but not arrays)
alike(matrix(logical()), array(rep(TRUE, 8), rep(2, 3)))

## In order for objects to be alike, they must share a family
## tree, not just a common class
obj.tpl <- structure(TRUE, class=letters[1:3])
obj.cur.1 <-  structure(TRUE, class=c("x", letters[1:3]))
obj.cur.2 <-  structure(TRUE, class=c(letters[1:3], "x"))

alike(obj.tpl, obj.cur.1)
alike(obj.tpl, obj.cur.2)

## You can compare language objects; these are alike if they are self
## consistent; we don't care what the symbols are, so long as they are used
## consistently across target and current:

## TRUE, symbols are consistent (adding two different symbols)
alike(quote(x + y), quote(a + b))
## FALSE, different function
alike(quote(x + y), quote(a - b))
## FALSE, inconsistent symbols
alike(quote(x + y), quote(a + a))

Run the code above in your browser using DataLab