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For a recipe with at least one preprocessing operation that has been trained by prep(), apply the computations to new data.

Usage

bake(object, ...)

# S3 method for recipe
bake(object, new_data, ..., composition = "tibble")

Arguments

object

A trained object such as a recipe() with at least one preprocessing operation.

...

One or more selector functions to choose which variables will be returned by the function. See selections() for more details. If no selectors are given, the default is to use everything().

new_data

A data frame or tibble for whom the preprocessing will be applied. If NULL is given to new_data, the pre-processed training data will be returned (assuming that prep(retain = TRUE) was used).

composition

Either "tibble", "matrix", "data.frame", or "dgCMatrix" for the format of the processed data set. Note that all computations during the baking process are done in a non-sparse format. Also, note that this argument should be called after any selectors and the selectors should only resolve to numeric columns (otherwise an error is thrown).

Value

A tibble, matrix, or sparse matrix that may have different columns than the original columns in new_data.

Details

bake() takes a trained recipe and applies its operations to a data set to create a design matrix. If you are using a recipe as a preprocessor for modeling, we highly recommend that you use a workflow() instead of manually applying a recipe (see the example in recipe()).

If the data set is not too large, time can be saved by using the retain = TRUE option of prep(). This stores the processed version of the training set. With this option set, bake(object, new_data = NULL) will return it for free.

Also, any steps with skip = TRUE will not be applied to the data when bake() is invoked with a data set in new_data. bake(object, new_data = NULL) will always have all of the steps applied.

See also

Examples

data(ames, package = "modeldata")

ames <- mutate(ames, Sale_Price = log10(Sale_Price))

ames_rec <-
  recipe(Sale_Price ~ ., data = ames[-(1:6), ]) %>%
  step_other(Neighborhood, threshold = 0.05) %>%
  step_dummy(all_nominal()) %>%
  step_interact(~ starts_with("Central_Air"):Year_Built) %>%
  step_ns(Longitude, Latitude, deg_free = 2) %>%
  step_zv(all_predictors()) %>%
  prep()

# return the training set (already embedded in ames_rec)
bake(ames_rec, new_data = NULL)
#> # A tibble: 2,924 × 259
#>    Lot_Frontage Lot_Area Year_Built Year_Remod_Add Mas_Vnr_Area
#>           <dbl>    <int>      <int>          <int>        <dbl>
#>  1           41     4920       2001           2001            0
#>  2           43     5005       1992           1992            0
#>  3           39     5389       1995           1996            0
#>  4           60     7500       1999           1999            0
#>  5           75    10000       1993           1994            0
#>  6            0     7980       1992           2007            0
#>  7           63     8402       1998           1998            0
#>  8           85    10176       1990           1990            0
#>  9            0     6820       1985           1985            0
#> 10           47    53504       2003           2003          603
#> # … with 2,914 more rows, and 254 more variables: BsmtFin_SF_1 <dbl>,
#> #   BsmtFin_SF_2 <dbl>, Bsmt_Unf_SF <dbl>, Total_Bsmt_SF <dbl>,
#> #   First_Flr_SF <int>, Second_Flr_SF <int>, Gr_Liv_Area <int>,
#> #   Bsmt_Full_Bath <dbl>, Bsmt_Half_Bath <dbl>, Full_Bath <int>,
#> #   Half_Bath <int>, Bedroom_AbvGr <int>, Kitchen_AbvGr <int>,
#> #   TotRms_AbvGrd <int>, Fireplaces <int>, Garage_Cars <dbl>,
#> #   Garage_Area <dbl>, Wood_Deck_SF <int>, Open_Porch_SF <int>, …

# apply processing to other data:
bake(ames_rec, new_data = head(ames))
#> # A tibble: 6 × 259
#>   Lot_Frontage Lot_Area Year_Built Year_Remod_Add Mas_Vnr_Area
#>          <dbl>    <int>      <int>          <int>        <dbl>
#> 1          141    31770       1960           1960          112
#> 2           80    11622       1961           1961            0
#> 3           81    14267       1958           1958          108
#> 4           93    11160       1968           1968            0
#> 5           74    13830       1997           1998            0
#> 6           78     9978       1998           1998           20
#> # … with 254 more variables: BsmtFin_SF_1 <dbl>, BsmtFin_SF_2 <dbl>,
#> #   Bsmt_Unf_SF <dbl>, Total_Bsmt_SF <dbl>, First_Flr_SF <int>,
#> #   Second_Flr_SF <int>, Gr_Liv_Area <int>, Bsmt_Full_Bath <dbl>,
#> #   Bsmt_Half_Bath <dbl>, Full_Bath <int>, Half_Bath <int>,
#> #   Bedroom_AbvGr <int>, Kitchen_AbvGr <int>, TotRms_AbvGrd <int>,
#> #   Fireplaces <int>, Garage_Cars <dbl>, Garage_Area <dbl>,
#> #   Wood_Deck_SF <int>, Open_Porch_SF <int>, Enclosed_Porch <int>, …

# only return selected variables:
bake(ames_rec, new_data = head(ames), all_numeric_predictors())
#> # A tibble: 6 × 258
#>   Lot_Frontage Lot_Area Year_Built Year_Remod_Add Mas_Vnr_Area
#>          <dbl>    <int>      <int>          <int>        <dbl>
#> 1          141    31770       1960           1960          112
#> 2           80    11622       1961           1961            0
#> 3           81    14267       1958           1958          108
#> 4           93    11160       1968           1968            0
#> 5           74    13830       1997           1998            0
#> 6           78     9978       1998           1998           20
#> # … with 253 more variables: BsmtFin_SF_1 <dbl>, BsmtFin_SF_2 <dbl>,
#> #   Bsmt_Unf_SF <dbl>, Total_Bsmt_SF <dbl>, First_Flr_SF <int>,
#> #   Second_Flr_SF <int>, Gr_Liv_Area <int>, Bsmt_Full_Bath <dbl>,
#> #   Bsmt_Half_Bath <dbl>, Full_Bath <int>, Half_Bath <int>,
#> #   Bedroom_AbvGr <int>, Kitchen_AbvGr <int>, TotRms_AbvGrd <int>,
#> #   Fireplaces <int>, Garage_Cars <dbl>, Garage_Area <dbl>,
#> #   Wood_Deck_SF <int>, Open_Porch_SF <int>, Enclosed_Porch <int>, …
bake(ames_rec, new_data = head(ames), starts_with(c("Longitude", "Latitude")))
#> # A tibble: 6 × 4
#>   Longitude_ns_1 Longitude_ns_2 Latitude_ns_1 Latitude_ns_2
#>            <dbl>          <dbl>         <dbl>         <dbl>
#> 1          0.570       -0.0141          0.472         0.394
#> 2          0.570       -0.0142          0.481         0.360
#> 3          0.569       -0.00893         0.484         0.348
#> 4          0.563        0.0212          0.496         0.301
#> 5          0.562       -0.212           0.405         0.634
#> 6          0.562       -0.212           0.407         0.630