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
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/discretize.R
\name{step_discretize}
\alias{step_discretize}
\title{Discretize Numeric Variables}
\usage{
step_discretize(
recipe,
...,
role = NA,
trained = FALSE,
num_breaks = 4,
min_unique = 10,
objects = NULL,
options = list(prefix = "bin"),
skip = FALSE,
id = rand_id("discretize")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose variables
for this step. See \code{\link[=selections]{selections()}} for more details.}
\item{role}{Not used by this step since no new variables are
created.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{num_breaks}{An integer defining how many cuts to make of the
data.}
\item{min_unique}{An integer defining a sample size line of
dignity for the binning. If (the number of unique
values)\verb{/(cuts+1)} is less than \code{min_unique}, no
discretization takes place.}
\item{objects}{The \code{\link[=discretize]{discretize()}} objects are stored
here once the recipe has be trained by
\code{\link[=prep]{prep()}}.}
\item{options}{A list of options to \code{\link[=discretize]{discretize()}}. A
default is set for the argument \code{x}. Note that using
the options \code{prefix} and \code{labels} when more than one
variable is being transformed might be problematic as all
variables inherit those values.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
when \code{\link[=prep]{prep()}} is run, some operations may not be able to be
conducted on new data (e.g. processing the outcome variable(s)).
Care should be taken when using \code{skip = TRUE} as it may affect
the computations for subsequent operations.}
\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\code{step_discretize} creates a \emph{specification} of a recipe
step that will convert numeric data into a factor with
bins having approximately the same number of data points (based
on a training set).
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with columns
\code{terms} (the selectors or variables selected) and \code{value}
(the breaks) is returned.
}
\section{Case weights}{
The underlying operation does not allow for case weights.
}
\examples{
\dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
data(biomass, package = "modeldata")
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(
HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr
) \%>\%
step_discretize(carbon, hydrogen)
rec <- prep(rec, biomass_tr)
binned_te <- bake(rec, biomass_te)
table(binned_te$carbon)
tidy(rec, 1)
\dontshow{\}) # examplesIf}
}
\seealso{
Other discretization steps:
\code{\link{step_cut}()}
}
\concept{discretization steps}
|
