stopifnot(rlang::is_installed("dplyr"))
The tidyverse is a bit stricter than base R regarding what kind of objects are considered as vectors (see the [user FAQ][faq-error-scalar-type] about this topic). Sometimes vctrs won't treat your class as a vector when it should.
By default, S3 lists are not considered to be vectors by vctrs:
my_list <- structure(list(), class = "my_class") vctrs::vec_is(my_list)
To be treated as a vector, the class must either inherit from "list"
explicitly:
my_explicit_list <- structure(list(), class = c("my_class", "list")) vctrs::vec_is(my_explicit_list)
Or it should implement a vec_proxy()
method that returns its input if explicit inheritance is not possible or troublesome:
#' @export vec_proxy.my_class <- function(x, ...) x vctrs::vec_is(my_list)
Note that explicit inheritance is the preferred way because this makes it possible for your class to dispatch on list
methods of S3 generics:
my_generic <- function(x) UseMethod("my_generic") my_generic.list <- function(x) "dispatched!" my_generic(my_list) my_generic(my_explicit_list)
The most likely explanation is that the data frame has not been properly constructed.
However, if you get an "Input must be a vector" error with a data frame subclass, it probably means that the data frame has not been properly constructed. The main cause of these errors are data frames whose base class is not "data.frame"
:
my_df <- data.frame(x = 1) class(my_df) <- c("data.frame", "my_class") vctrs::obj_check_vector(my_df)
This is problematic as many tidyverse functions won't work properly:
dplyr::slice(my_df, 1)
It is generally not appropriate to declare your class to be a superclass of another class. We generally consider this undefined behaviour (UB). To fix these errors, you can simply change the construction of your data frame class so that "data.frame"
is a base class, i.e. it should come last in the class vector:
class(my_df) <- c("my_class", "data.frame") vctrs::obj_check_vector(my_df) dplyr::slice(my_df, 1)
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