`recipes`

?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.

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 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric predictor original
#> 2 Sepal.Width numeric predictor original
#> 3 Petal.Length numeric predictor original
#> 4 Petal.Width numeric predictor original
#> 5 Species nominal predictor original
#> 6 original nominal 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 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric predictor original
#> 2 Sepal.Width numeric predictor original
#> 3 Petal.Length numeric predictor original
#> 4 Petal.Width numeric predictor original
#> 5 original nominal predictor original
#> 6 Species_versicolor numeric predictor derived
#> 7 Species_virginica numeric predictor derived
# Get a row for each factor level
juice(ref_cell, original, starts_with("Species")) %>% distinct()
#> # A tibble: 3 x 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:

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, the global option for the contrasts can be changed and saved. `step_dummy`

picks up on this and makes the correct calculations:

```
# change it:
go_helmert <- param
go_helmert["unordered"] <- "contr.helmert"
options(contrasts = go_helmert)
# now make dummy variables with new parameterization
helmert <-
iris_rec %>%
step_dummy(Species) %>%
prep(training = iris)
summary(helmert)
#> # A tibble: 7 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric predictor original
#> 2 Sepal.Width numeric predictor original
#> 3 Petal.Length numeric predictor original
#> 4 Petal.Width numeric predictor original
#> 5 original nominal predictor original
#> 6 Species_X1 numeric predictor derived
#> 7 Species_X2 numeric predictor derived
juice(helmert, original, starts_with("Species")) %>% distinct()
#> # A tibble: 3 x 3
#> original Species_X1 Species_X2
#> <fct> <dbl> <dbl>
#> 1 setosa -1 -1
#> 2 versicolor 1 -1
#> 3 virginica 0 2
# Yuk; go back to the original method
options(contrasts = param)
```

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()`

and `juice()`

functions will return the data as-is (e.g. with no dummy variables).

Finally, `step_dummy()`

has an option called `preserve`

that can be used to keep the original column that are being used to create the 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 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric predictor original
#> 2 Sepal.Width numeric predictor original
#> 3 Petal.Length numeric predictor original
#> 4 Petal.Width numeric predictor original
#> 5 Species nominal predictor original
#> 6 original nominal predictor original
#> 7 Sepal.Width_x_Sepal.Length numeric 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 do use `*`

, but only the interactions are recorded as columns that needs to be created.

One thing that `recipes`

does differently than base R is to construct 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 variable?

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

Note 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 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric predictor original
#> 2 Sepal.Width numeric predictor original
#> 3 Petal.Length numeric predictor original
#> 4 Petal.Width numeric predictor original
#> 5 original nominal predictor original
#> 6 Species_versicolor numeric predictor derived
#> 7 Species_virginica numeric predictor derived
#> 8 Species_versicolor_x_Sepal.Length numeric predictor derived
#> 9 Species_virginica_x_Sepal.Length numeric 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 selectors are then translated to an additive function of the results. In this case, that means that

becomes

The entire interaction formula is shown here:

```
iris_int
#> Data Recipe
#>
#> Inputs:
#>
#> role #variables
#> predictor 6
#>
#> Training data contained 150 data points and no missing data.
#>
#> Operations:
#>
#> Dummy variables from Species [trained]
#> Interactions with (Species_versicolor + Species_virginica):Sepal.Length [trained]
```

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

).

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`. Please convert all involved variables to dummy variables first.
summary(iris_int)
#> # A tibble: 8 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric predictor original
#> 2 Sepal.Width numeric predictor original
#> 3 Petal.Length numeric predictor original
#> 4 Petal.Width numeric predictor original
#> 5 Species nominal predictor original
#> 6 original nominal predictor original
#> 7 Speciesversicolor_x_Sepal.Length numeric predictor derived
#> 8 Speciesvirginica_x_Sepal.Length numeric 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.

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) %>%
juice(original, starts_with("Species")) %>%
distinct()
#> # A tibble: 3 x 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 that 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) %>%
juice(original, starts_with("Species")) %>%
distinct()
hot_reference
#> # A tibble: 3 x 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
# from above
options(contrasts = go_helmert)
hot_helmert <-
iris_rec %>%
step_dummy(Species, one_hot = TRUE) %>%
prep(training = iris) %>%
juice(original, starts_with("Species")) %>%
distinct()
hot_helmert
#> # A tibble: 3 x 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.

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 issues (“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.