Selecting variables
Description: You can select which variables or features should be used in recipes. This vignette goes over the basics of using selection functions.

When recipe steps are used, there are different approaches that can be used to select which variables or features should be used.

The three main characteristics of variables that can be queried:

- the name of the variable
- the data type (e.g. numeric or nominal)
- the role that was declared by the recipe

The manual pages for `?selections` and `?has_role` have details about the available selection methods.

To illustrate this, the palmer penguins data will be used:

    library(recipes)
    library(modeldata)

    data("penguins")
    str(penguins)
    #> tibble [344 × 7] (S3: tbl_df/tbl/data.frame)
    #>  $ species          : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
    #>  $ island           : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
    #>  $ bill_length_mm   : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
    #>  $ bill_depth_mm    : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
    #>  $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
    #>  $ body_mass_g      : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
    #>  $ sex              : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...

    rec <- recipe(body_mass_g ~ ., data = penguins)
    rec

Before any steps are used the information on the original variables is:

    summary(rec, original = TRUE)
    #> # A tibble: 7 × 5
    #>   variable          type      role      source   required_to_bake
    #>   <chr>             <list>    <chr>     <chr>    <lgl>
    #> 1 species           <chr [3]> predictor original TRUE
    #> 2 island            <chr [3]> predictor original TRUE
    #> 3 bill_length_mm    <chr [2]> predictor original TRUE
    #> 4 bill_depth_mm     <chr [2]> predictor original TRUE
    #> 5 flipper_length_mm <chr [2]> predictor original TRUE
    #> 6 sex               <chr [3]> predictor original TRUE
    #> 7 body_mass_g       <chr [2]> outcome   original FALSE

This shows the types and roles. Each variable can have one or more types, so we can printing them out seperately

    summary(rec, original = TRUE)$type
    #> [[1]]
    #> [1] "factor"    "unordered" "nominal"
    #>
    #> [[2]]
    #> [1] "factor"    "unordered" "nominal"
    #>
    #> [[3]]
    #> [1] "double"  "numeric"
    #>
    #> [[4]]
    #> [1] "double"  "numeric"
    #>
    #> [[5]]
    #> [1] "integer" "numeric"
    #>
    #> [[6]]
    #> [1] "factor"    "unordered" "nominal"
    #>
    #> [[7]]
    #> [1] "integer" "numeric"

Notice that integer variables have roles `"integer"` and `"numeric"`, and the factor variables have roles `"factor"`, `"unordered"`, `"nominal"`. This allows for some neat selections where the selector `all_numeric()` select double and integer variables, and more specific selectors such as `all_integer()` only select integer variables. A full hierarchy of types can be seen in `?has_role`.

We can add a step to normalize numeric data:

    dummied <- rec |> step_normalize(all_numeric())

This will capture any variables that are either character integers or doubles: `bill_length_mm`, `bill_depth_mm`, `flipper_length_mm` and `body_mass_g`. However, since `body_mass_g` is our outcome, we might want to keep it as a factor so we can subtract that variable out either by name or by role:

    dummied <- rec |> step_normalize(bill_length_mm, bill_depth_mm,
                                      flipper_length_mm) # or
    dummied <- rec |> step_normalize(all_numeric(), - body_mass_g) # or
    dummied <- rec |> step_normalize(all_numeric_predictors()) # recommended

Whenever possible, it is recommended to use the more specific `*_predictors()` variants to avoid accidentally selecting the outcomes.

    rec |>
      step_dummy(sex) |>
      prep() |>
      juice()
    #> Warning: ! There are new levels in `sex`: NA.
    #> ℹ Consider using step_unknown() (`?recipes::step_unknown()`) before
    #>   `step_dummy()` to handle missing values.
    #> # A tibble: 344 × 7
    #>    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
    #>    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
    #>  1 Adelie  Torgersen           39.1          18.7               181        3750
    #>  2 Adelie  Torgersen           39.5          17.4               186        3800
    #>  3 Adelie  Torgersen           40.3          18                 195        3250
    #>  4 Adelie  Torgersen           NA            NA                  NA          NA
    #>  5 Adelie  Torgersen           36.7          19.3               193        3450
    #>  6 Adelie  Torgersen           39.3          20.6               190        3650
    #>  7 Adelie  Torgersen           38.9          17.8               181        3625
    #>  8 Adelie  Torgersen           39.2          19.6               195        4675
    #>  9 Adelie  Torgersen           34.1          18.1               193        3475
    #> 10 Adelie  Torgersen           42            20.2               190        4250
    #> # ℹ 334 more rows
    #> # ℹ 1 more variable: sex_male <dbl>

Using the last definition:

    dummied <- prep(dummied, training = penguins)
    with_dummy <- bake(dummied, new_data = penguins)
    with_dummy
    #> # A tibble: 344 × 7
    #>    species island    bill_length_mm bill_depth_mm flipper_length_mm sex
    #>    <fct>   <fct>              <dbl>         <dbl>             <dbl> <fct>
    #>  1 Adelie  Torgersen         -0.883         0.784            -1.42  male
    #>  2 Adelie  Torgersen         -0.810         0.126            -1.06  female
    #>  3 Adelie  Torgersen         -0.663         0.430            -0.421 female
    #>  4 Adelie  Torgersen         NA            NA                NA     <NA>
    #>  5 Adelie  Torgersen         -1.32          1.09             -0.563 female
    #>  6 Adelie  Torgersen         -0.847         1.75             -0.776 male
    #>  7 Adelie  Torgersen         -0.920         0.329            -1.42  female
    #>  8 Adelie  Torgersen         -0.865         1.24             -0.421 male
    #>  9 Adelie  Torgersen         -1.80          0.480            -0.563 <NA>
    #> 10 Adelie  Torgersen         -0.352         1.54             -0.776 <NA>
    #> # ℹ 334 more rows
    #> # ℹ 1 more variable: body_mass_g <int>

`body_mass_g` is unaffected.

One important aspect of selecting variables in steps is that the variable names and types may change as steps are being executed. In the above example, `sex` is a factor variable, if `step_dummy()` was used on it, then `sex` would be removed and the binary variable `sex_male` is in its place. One reason to have general selection routines like `all_predictors()` or `contains()` is to be able to select variables that have not been created yet.

All steps in the recipes package support empty selections. Meaning that if `all_date_predictors()` is used in a step, and no date variables was found the in the data set, then the step is applied without error. The calculations inside the step will be skipped. This allows for quite relaxed recipes as you don’t have to make sure that the variables exists at that point in the recipe.