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
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/slice.R
\name{step_slice}
\alias{step_slice}
\title{Filter rows by position using dplyr}
\usage{
step_slice(
recipe,
...,
role = NA,
trained = FALSE,
inputs = NULL,
skip = TRUE,
id = rand_id("slice")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{Integer row values. See
\code{\link[dplyr:slice]{dplyr::slice()}} 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{inputs}{Quosure of values given by \code{...}.}
\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 = FALSE}.}
\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_slice} creates a \emph{specification} of a recipe step
that will filter rows using \code{\link[dplyr:slice]{dplyr::slice()}}.
}
\details{
When an object in the user's global environment is
referenced in the expression defining the new variable(s),
it is a good idea to use quasiquotation (e.g. \verb{!!})
to embed the value of the object in the expression (to
be portable between sessions). See the examples.
}
\section{Row Filtering}{
This step can entirely remove observations (rows of data), which can have
unintended and/or problematic consequences when applying the step to new
data later via \code{\link[=bake]{bake()}}. Consider whether \code{skip = TRUE} or
\code{skip = FALSE} is more appropriate in any given use case. In most instances
that affect the rows of the data being predicted, this step probably should
not be applied at all; instead, execute operations like this outside and
before starting a preprocessing \code{\link[=recipe]{recipe()}}.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with column
\code{terms} which contains the filtering indices is returned.
}
\section{Case weights}{
The underlying operation does not allow for case weights.
}
\examples{
rec <- recipe(~., data = iris) \%>\%
step_slice(1:3)
prepped <- prep(rec, training = iris \%>\% slice(1:75))
tidy(prepped, number = 1)
library(dplyr)
dplyr_train <-
iris \%>\%
as_tibble() \%>\%
slice(1:75) \%>\%
slice(1:3)
rec_train <- bake(prepped, new_data = NULL)
all.equal(dplyr_train, rec_train)
dplyr_test <-
iris \%>\%
as_tibble() \%>\%
slice(76:150) \%>\%
slice(1:3)
rec_test <- bake(prepped, iris \%>\% slice(76:150))
all.equal(dplyr_test, rec_test)
# Embedding the integer expression (or vector) into the
# recipe:
keep_rows <- 1:6
qq_rec <-
recipe(~., data = iris) \%>\%
# Embed `keep_rows` in the call using !!
step_slice(!!keep_rows) \%>\%
prep(training = iris)
tidy(qq_rec, number = 1)
}
\seealso{
Other row operation steps:
\code{\link{step_arrange}()},
\code{\link{step_filter}()},
\code{\link{step_impute_roll}()},
\code{\link{step_lag}()},
\code{\link{step_naomit}()},
\code{\link{step_sample}()},
\code{\link{step_shuffle}()}
Other dplyr steps:
\code{\link{step_arrange}()},
\code{\link{step_filter}()},
\code{\link{step_mutate_at}()},
\code{\link{step_mutate}()},
\code{\link{step_rename_at}()},
\code{\link{step_rename}()},
\code{\link{step_sample}()},
\code{\link{step_select}()}
}
\concept{dplyr steps}
\concept{row operation steps}
|
