The tidyverse is a bit stricter than base R regarding what kind of objects are considered as vectors (see the user FAQ 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:
To be treated as a vector, the class must either inherit from
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) #>  FALSE
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) #> Error in UseMethod("my_generic"): no applicable method for 'my_generic' applied to an object of class "my_class" my_generic(my_explicit_list) #>  "dispatched!"
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
my_df <- data.frame(x = 1) class(my_df) <- c("data.frame", "my_class") vctrs::vec_assert(my_df) #> Error: `my_df` must be a vector, not a <data.frame/my_class> object.
This is problematic as many tidyverse functions won’t work properly:
dplyr::slice(my_df, 1) #> Error: Input must be a vector, not a <data.frame/my_class> object.
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: