step_mutate_at() creates a specification of a recipe step that will
modify the selected variables using a common function via
dplyr::mutate_at().
Usage
step_mutate_at(
recipe,
...,
fn,
role = "predictor",
trained = FALSE,
inputs = NULL,
skip = FALSE,
id = rand_id("mutate_at")
)Arguments
- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.
- ...
One or more selector functions to choose variables for this step. See
selections()for more details.- fn
A function fun, a quosure style lambda `~ fun(.)“ or a list of either form. (see
dplyr::mutate_at()). Note that this argument must be named.- role
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
- trained
A logical to indicate if the quantities for preprocessing have been estimated.
- inputs
A vector of column names populated by
prep().- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()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 usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe with the new step added to the
sequence of any existing operations.
Details
When using this flexible step, use extra care to avoid data leakage in your
preprocessing. Consider, for example, the transformation x = w > mean(w).
When applied to new data or testing data, this transformation would use the
mean of w from the new data, not the mean of w from the training data.
Tidying
When you tidy() this step, a tibble is returned with
columns terms and id:
- terms
character, the selectors or variables selected
- id
character, id of this step
See also
Other multivariate transformation steps:
step_classdist(),
step_classdist_shrunken(),
step_depth(),
step_geodist(),
step_ica(),
step_isomap(),
step_kpca(),
step_kpca_poly(),
step_kpca_rbf(),
step_nnmf(),
step_nnmf_sparse(),
step_pca(),
step_pls(),
step_ratio(),
step_spatialsign()
Other dplyr steps:
step_arrange(),
step_filter(),
step_mutate(),
step_rename(),
step_rename_at(),
step_sample(),
step_select(),
step_slice()
Examples
library(dplyr)
recipe(~., data = iris) %>%
step_mutate_at(contains("Length"), fn = ~ 1 / .) %>%
prep() %>%
bake(new_data = NULL) %>%
slice(1:10)
#> # A tibble: 10 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 0.196 3.5 0.714 0.2 setosa
#> 2 0.204 3 0.714 0.2 setosa
#> 3 0.213 3.2 0.769 0.2 setosa
#> 4 0.217 3.1 0.667 0.2 setosa
#> 5 0.2 3.6 0.714 0.2 setosa
#> 6 0.185 3.9 0.588 0.4 setosa
#> 7 0.217 3.4 0.714 0.3 setosa
#> 8 0.2 3.4 0.667 0.2 setosa
#> 9 0.227 2.9 0.714 0.2 setosa
#> 10 0.204 3.1 0.667 0.1 setosa
recipe(~., data = iris) %>%
# leads to more columns being created.
step_mutate_at(contains("Length"), fn = list(log = log, sqrt = sqrt)) %>%
prep() %>%
bake(new_data = NULL) %>%
slice(1:10)
#> # A tibble: 10 × 9
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 4 more variables: Sepal.Length_log <dbl>, Petal.Length_log <dbl>,
#> # Sepal.Length_sqrt <dbl>, Petal.Length_sqrt <dbl>