step_unknown
creates a specification of a recipe
step that will assign a missing value in a factor level to"unknown".
Usage
step_unknown(
recipe,
...,
role = NA,
trained = FALSE,
new_level = "unknown",
objects = NULL,
skip = FALSE,
id = rand_id("unknown")
)
Arguments
- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.
- ...
One or more selector functions to choose variables for this step. See
selections()
for more details.- role
Not used by this step since no new variables are created.
- trained
A logical to indicate if the quantities for preprocessing have been estimated.
- new_level
A single character value that will be assigned to new factor levels.
- objects
A list of objects that contain the information on factor levels that will be determined by
prep()
.- skip
A logical. Should the step be skipped when the recipe is baked by
bake()
? While all operations are baked whenprep()
is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when usingskip = TRUE
as it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Details
The selected variables are adjusted to have a new
level (given by new_level
) that is placed in the last
position.
Note that if the original columns are character, they will be converted to factors by this step.
If new_level
is already in the data given to prep
, an error
is thrown.
Tidying
When you tidy()
this step, a tibble with columns
terms
(the columns that will be affected) and value
(the factor
levels that is used for the new value) is returned.
See also
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy_extract()
,
step_dummy_multi_choice()
,
step_dummy()
,
step_factor2string()
,
step_holiday()
,
step_indicate_na()
,
step_integer()
,
step_novel()
,
step_num2factor()
,
step_ordinalscore()
,
step_other()
,
step_regex()
,
step_relevel()
,
step_string2factor()
,
step_unorder()
Examples
library(modeldata)
data(okc)
rec <-
recipe(~ diet + location, data = okc) %>%
step_unknown(diet, new_level = "unknown diet") %>%
step_unknown(location, new_level = "unknown location") %>%
prep()
table(bake(rec, new_data = NULL) %>% pull(diet),
okc %>% pull(diet),
useNA = "always") %>%
as.data.frame() %>%
dplyr::filter(Freq > 0)
#> Var1 Var2 Freq
#> 1 anything anything 6174
#> 2 halal halal 11
#> 3 kosher kosher 11
#> 4 mostly anything mostly anything 16562
#> 5 mostly halal mostly halal 48
#> 6 mostly kosher mostly kosher 86
#> 7 mostly other mostly other 1004
#> 8 mostly vegan mostly vegan 335
#> 9 mostly vegetarian mostly vegetarian 3438
#> 10 other other 331
#> 11 strictly anything strictly anything 5107
#> 12 strictly halal strictly halal 18
#> 13 strictly kosher strictly kosher 18
#> 14 strictly other strictly other 450
#> 15 strictly vegan strictly vegan 227
#> 16 strictly vegetarian strictly vegetarian 874
#> 17 vegan vegan 136
#> 18 vegetarian vegetarian 665
#> 19 unknown diet <NA> 24360
tidy(rec, number = 1)
#> # A tibble: 1 × 3
#> terms value id
#> <chr> <chr> <chr>
#> 1 diet unknown diet unknown_ZB2lW