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 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)
recBefore 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 FALSEThis 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:
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()) # recommendedWhenever 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_X1 <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.