These make it clear that we need to use the tibble behavior when joining,
concatenating or casting skim_df objects. For a better discussion, on
why this is important and how these functions work, see:
https://vctrs.r-lib.org/reference/howto-faq-coercion-data-frame.html.
# S3 method for skim_df.skim_df
vec_ptype2(x, y, ...)# S3 method for skim_df.tbl_df
vec_ptype2(x, y, ...)
# S3 method for tbl_df.skim_df
vec_ptype2(x, y, ...)
# S3 method for skim_df.skim_df
vec_cast(x, to, ...)
# S3 method for skim_df.tbl_df
vec_cast(x, to, ...)
# S3 method for tbl_df.skim_df
vec_cast(x, to, ...)
vec_ptype2.* handles finding common prototypes between skim_df and
similar objects. vec_cast.* handles casting between objects. Note that
as of dplyr 1.0.2, dplyr::bind_rows() does not full support combining
attributes and vctrs::vec_rbind() is preferred instead.