1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
|
#' Remove observations with missing values
#'
#' `step_naomit` creates a *specification* of a recipe step that
#' will remove observations (rows of data) if they contain `NA`
#' or `NaN` values.
#'
#' @template row-ops
#' @inheritParams step_center
#' @param role Unused, include for consistency with other steps.
#' @param trained A logical to indicate if the quantities for preprocessing
#' have been estimated. Again included for consistency.
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#'
#' @template case-weights-not-supported
#'
#' @template step-return
#' @family row operation steps
#' @export
#'
#' @examples
#'
#' recipe(Ozone ~ ., data = airquality) %>%
#' step_naomit(Solar.R) %>%
#' prep(airquality, verbose = FALSE) %>%
#' bake(new_data = NULL)
step_naomit <- function(recipe, ..., role = NA, trained = FALSE,
columns = NULL, skip = TRUE,
id = rand_id("naomit")) {
add_step(
recipe,
step_naomit_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
)
}
step_naomit_new <- function(terms, role, trained, columns, skip, id) {
step(
subclass = "naomit",
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_naomit <- function(x, training, info = NULL, ...) {
step_naomit_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = recipes_eval_select(x$terms, training, info),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_naomit <- function(object, new_data, ...) {
columns <- object$columns
columns <- unname(columns)
tibble::as_tibble(tidyr::drop_na(new_data, tidyselect::all_of(columns)))
}
print.step_naomit <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Removing rows with NA values in "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_naomit <- function(x, ...) {
res <- simple_terms(x, ...)
res$id <- x$id
res
}
|
