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#' Check if all Columns are Present
#'
#' `check_cols` creates a *specification* of a recipe
#' step that will check if all the columns of the training frame are
#' present in the new data.
#'
#' @inheritParams check_missing
#' @template check-return
#' @family checks
#' @export
#' @details This check will break the `bake` function if any of the specified
#' columns is not present in the data. If the check passes, nothing is changed
#' to the data.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this check, a tibble with columns
#' `terms` (the selectors or variables selected) and `value` (the type)
#' is returned.
#' @examplesIf rlang::is_installed("modeldata")
#' data(biomass, package = "modeldata")
#'
#' biomass_rec <- recipe(HHV ~ ., data = biomass) %>%
#' step_rm(sample, dataset) %>%
#' check_cols(contains("gen")) %>%
#' step_center(all_numeric_predictors())
#' \dontrun{
#' bake(biomass_rec, biomass[, c("carbon", "HHV")])
#' }
check_cols <-
function(recipe,
...,
role = NA,
trained = FALSE,
skip = FALSE,
id = rand_id("cols")) {
add_check(
recipe,
check_cols_new(
terms = enquos(...),
role = role,
trained = trained,
columns = NULL,
skip = skip,
id = id
)
)
}
check_cols_new <-
function(terms, role, trained, columns, skip, id) {
check(
subclass = "cols",
prefix = "check_",
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
prep.check_cols <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_cols_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id
)
}
bake.check_cols <- function(object, new_data, ...) {
original_cols <- object$columns
new_cols <- names(new_data)
missing <- setdiff(original_cols, new_cols)
if (length(missing) > 0) {
mis_cols <- paste(paste0("`", missing, "`"), collapse = ", ")
rlang::abort(
paste0(
"The following cols are missing from `new_data`: ",
mis_cols,
"."
)
)
}
new_data
}
print.check_cols <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Check if the following columns are present: "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.check_cols <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = unname(x$columns))
} else {
res <- tibble(terms = sel2char(x$terms))
}
res$id <- x$id
res
}
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