# Generated by 02-duckplyr_df-methods.R
#' @export
rows_delete.duckplyr_df <- function(x, y, by = NULL, ..., unmatched = c("error", "ignore"), copy = FALSE, in_place = FALSE) {
# Our implementation
rel_try(NULL,
"No relational implementation for rows_delete()" = TRUE,
{
return(out)
}
)
# dplyr forward
rows_delete <- dplyr$rows_delete.data.frame
out <- rows_delete(x, y, by, ..., unmatched = unmatched, copy = copy, in_place = in_place)
return(out)
# dplyr implementation
check_dots_empty()
rows_df_in_place(in_place)
y <- auto_copy(x, y, copy = copy)
by <- rows_check_by(by, y)
rows_check_contains_by(x, by, "x")
rows_check_contains_by(y, by, "y")
x_key <- dplyr_col_select(x, by)
y_key <- dplyr_col_select(y, by)
args <- vec_cast_common(x = x_key, y = y_key)
x_key <- args$x
y_key <- args$y
keep <- rows_check_y_unmatched(x_key, y_key, unmatched)
if (!is.null(keep)) {
y_key <- dplyr_row_slice(y_key, keep)
}
extra <- setdiff(names(y), names(y_key))
if (!is_empty(extra)) {
message <- glue("Ignoring extra `y` columns: ", commas(tick_if_needed(extra)))
inform(message, class = c("dplyr_message_delete_extra_cols", "dplyr_message"))
}
loc <- vec_match(x_key, y_key)
unmatched <- is.na(loc)
x_loc <- which(unmatched)
dplyr_row_slice(x, x_loc)
}
duckplyr_rows_delete <- function(x, y, ...) {
try_fetch(
{
x <- as_duckplyr_df(x)
y <- as_duckplyr_df(y)
},
error = function(e) {
testthat::skip(conditionMessage(e))
}
)
out <- rows_delete(x, y, ...)
class(out) <- setdiff(class(out), "duckplyr_df")
out
}
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