howto-faq-coercion | R Documentation |

This guide illustrates how to implement `vec_ptype2()`

and `vec_cast()`

methods for existing classes. Related topics:

For an overview of how these generics work and their roles in vctrs, see

`?theory-faq-coercion`

.For an example of implementing coercion methods for data frame subclasses, see

`?howto-faq-coercion-data-frame`

.For a tutorial about implementing vctrs classes from scratch, see

`vignette("s3-vector")`

We’ll illustrate how to implement coercion methods with a simple class
that represents natural numbers. In this scenario we have an existing
class that already features a constructor and methods for `print()`

and
subset.

#' @export new_natural <- function(x) { if (is.numeric(x) || is.logical(x)) { stopifnot(is_whole(x)) x <- as.integer(x) } else { stop("Can't construct natural from unknown type.") } structure(x, class = "my_natural") } is_whole <- function(x) { all(x %% 1 == 0 | is.na(x)) } #' @export print.my_natural <- function(x, ...) { cat("<natural>\n") x <- unclass(x) NextMethod() } #' @export `[.my_natural` <- function(x, i, ...) { new_natural(NextMethod()) }

new_natural(1:3) #> <natural> #> [1] 1 2 3 new_natural(c(1, NA)) #> <natural> #> [1] 1 NA

To implement methods for generics, first import the generics in your namespace and redocument:

#' @importFrom vctrs vec_ptype2 vec_cast NULL

Note that for each batches of methods that you add to your package, you need to export the methods and redocument immediately, even during development. Otherwise they won’t be in scope when you run unit tests e.g. with testthat.

Implementing double dispatch methods is very similar to implementing
regular S3 methods. In these examples we are using roxygen2 tags to
register the methods, but you can also register the methods manually in
your NAMESPACE file or lazily with `s3_register()`

.

`vec_ptype2()`

The first method to implement is the one that signals that your class is compatible with itself:

#' @export vec_ptype2.my_natural.my_natural <- function(x, y, ...) { x } vec_ptype2(new_natural(1), new_natural(2:3)) #> <natural> #> integer(0)

`vec_ptype2()`

implements a fallback to try and be compatible with
simple classes, so it may seem that you don’t need to implement the
self-self coercion method. However, you must implement it explicitly
because this is how vctrs knows that a class that is implementing vctrs
methods (for instance this disable fallbacks to `base::c()`

). Also, it
makes your class a bit more efficient.

Our natural number class is conceptually a parent of `<logical>`

and a
child of `<integer>`

, but the class is not compatible with logical,
integer, or double vectors yet:

vec_ptype2(TRUE, new_natural(2:3)) #> Error: #> ! Can't combine `TRUE` <logical> and `new_natural(2:3)` <my_natural>. vec_ptype2(new_natural(1), 2:3) #> Error: #> ! Can't combine `new_natural(1)` <my_natural> and `2:3` <integer>.

We’ll specify the twin methods for each of these classes, returning the richer class in each case.

#' @export vec_ptype2.my_natural.logical <- function(x, y, ...) { # The order of the classes in the method name follows the order of # the arguments in the function signature, so `x` is the natural # number and `y` is the logical x } #' @export vec_ptype2.logical.my_natural <- function(x, y, ...) { # In this case `y` is the richer natural number y }

Between a natural number and an integer, the latter is the richer class:

#' @export vec_ptype2.my_natural.integer <- function(x, y, ...) { y } #' @export vec_ptype2.integer.my_natural <- function(x, y, ...) { x }

We no longer get common type errors for logical and integer:

vec_ptype2(TRUE, new_natural(2:3)) #> <natural> #> integer(0) vec_ptype2(new_natural(1), 2:3) #> integer(0)

We are not done yet. Pairwise coercion methods must be implemented for all the connected nodes in the coercion hierarchy, which include double vectors further up. The coercion methods for grand-parent types must be implemented separately:

#' @export vec_ptype2.my_natural.double <- function(x, y, ...) { y } #' @export vec_ptype2.double.my_natural <- function(x, y, ...) { x }

Most of the time, inputs are incompatible because they have different
classes for which no `vec_ptype2()`

method is implemented. More rarely,
inputs could be incompatible because of their attributes. In that case
incompatibility is signalled by calling `stop_incompatible_type()`

.

In the following example, we implement a self-self ptype2 method for a
hypothetical subclass of `<factor>`

that has stricter combination
semantics. The method throws when the levels of the two factors are not
compatible.

#' @export vec_ptype2.my_strict_factor.my_strict_factor <- function(x, y, ..., x_arg = "", y_arg = "") { if (!setequal(levels(x), levels(y))) { stop_incompatible_type(x, y, x_arg = x_arg, y_arg = y_arg) } x }

Note how the methods need to take `x_arg`

and `y_arg`

parameters and
pass them on to `stop_incompatible_type()`

. These argument tags help
create more informative error messages when the common type
determination is for a column of a data frame. They are part of the
generic signature but can usually be left out if not used.

`vec_cast()`

Corresponding `vec_cast()`

methods must be implemented for all
`vec_ptype2()`

methods. The general pattern is to convert the argument
`x`

to the type of `to`

. The methods should validate the values in `x`

and make sure they conform to the values of `to`

.

Please note that for historical reasons, the order of the classes in the
method name is in reverse order of the arguments in the function
signature. The first class represents `to`

, whereas the second class
represents `x`

.

The self-self method is easy in this case, it just returns the target input:

#' @export vec_cast.my_natural.my_natural <- function(x, to, ...) { x }

The other types need to be validated. We perform input validation in the
`new_natural()`

constructor, so that’s a good fit for our `vec_cast()`

implementations.

#' @export vec_cast.my_natural.logical <- function(x, to, ...) { # The order of the classes in the method name is in reverse order # of the arguments in the function signature, so `to` is the natural # number and `x` is the logical new_natural(x) } vec_cast.my_natural.integer <- function(x, to, ...) { new_natural(x) } vec_cast.my_natural.double <- function(x, to, ...) { new_natural(x) }

With these methods, vctrs is now able to combine logical and natural vectors. It properly returns the richer type of the two, a natural vector:

vec_c(TRUE, new_natural(1), FALSE) #> <natural> #> [1] 1 1 0

Because we haven’t implemented conversions *from* natural, it still
doesn’t know how to combine natural with the richer integer and double
types:

vec_c(new_natural(1), 10L) #> Error in `vec_c()`: #> ! Can't convert `..1` <my_natural> to <integer>. vec_c(1.5, new_natural(1)) #> Error in `vec_c()`: #> ! Can't convert `..2` <my_natural> to <double>.

This is quick work which completes the implementation of coercion methods for vctrs:

#' @export vec_cast.logical.my_natural <- function(x, to, ...) { # In this case `to` is the logical and `x` is the natural number attributes(x) <- NULL as.logical(x) } #' @export vec_cast.integer.my_natural <- function(x, to, ...) { attributes(x) <- NULL as.integer(x) } #' @export vec_cast.double.my_natural <- function(x, to, ...) { attributes(x) <- NULL as.double(x) }

And we now get the expected combinations.

vec_c(new_natural(1), 10L) #> [1] 1 10 vec_c(1.5, new_natural(1)) #> [1] 1.5 1.0

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