Adds features according to expressions given as formulas that may depend on values of other features. This can add new features, or can change existing features.

## Format

`R6Class`

object inheriting from `PipeOpTaskPreprocSimple`

/`PipeOpTaskPreproc`

/`PipeOp`

.

## Construction

`id`

::`character(1)`

Identifier of resulting object, default`"mutate"`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

## Input and Output Channels

Input and output channels are inherited from `PipeOpTaskPreproc`

.

The output is the input `Task`

with added and/or mutated features according to the `mutation`

parameter.

## State

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

.

## Parameters

The parameters are the parameters inherited from `PipeOpTaskPreproc`

, as well as:

`mutation`

:: named`list`

of`formula`

Expressions for new features to create (or present features to change), in the form of`formula`

. Each element of the list is a`formula`

with the name of the element naming the feature to create or change, and the formula expression determining the result. This expression may reference other features, as well as variables visible at the creation of the`formula`

(see examples). Initialized to`list()`

.`delete_originals`

::`logical(1)`

Whether to delete original features. Even when this is`FALSE`

, present features may still be overwritten. Initialized to`FALSE`

.

## Internals

A `formula`

created using the `~`

operator always contains a reference to the `environment`

in which
the `formula`

is created. This makes it possible to use variables in the `~`

-expressions that both
reference either column names or variable names.

Note that the `formula`

s in `mutation`

are evaluated sequentially. This allows for using
variables that were constructed during evaluation of a previous formula. However, if existing
features are changed, precedence is given to the original ones before the newly constructed ones.

## Fields

Only fields inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

## Methods

Only methods inherited from `PipeOpTaskPreprocSimple`

/`PipeOpTaskPreproc`

/`PipeOp`

.

## See also

https://mlr-org.com/pipeops.html

Other PipeOps:
`PipeOp`

,
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTargetTrafo`

,
`PipeOpTaskPreproc`

,
`PipeOpTaskPreprocSimple`

,
`mlr_pipeops`

,
`mlr_pipeops_adas`

,
`mlr_pipeops_blsmote`

,
`mlr_pipeops_boxcox`

,
`mlr_pipeops_branch`

,
`mlr_pipeops_chunk`

,
`mlr_pipeops_classbalancing`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_classweights`

,
`mlr_pipeops_colapply`

,
`mlr_pipeops_collapsefactors`

,
`mlr_pipeops_colroles`

,
`mlr_pipeops_copy`

,
`mlr_pipeops_datefeatures`

,
`mlr_pipeops_encode`

,
`mlr_pipeops_encodeimpact`

,
`mlr_pipeops_encodelmer`

,
`mlr_pipeops_featureunion`

,
`mlr_pipeops_filter`

,
`mlr_pipeops_fixfactors`

,
`mlr_pipeops_histbin`

,
`mlr_pipeops_ica`

,
`mlr_pipeops_imputeconstant`

,
`mlr_pipeops_imputehist`

,
`mlr_pipeops_imputelearner`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputemode`

,
`mlr_pipeops_imputeoor`

,
`mlr_pipeops_imputesample`

,
`mlr_pipeops_kernelpca`

,
`mlr_pipeops_learner`

,
`mlr_pipeops_missind`

,
`mlr_pipeops_modelmatrix`

,
`mlr_pipeops_multiplicityexply`

,
`mlr_pipeops_multiplicityimply`

,
`mlr_pipeops_nmf`

,
`mlr_pipeops_nop`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_ovrunite`

,
`mlr_pipeops_pca`

,
`mlr_pipeops_proxy`

,
`mlr_pipeops_quantilebin`

,
`mlr_pipeops_randomprojection`

,
`mlr_pipeops_randomresponse`

,
`mlr_pipeops_regravg`

,
`mlr_pipeops_removeconstants`

,
`mlr_pipeops_renamecolumns`

,
`mlr_pipeops_replicate`

,
`mlr_pipeops_rowapply`

,
`mlr_pipeops_scale`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_smotenc`

,
`mlr_pipeops_spatialsign`

,
`mlr_pipeops_subsample`

,
`mlr_pipeops_targetinvert`

,
`mlr_pipeops_targetmutate`

,
`mlr_pipeops_targettrafoscalerange`

,
`mlr_pipeops_textvectorizer`

,
`mlr_pipeops_threshold`

,
`mlr_pipeops_tunethreshold`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_updatetarget`

,
`mlr_pipeops_vtreat`

,
`mlr_pipeops_yeojohnson`

## Examples

```
library("mlr3")
constant = 1
pom = po("mutate")
pom$param_set$values$mutation = list(
Sepal.Length_plus_constant = ~ Sepal.Length + constant,
Sepal.Area = ~ Sepal.Width * Sepal.Length,
Petal.Area = ~ Petal.Width * Petal.Length,
Sepal.Area_plus_Petal.Area = ~ Sepal.Area + Petal.Area
)
pom$train(list(tsk("iris")))[[1]]$data()
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> <fctr> <num> <num> <num> <num>
#> 1: setosa 1.4 0.2 5.1 3.5
#> 2: setosa 1.4 0.2 4.9 3.0
#> 3: setosa 1.3 0.2 4.7 3.2
#> 4: setosa 1.5 0.2 4.6 3.1
#> 5: setosa 1.4 0.2 5.0 3.6
#> ---
#> 146: virginica 5.2 2.3 6.7 3.0
#> 147: virginica 5.0 1.9 6.3 2.5
#> 148: virginica 5.2 2.0 6.5 3.0
#> 149: virginica 5.4 2.3 6.2 3.4
#> 150: virginica 5.1 1.8 5.9 3.0
#> Sepal.Length_plus_constant Sepal.Area Petal.Area
#> <num> <num> <num>
#> 1: 6.1 17.85 0.28
#> 2: 5.9 14.70 0.28
#> 3: 5.7 15.04 0.26
#> 4: 5.6 14.26 0.30
#> 5: 6.0 18.00 0.28
#> ---
#> 146: 7.7 20.10 11.96
#> 147: 7.3 15.75 9.50
#> 148: 7.5 19.50 10.40
#> 149: 7.2 21.08 12.42
#> 150: 6.9 17.70 9.18
#> Sepal.Area_plus_Petal.Area
#> <num>
#> 1: 18.13
#> 2: 14.98
#> 3: 15.30
#> 4: 14.56
#> 5: 18.28
#> ---
#> 146: 32.06
#> 147: 25.25
#> 148: 29.90
#> 149: 33.50
#> 150: 26.88
```