tidy will return a data frame that contains information regarding a recipe or operation within the recipe (when a tidy method for the operation exists).

# S3 method for recipe
tidy(x, number = NA, id = NA, ...)

# S3 method for step
tidy(x, ...)

# S3 method for check
tidy(x, ...)

Arguments

x

A recipe object (trained or otherwise).

number

An integer or NA. If missing and id is not provided, the return value is a list of the operations in the recipe. If a number is given, a tidy method is executed for that operation in the recipe (if it exists). number must not be provided if id is.

id

A character string or NA. If missing and number is not provided, the return value is a list of the operations in the recipe. If a character string is given, a tidy method is executed for that operation in the recipe (if it exists). id must not be provided if number is.

...

Not currently used.

Value

A tibble with columns that would vary depending on what tidy method is executed. When number and id are NA, a tibble with columns number (the operation iteration), operation (either "step" or "check"), type (the method, e.g. "nzv", "center"), a logical column called trained for whether the operation has been estimated using prep, a logical for skip, and a character column id.

Examples

library(modeldata) data(okc) okc_rec <- recipe(~ ., data = okc) %>% step_other(all_nominal(), threshold = 0.05, other = "another") %>% step_date(date, features = "dow") %>% step_center(all_numeric()) %>% step_dummy(all_nominal()) %>% check_cols(starts_with("date"), age, height) tidy(okc_rec)
#> # A tibble: 5 x 6 #> number operation type trained skip id #> <int> <chr> <chr> <lgl> <lgl> <chr> #> 1 1 step other FALSE FALSE other_OruwB #> 2 2 step date FALSE FALSE date_l3nYA #> 3 3 step center FALSE FALSE center_jp5Kj #> 4 4 step dummy FALSE FALSE dummy_FhDIk #> 5 5 check cols FALSE FALSE cols_IyjzO
tidy(okc_rec, number = 2)
#> # A tibble: 1 x 4 #> terms value ordinal id #> <chr> <chr> <lgl> <chr> #> 1 date dow FALSE date_l3nYA
tidy(okc_rec, number = 3)
#> # A tibble: 1 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 all_numeric() NA center_jp5Kj
okc_rec_trained <- prep(okc_rec, training = okc)
#> Warning: There are new levels in a factor: NA
tidy(okc_rec_trained)
#> # A tibble: 5 x 6 #> number operation type trained skip id #> <int> <chr> <chr> <lgl> <lgl> <chr> #> 1 1 step other TRUE FALSE other_OruwB #> 2 2 step date TRUE FALSE date_l3nYA #> 3 3 step center TRUE FALSE center_jp5Kj #> 4 4 step dummy TRUE FALSE dummy_FhDIk #> 5 5 check cols TRUE FALSE cols_IyjzO
tidy(okc_rec_trained, number = 3)
#> # A tibble: 2 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 age 32.3 center_jp5Kj #> 2 height 68.3 center_jp5Kj