Rename variables by name using dplyr

step_rename() creates a specification of a recipe step that will add variables using dplyr::rename().

step_rename(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  inputs = NULL,
  skip = FALSE,
  id = rand_id("rename")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more unquoted expressions separated by commas. See dplyr::rename() where the convention is new_name = old_name.
role: For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
inputs: Quosure(s) of ....
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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 = 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

When an object in the user's global environment is referenced in the expression defining the new variable(s), it is a good idea to use quasiquotation (e.g. !!) to embed the value of the object in the expression (to be portable between sessions).

Tidying

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

terms: character, the selectors or variables selected
value: character, rename expression
id: character, id of this step

Sparse data

This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.

Case weights

The underlying operation does not allow for case weights.

Examples

recipe(~., data = iris) |>
  step_rename(Sepal_Width = Sepal.Width) |>
  prep() |>
  bake(new_data = NULL) |>
  slice(1:5)
#> # A tibble: 5 × 5
#>   Sepal.Length Sepal_Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> 5          5           3.6          1.4         0.2 setosa 

vars <- c(var1 = "cyl", var2 = "am")
car_rec <-
  recipe(~., data = mtcars) |>
  step_rename(!!!vars)

car_rec |>
  prep() |>
  bake(new_data = NULL)
#> # A tibble: 32 × 11
#>      mpg  var1  disp    hp  drat    wt  qsec    vs  var2  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # ℹ 22 more rows

car_rec |>
  tidy(number = 1)
#> # A tibble: 2 × 3
#>   terms value     id          
#>   <chr> <chr>     <chr>       
#> 1 var1  "\"cyl\"" rename_sfgIN
#> 2 var2  "\"am\""  rename_sfgIN