Clean Variable Names

step_clean_names() creates a specification of a recipe step that will clean variable names so the names consist only of letters, numbers, and the underscore.

Usage

step_clean_names(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  clean = NULL,
  skip = FALSE,
  id = rand_id("clean_names")
)

Arguments

recipe

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

...

One or more selector functions to choose which variables are affected by the step. See recipes::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.

clean

A named character vector to clean variable names. This is NULL until computed by recipes::prep.recipe().

skip

A logical. Should the step be skipped when the recipe is baked by recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() 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 using skip = FALSE.

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 existing steps (if any).

Tidying

When you tidy() this step, a tibble is returned with columns terms, value, and id:

terms

character, the new clean variable names

value

character, the original variable names

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

step_clean_levels(), recipes::step_factor2string(), recipes::step_string2factor(), recipes::step_regex(), recipes::step_unknown(), recipes::step_novel(), recipes::step_other()

Other Steps for Text Cleaning: step_clean_levels()

Examples

library(recipes)
data(airquality)

air_tr <- tibble(airquality[1:100, ])
air_te <- tibble(airquality[101:153, ])

rec <- recipe(~., data = air_tr)

rec <- rec %>%
  step_clean_names(all_predictors())
rec <- prep(rec, training = air_tr)
tidy(rec, number = 1)
#> # A tibble: 6 × 3
#>   terms   value   id               
#>   <chr>   <chr>   <chr>            
#> 1 ozone   Ozone   clean_names_mb92Q
#> 2 solar_r Solar.R clean_names_mb92Q
#> 3 wind    Wind    clean_names_mb92Q
#> 4 temp    Temp    clean_names_mb92Q
#> 5 month   Month   clean_names_mb92Q
#> 6 day     Day     clean_names_mb92Q

bake(rec, air_tr)
#> # A tibble: 100 × 6
#>    ozone solar_r  wind  temp month   day
#>    <int>   <int> <dbl> <int> <int> <int>
#>  1    41     190   7.4    67     5     1
#>  2    36     118   8      72     5     2
#>  3    12     149  12.6    74     5     3
#>  4    18     313  11.5    62     5     4
#>  5    NA      NA  14.3    56     5     5
#>  6    28      NA  14.9    66     5     6
#>  7    23     299   8.6    65     5     7
#>  8    19      99  13.8    59     5     8
#>  9     8      19  20.1    61     5     9
#> 10    NA     194   8.6    69     5    10
#> # ℹ 90 more rows
bake(rec, air_te)
#> # A tibble: 53 × 6
#>    ozone solar_r  wind  temp month   day
#>    <int>   <int> <dbl> <int> <int> <int>
#>  1   110     207   8      90     8     9
#>  2    NA     222   8.6    92     8    10
#>  3    NA     137  11.5    86     8    11
#>  4    44     192  11.5    86     8    12
#>  5    28     273  11.5    82     8    13
#>  6    65     157   9.7    80     8    14
#>  7    NA      64  11.5    79     8    15
#>  8    22      71  10.3    77     8    16
#>  9    59      51   6.3    79     8    17
#> 10    23     115   7.4    76     8    18
#> # ℹ 43 more rows