`step_sqrt`

creates a *specification* of a recipe
step that will square root transform the data.

## Usage

```
step_sqrt(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("sqrt")
)
```

## 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 variable names that will be populated (eventually) by the

`terms`

argument.- 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.

## Tidying

When you `tidy()`

this step, a tibble with column
`terms`

(the columns that will be affected) is returned.

## See also

Other individual transformation steps:
`step_BoxCox()`

,
`step_YeoJohnson()`

,
`step_bs()`

,
`step_harmonic()`

,
`step_hyperbolic()`

,
`step_inverse()`

,
`step_invlogit()`

,
`step_logit()`

,
`step_log()`

,
`step_mutate()`

,
`step_ns()`

,
`step_percentile()`

,
`step_poly()`

,
`step_relu()`

## Examples

```
set.seed(313)
examples <- matrix(rnorm(40)^2, ncol = 2)
examples <- as.data.frame(examples)
rec <- recipe(~ V1 + V2, data = examples)
sqrt_trans <- rec %>%
step_sqrt(all_numeric_predictors())
sqrt_obj <- prep(sqrt_trans, training = examples)
transformed_te <- bake(sqrt_obj, examples)
plot(examples$V1, transformed_te$V1)
tidy(sqrt_trans, number = 1)
#> # A tibble: 1 × 2
#> terms id
#> <chr> <chr>
#> 1 all_numeric_predictors() sqrt_IhS7o
tidy(sqrt_obj, number = 1)
#> # A tibble: 2 × 2
#> terms id
#> <chr> <chr>
#> 1 V1 sqrt_IhS7o
#> 2 V2 sqrt_IhS7o
```