# Handling categorical predictors

Recipes can be different from their base R counterparts such as `model.matrix`. This vignette describes the different methods for encoding categorical predictors with special attention to interaction terms and contrasts.

## Creating Dummy Variables

Let’s start, of course, with `iris` data. This has four numeric columns and a single factor column with three levels: `'setosa'`, `'versicolor'`, and `'virginica'`. Our initial recipe will have no outcome:

    library(recipes)

    # make a copy for use below
    iris <- iris |> mutate(original = Species)

    iris_rec <- recipe( ~ ., data = iris)
    summary(iris_rec)
    #> # A tibble: 6 × 4
    #>   variable     type      role      source
    #>   <chr>        <list>    <chr>     <chr>
    #> 1 Sepal.Length <chr [2]> predictor original
    #> 2 Sepal.Width  <chr [2]> predictor original
    #> 3 Petal.Length <chr [2]> predictor original
    #> 4 Petal.Width  <chr [2]> predictor original
    #> 5 Species      <chr [3]> predictor original
    #> 6 original     <chr [3]> predictor original

A contrast function in R is a method for translating a column with categorical values into one or more numeric columns that take the place of the original. This can also be known as an encoding method or a parameterization function.

The default approach is to create dummy variables using the “reference cell” parameterization. This means that, if there are C levels of the factor, there will be C - 1 dummy variables created and all but the first factor level are made into new columns:

    ref_cell <-
      iris_rec |>
      step_dummy(Species) |>
      prep(training = iris)
    summary(ref_cell)
    #> # A tibble: 7 × 4
    #>   variable           type      role      source
    #>   <chr>              <list>    <chr>     <chr>
    #> 1 Sepal.Length       <chr [2]> predictor original
    #> 2 Sepal.Width        <chr [2]> predictor original
    #> 3 Petal.Length       <chr [2]> predictor original
    #> 4 Petal.Width        <chr [2]> predictor original
    #> 5 original           <chr [3]> predictor original
    #> 6 Species_versicolor <chr [2]> predictor derived
    #> 7 Species_virginica  <chr [2]> predictor derived

    # Get a row for each factor level
    bake(ref_cell, new_data = NULL, original, starts_with("Species")) |> distinct()
    #> # A tibble: 3 × 3
    #>   original   Species_versicolor Species_virginica
    #>   <fct>                   <dbl>             <dbl>
    #> 1 setosa                      0                 0
    #> 2 versicolor                  1                 0
    #> 3 virginica                   0                 1

Note that the column that was used to make the new columns (`Species`) is no longer there. See the section below on obtaining the entire set of C columns.

There are different types of contrasts that can be used for different types of factors. The defaults are `"contr.treatment"` for unordered and `"contr.poly"` for ordered factors.

Looking at `?contrast`, there are other options. One alternative is the little known Helmert contrast:

> `contr.helmert` returns Helmert contrasts, which contrast the second level with the first, the third with the average of the first two, and so on.

To get this encoding you can use the `contrasts` argument like so:, the global option for the contrasts can be changed and saved. `step_dummy` picks up on this and makes the correct calculations:

    # now make dummy variables with new parameterization
    helmert <-
      iris_rec |>
      step_dummy(Species, contrasts = "contr.helmert") |>
      prep(training = iris)
    summary(helmert)
    #> # A tibble: 7 × 4
    #>   variable           type      role      source
    #>   <chr>              <list>    <chr>     <chr>
    #> 1 Sepal.Length       <chr [2]> predictor original
    #> 2 Sepal.Width        <chr [2]> predictor original
    #> 3 Petal.Length       <chr [2]> predictor original
    #> 4 Petal.Width        <chr [2]> predictor original
    #> 5 original           <chr [3]> predictor original
    #> 6 Species_versicolor <chr [2]> predictor derived
    #> 7 Species_virginica  <chr [2]> predictor derived

    bake(helmert, new_data = NULL, original, starts_with("Species")) |> distinct()
    #> # A tibble: 3 × 3
    #>   original   Species_versicolor Species_virginica
    #>   <fct>                   <dbl>             <dbl>
    #> 1 setosa                     -1                -1
    #> 2 versicolor                  1                -1
    #> 3 virginica                   0                 2

Note that the column names do not reference a specific level of the species variable. This contrast function has columns that can involve multiple levels; level-specific columns wouldn’t make sense.

If no columns are selected (perhaps due to an earlier `step_zv()`), the `bake()` function will return the data as-is (e.g. with no dummy variables).

Finally, `step_dummy()` has an option called `keep_original_cols` that can be used to keep the original columns that are being used to create the dummy variables.

## Interactions with Dummy Variables

Creating interactions with recipes requires the use of a model formula, such as

    iris_int <-
      iris_rec |>
      step_interact( ~ Sepal.Width:Sepal.Length) |>
      prep(training = iris)
    summary(iris_int)
    #> # A tibble: 7 × 4
    #>   variable                   type      role      source
    #>   <chr>                      <list>    <chr>     <chr>
    #> 1 Sepal.Length               <chr [2]> predictor original
    #> 2 Sepal.Width                <chr [2]> predictor original
    #> 3 Petal.Length               <chr [2]> predictor original
    #> 4 Petal.Width                <chr [2]> predictor original
    #> 5 Species                    <chr [3]> predictor original
    #> 6 original                   <chr [3]> predictor original
    #> 7 Sepal.Width_x_Sepal.Length <chr [2]> predictor derived

In R model formulae, using a `*` between two variables would expand to `a*b = a + b + a:b` so that the main effects are included. In `step_interact`, you can use `*`, but only the interactions are recorded as columns that need to be created.

One thing that `recipes` does differently than base R is it constructs the design matrix in sequential iterations. This is relevant when thinking about interactions between continuous and categorical predictors.

For example, if you were to use the standard formula interface, the creation of the dummy variables happens at the same time as the interactions are created:

    model.matrix(~ Species*Sepal.Length, data = iris) |>
      as.data.frame() |>
      # show a few specific rows
      slice(c(1, 51, 101)) |>
      as.data.frame()
    #>     (Intercept) Speciesversicolor Speciesvirginica Sepal.Length
    #> 1             1                 0                0          5.1
    #> 51            1                 1                0          7.0
    #> 101           1                 0                1          6.3
    #>     Speciesversicolor:Sepal.Length Speciesvirginica:Sepal.Length
    #> 1                                0                           0.0
    #> 51                               7                           0.0
    #> 101                              0                           6.3

With recipes, you create them sequentially. This raises an issue: do I have to type out all of the interaction effects by their specific names when using dummy variables?

    # Must I do this?
    iris_rec |>
      step_interact( ~ Species_versicolor:Sepal.Length +
                       Species_virginica:Sepal.Length)

Not only is this a pain, but it may not be obvious what dummy variables are available (especially when `step_other` is used).

The solution is to use a selector:

    iris_int <-
      iris_rec |>
      step_dummy(Species) |>
      step_interact( ~ starts_with("Species"):Sepal.Length) |>
      prep(training = iris)
    summary(iris_int)
    #> # A tibble: 9 × 4
    #>   variable                          type      role      source
    #>   <chr>                             <list>    <chr>     <chr>
    #> 1 Sepal.Length                      <chr [2]> predictor original
    #> 2 Sepal.Width                       <chr [2]> predictor original
    #> 3 Petal.Length                      <chr [2]> predictor original
    #> 4 Petal.Width                       <chr [2]> predictor original
    #> 5 original                          <chr [3]> predictor original
    #> 6 Species_versicolor                <chr [2]> predictor derived
    #> 7 Species_virginica                 <chr [2]> predictor derived
    #> 8 Species_versicolor_x_Sepal.Length <chr [2]> predictor derived
    #> 9 Species_virginica_x_Sepal.Length  <chr [2]> predictor derived

What happens here is that `starts_with("Species")` is executed on the data that are available when the previous steps have been applied to the data. That means that the dummy variable columns are present. The results of this selector are then translated to an additive function of the results. In this case, that means that

    starts_with("Species")

becomes

    (Species_versicolor + Species_virginica)

The entire interaction formula is shown here:

    iris_int

For interactions between multiple sets of dummy variables, the formula could include multiple selectors (e.g. `starts_with("x_"):starts_with("y_")`).

## Warning!

Would it work if I didn’t convert species to a factor and used the interactions step?

    iris_int <-
      iris_rec |>
      step_interact( ~ Species:Sepal.Length) |>
      prep(training = iris)
    #> Warning: Categorical variables used in `step_interact()` should probably be avoided;
    #> This can lead to differences in dummy variable values that are produced by
    #> ?step_dummy (`?recipes::step_dummy()`). Please convert all involved variables
    #> to dummy variables first.
    summary(iris_int)
    #> # A tibble: 8 × 4
    #>   variable                         type      role      source
    #>   <chr>                            <list>    <chr>     <chr>
    #> 1 Sepal.Length                     <chr [2]> predictor original
    #> 2 Sepal.Width                      <chr [2]> predictor original
    #> 3 Petal.Length                     <chr [2]> predictor original
    #> 4 Petal.Width                      <chr [2]> predictor original
    #> 5 Species                          <chr [3]> predictor original
    #> 6 original                         <chr [3]> predictor original
    #> 7 Speciesversicolor_x_Sepal.Length <chr [2]> predictor derived
    #> 8 Speciesvirginica_x_Sepal.Length  <chr [2]> predictor derived

The columns `Species` isn’t affected and a warning is issued. Basically, you only get half of what `model.matrix` does and that could really be problematic in subsequent steps.

## Getting All of the Indicator Variables

As mentioned above, if there are C levels of the factor, there will be C - 1 dummy variables created. You might want to get all of them back.

Historically, C - 1 are used so that a linear dependency is avoided in the design matrix; all C dummy variables would add up row-wise to the intercept column and the inverse matrix for linear regression can’t be computed. This technical term for a the design matrix like this is “less than full rank”.

There are models (e.g. `glmnet` and others) that can avoid this issue so you might want to get all of the columns. To do this, `step_dummy` has an option called `one_hot` that will make sure that all C are produced:

    iris_rec |>
      step_dummy(Species, one_hot = TRUE) |>
      prep(training = iris) |>
      bake(original, new_data = NULL, starts_with("Species")) |>
      distinct()
    #> # A tibble: 3 × 4
    #>   original   Species_setosa Species_versicolor Species_virginica
    #>   <fct>               <dbl>              <dbl>             <dbl>
    #> 1 setosa                  1                  0                 0
    #> 2 versicolor              0                  1                 0
    #> 3 virginica               0                  0                 1

The option is named that way since this is what the computer scientists call “one-hot encoding”.

Warning! (again)

This will give you the full set of indicators and, when you use the typical contrast function, it does. It might do some seemingly weird (but legitimate) things when used with other contrasts:

    hot_reference <-
      iris_rec |>
      step_dummy(Species, one_hot = TRUE) |>
      prep(training = iris) |>
      bake(original, new_data = NULL, starts_with("Species")) |>
      distinct()

    hot_reference
    #> # A tibble: 3 × 4
    #>   original   Species_setosa Species_versicolor Species_virginica
    #>   <fct>               <dbl>              <dbl>             <dbl>
    #> 1 setosa                  1                  0                 0
    #> 2 versicolor              0                  1                 0
    #> 3 virginica               0                  0                 1

    hot_helmert <-
      iris_rec |>
      step_dummy(Species, one_hot = TRUE, contrasts = "contr.helmert") |>
      prep(training = iris) |>
      bake(original, new_data = NULL, starts_with("Species")) |>
      distinct()

    hot_helmert
    #> # A tibble: 3 × 4
    #>   original   Species_setosa Species_versicolor Species_virginica
    #>   <fct>               <dbl>              <dbl>             <dbl>
    #> 1 setosa                  1                  0                 0
    #> 2 versicolor              0                  1                 0
    #> 3 virginica               0                  0                 1

Since this contrast doesn’t make sense using all C columns, it reverts back to the default encoding.

## Novel Levels

When a recipe is used with new samples, some factors may have acquired new levels that were not present when `prep` was run. If `step_dummy` encounters this situation, a warning is issued (“There are new levels in a factor”) and the indicator variables that correspond to the factor are assigned missing values.

One way around this is to use `step_other`. This step can convert infrequently occurring levels to a new category (that defaults to “other”). This step can also be used to convert new factor levels to “other” also.

Also, `step_integer` has functionality similar to `LabelEncoder` and encodes new values as zero.

The `embed` package can also handle novel factors levels within a recipe. `step_embed` and `step_tfembed` assign a common numeric score to novel levels.

## Other Steps Related to Dummy Variables

There are a bunch of steps related to going in-between factors and dummy variables:

- `step_unknown` assigns missing factor values into an `'unknown'` category.
- `step_other` can collapse infrequently occurring levels into `'other'`.
- `step_regex` will create a single dummy variable based on applying a regular expression to a text field. Similarly, `step_count` does the same but counts the occurrences of the pattern in the string.
- `step_holiday` creates dummy variables from date fields to capture holidays.
- `step_lincomb` can be useful if you over-specify interactions and need to remove linear dependencies.
- `step_zv` can remove dummy variables that never show a 1 in the column (i.e. is zero-variance).
- `step_bin2factor` takes a binary indicator and makes a factor variable. This can be useful when using naive Bayes models.
- `step_embed`, `step_lencode_glm`, `step_lencode_bayes` and others in the `embed` package can use one or more (non-binary) values to encode factor predictors into a numeric form.
- `step_dummy_extract` can create binary indicators from strings and is especially useful for multiple choice columns.

`step_dummy` also works with ordered factors. As seen above, the default encoding is to create a series of polynomial variables. There are also a few steps for ordered factors:

- `step_ordinalscore` can translate the levels to a single numeric score.
- `step_unorder` can convert to an unordered factor.