`step_interact`

creates a *specification* of a recipe
step that will create new columns that are interaction terms
between two or more variables.

```
step_interact(
recipe,
terms,
role = "predictor",
trained = FALSE,
objects = NULL,
sep = "_x_",
skip = FALSE,
id = rand_id("interact")
)
```

recipe | A recipe object. The step will be added to the sequence of operations for this recipe. |
---|---|

terms | A traditional R formula that contains interaction
terms. This can include |

role | For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created from the original variables will be used as predictors in a model. |

trained | A logical to indicate if the quantities for preprocessing have been estimated. |

objects | A list of |

sep | A character value used to delineate variables in an
interaction (e.g. |

skip | A logical. Should the step be skipped when the
recipe is baked by |

id | A character string that is unique to this step to identify it. |

An updated version of `recipe`

with the new step
added to the sequence of existing steps (if any).

`step_interact`

can create interactions between
variables. It is primarily intended for **numeric data**;
categorical variables should probably be converted to dummy
variables using `step_dummy()`

prior to being used for
interactions.

Unlike other step functions, the `terms`

argument should
be a traditional R model formula but should contain no inline
functions (e.g. `log`

). For example, for predictors
`A`

, `B`

, and `C`

, a formula such as
`~A:B:C`

can be used to make a three way interaction
between the variables. If the formula contains terms other than
interactions (e.g. `(A+B+C)^3`

) only the interaction terms
are retained for the design matrix.

The separator between the variables defaults to "`_x_`

" so
that the three way interaction shown previously would generate a
column named `A_x_B_x_C`

. This can be changed using the
`sep`

argument.

When dummy variables are created and are used in interactions,
selectors can help specify the interactions succinctly. For
example, suppose a factor column `X`

gets converted to dummy
variables `x_2`

, `x_3`

, ..., `x_6`

using `step_dummy()`

. If
you wanted an interaction with numeric column `z`

, you could
create a set of specific interaction effects (e.g.
`x_2:z + x_3:z`

and so on) or you could use
`starts_with("x_"):z`

. When `prep()`

evaluates this step,
`starts_with("x_")`

resolves to `(x_2 + x_3 + x_4 + x_5 + x_6)`

so that the formula is now `(x_2 + x_3 + x_4 + x_5 + x_6):z`

and
all two-way interactions are created.

When you `tidy()`

this step, a tibble with column `terms`

(the interaction effects) is returned.

```
library(modeldata)
data(penguins)
penguins <- penguins %>% na.omit()
rec <- recipe(flipper_length_mm ~., data = penguins)
int_mod_1 <- rec %>%
step_interact(terms = ~ bill_depth_mm:bill_length_mm)
# specify all dummy variables succinctly with `starts_with()`
int_mod_2 <- rec %>%
step_dummy(sex, species, island) %>%
step_interact(terms = ~ body_mass_g:starts_with("species"))
int_mod_1 <- prep(int_mod_1, training = penguins)
int_mod_2 <- prep(int_mod_2, training = penguins)
dat_1 <- bake(int_mod_1, penguins)
dat_2 <- bake(int_mod_2, penguins)
names(dat_1)
#> [1] "species" "island"
#> [3] "bill_length_mm" "bill_depth_mm"
#> [5] "body_mass_g" "sex"
#> [7] "flipper_length_mm" "bill_depth_mm_x_bill_length_mm"
names(dat_2)
#> [1] "bill_length_mm" "bill_depth_mm"
#> [3] "body_mass_g" "flipper_length_mm"
#> [5] "sex_male" "species_Chinstrap"
#> [7] "species_Gentoo" "island_Dream"
#> [9] "island_Torgersen" "body_mass_g_x_species_Chinstrap"
#> [11] "body_mass_g_x_species_Gentoo"
tidy(int_mod_1, number = 1)
#> # A tibble: 1 x 2
#> terms id
#> <chr> <chr>
#> 1 bill_depth_mm:bill_length_mm interact_IUB7W
tidy(int_mod_2, number = 2)
#> # A tibble: 2 x 2
#> terms id
#> <chr> <chr>
#> 1 body_mass_g:species_Chinstrap interact_kM5w7
#> 2 body_mass_g:species_Gentoo interact_kM5w7
```