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
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
|
#' Square Root Transformation
#'
#' `step_sqrt` creates a *specification* of a recipe
#' step that will square root transform the data.
#'
#' @inheritParams step_center
#' @inherit step_center return
#' @param ... One or more selector functions to choose which
#' variables will be transformed. See [selections()] for
#' more details. For the `tidy` method, these are not
#' currently used.
#' @param role Not used by this step since no new variables are
#' created.
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @return An updated version of `recipe` with the new step
#' added to the sequence of existing steps (if any). For the
#' `tidy` method, a tibble with columns `terms` which
#' is the columns that will be affected.
#' @keywords datagen
#' @concept preprocessing
#' @concept transformation_methods
#' @export
#' @examples
#' set.seed(313)
#' examples <- matrix(rnorm(40)^2, ncol = 2)
#' examples <- as.data.frame(examples)
#'
#' rec <- recipe(~ V1 + V2, data = examples)
#'
#' sqrt_trans <- rec %>%
#' step_sqrt(all_predictors())
#'
#' sqrt_obj <- prep(sqrt_trans, training = examples)
#'
#' transformed_te <- bake(sqrt_obj, examples)
#' plot(examples$V1, transformed_te$V1)
#'
#' tidy(sqrt_trans, number = 1)
#' tidy(sqrt_obj, number = 1)
#' @seealso [step_logit()] [step_invlogit()]
#' [step_log()] [step_hyperbolic()] [recipe()]
#' [prep.recipe()] [bake.recipe()]
step_sqrt <- function(recipe, ..., role = NA,
trained = FALSE, columns = NULL,
skip = FALSE,
id = rand_id("sqrt")) {
add_step(
recipe,
step_sqrt_new(
terms = ellipse_check(...),
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
)
}
step_sqrt_new <-
function(terms, role, trained, columns, skip, id) {
step(
subclass = "sqrt",
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_sqrt <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
step_sqrt_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_sqrt <- function(object, new_data, ...) {
col_names <- object$columns
for (i in seq_along(col_names))
new_data[, col_names[i]] <-
sqrt(getElement(new_data, col_names[i]))
as_tibble(new_data)
}
print.step_sqrt <- function(x, width = max(20, options()$width - 29), ...) {
cat("Square root transformation on ", sep = "")
printer(x$columns, x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_sqrt
#' @param x A `step_sqrt` object.
#' @export
tidy.step_sqrt <- function(x, ...) {
res <-simple_terms(x, ...)
res$id <- x$id
res
}
|
