step_factor2string()
creates a specification of a recipe step that will
convert one or more factor vectors to strings.
Usage
step_factor2string(
recipe,
...,
role = NA,
trained = FALSE,
columns = FALSE,
skip = FALSE,
id = rand_id("factor2string")
)
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.
- columns
A character string of the selected variable names. This field is a placeholder and will be populated once
prep()
is used.- 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
prep
has an option strings_as_factors
that defaults to TRUE
.
If this step is used with the default option, the string(s() produced by
this step will be converted to factors after all of the steps have been
prepped.
Tidying
When you tidy()
this step, a tibble with columns terms
(the columns that will be affected) 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_holiday()
,
step_indicate_na()
,
step_integer()
,
step_novel()
,
step_num2factor()
,
step_ordinalscore()
,
step_other()
,
step_regex()
,
step_relevel()
,
step_string2factor()
,
step_time()
,
step_unknown()
,
step_unorder()
Examples
data(Sacramento, package = "modeldata")
rec <- recipe(~ city + zip, data = Sacramento)
make_string <- rec %>%
step_factor2string(city)
make_string <- prep(make_string,
training = Sacramento,
strings_as_factors = FALSE
)
make_string
#>
#> ── Recipe ────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> predictor: 2
#>
#> ── Training information
#> Training data contained 932 data points and no incomplete rows.
#>
#> ── Operations
#> • Character variables from: city | Trained
# note that `city` is a string in recipe output
bake(make_string, new_data = NULL) %>% head()
#> # A tibble: 6 × 2
#> city zip
#> <chr> <fct>
#> 1 SACRAMENTO z95838
#> 2 SACRAMENTO z95823
#> 3 SACRAMENTO z95815
#> 4 SACRAMENTO z95815
#> 5 SACRAMENTO z95824
#> 6 SACRAMENTO z95841
# ...but remains a factor in the original data
Sacramento %>% head()
#> # A tibble: 6 × 9
#> city zip beds baths sqft type price latitude longitude
#> <fct> <fct> <int> <dbl> <int> <fct> <int> <dbl> <dbl>
#> 1 SACRAMENTO z95838 2 1 836 Residential 59222 38.6 -121.
#> 2 SACRAMENTO z95823 3 1 1167 Residential 68212 38.5 -121.
#> 3 SACRAMENTO z95815 2 1 796 Residential 68880 38.6 -121.
#> 4 SACRAMENTO z95815 2 1 852 Residential 69307 38.6 -121.
#> 5 SACRAMENTO z95824 2 1 797 Residential 81900 38.5 -121.
#> 6 SACRAMENTO z95841 3 1 1122 Condo 89921 38.7 -121.