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") ) # S3 method for step_rename tidy(x, ...) # S3 method for step_rename_at tidy(x, ...)
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
|
role | For model terms created by this step, what analysis role should they be assigned? By default, the function assumes that the new dimension columns created by 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 |
id | A character string that is unique to this step to identify it. |
x | A |
An updated version of recipe
with the new step added to the
sequence of existing steps (if any). For the tidy
method, a tibble with
columns values
which contains the rename
expressions as character
strings (and are not reparsable).
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).
recipe( ~ ., data = iris) %>% step_rename(Sepal_Width = Sepal.Width) %>% prep() %>% bake(new_data = NULL) %>% slice(1:5)#> # A tibble: 5 x 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 setosavars <- c(var1 = "cyl", var2 = "am") car_rec <- recipe(~ ., data = mtcars) %>% step_rename(!!vars) car_rec %>% prep() %>% bake(new_data = NULL)#> # A tibble: 32 x 11 #> mpg `<chr>...var1` disp hp drat wt qsec vs `<chr>...var2` gear #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 #> # … with 22 more rows, and 1 more variable: carb <dbl>#> # A tibble: 1 x 3 #> terms value id #> <chr> <chr> <chr> #> 1 <chr> "c(var1 = \"cyl\", var2 = \"am\")" rename_o1y7D