When I try to use any of the 'step' functions in a recipe in R and check the results) (using head(df), none of the numbers are shown to have changed.
Using iris as an example, I tried to normalize the numeric predictors from 0-1. When I call the output under head(df) nothing changes in the results(no errors are noted), example below:
Original attempt:
iris_recipe<-iris %>%
recipe(Species ~ .) %>%
step_range(recipe, all_numeric_predictors(), min = 0, max=1)
head(iris_recipe)
$var_info
# A tibble: 5 × 4
variable type role source
<chr> <chr> <chr> <chr>
1 Sepal.Length numeric predictor original
2 Sepal.Width numeric predictor original
3 Petal.Length numeric predictor original
4 Petal.Width numeric predictor original
5 Species nominal outcome original
$term_info
# A tibble: 5 × 4
variable type role source
<chr> <chr> <chr> <chr>
1 Sepal.Length numeric predictor original
2 Sepal.Width numeric predictor original
3 Petal.Length numeric predictor original
4 Petal.Width numeric predictor original
5 Species nominal outcome original
$steps
$steps[[1]]
$terms
<list_of<quosure>>
[[1]]
<quosure>
expr: ^recipe
env: 0x000002b201dc23e0
[[2]]
<quosure>
expr: ^all_numeric_predictors()
env: 0x000002b201dc23e0
$role
[1] NA
$trained
[1] FALSE
$min
[1] 0
$max
[1] 1
$ranges
NULL
$skip
[1] FALSE
$id
[1] "range_5JGit"
attr(,"class")
[1] "step_range" "step"
$template
# A tibble: 150 × 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
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
# ℹ Use `print(n = ...)` to see more rows
$levels
NULL
$retained
[1] NA
Specific Attempt:
iris_recipe2<-iris %>%
recipe(Species ~ .) %>%
step_range(recipe, Sepal.Length, min=0, max=1)
head(iris_recipe2)
$var_info
# A tibble: 5 × 4
variable type role source
<chr> <chr> <chr> <chr>
1 Sepal.Length numeric predictor original
2 Sepal.Width numeric predictor original
3 Petal.Length numeric predictor original
4 Petal.Width numeric predictor original
5 Species nominal outcome original
$term_info
# A tibble: 5 × 4
variable type role source
<chr> <chr> <chr> <chr>
1 Sepal.Length numeric predictor original
2 Sepal.Width numeric predictor original
3 Petal.Length numeric predictor original
4 Petal.Width numeric predictor original
5 Species nominal outcome original
$steps
$steps[[1]]
$terms
<list_of<quosure>>
[[1]]
<quosure>
expr: ^recipe
env: 0x000002b2073fe328
[[2]]
<quosure>
expr: ^Sepal.Length
env: 0x000002b2073fe328
$role
[1] NA
$trained
[1] FALSE
$min
[1] 0
$max
[1] 1
$ranges
NULL
$skip
[1] FALSE
$id
[1] "range_p7JFo"
attr(,"class")
[1] "step_range" "step"
$template
# A tibble: 150 × 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
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
# ℹ Use `print(n = ...)` to see more rows
$levels
NULL
$retained
[1] NA
After creating a recipe specification we can prep the recipe and bake our data to apply the recipe.
library(tidyverse)
library(tidymodels)
iris_processed <- iris %>%
recipe(Species ~ .) %>%
step_range(all_numeric_predictors(), min = 0, max=1) %>%
prep() %>%
bake(iris)
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
head(iris_processed)
#> # A tibble: 6 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 0.222 0.625 0.0678 0.0417 setosa
#> 2 0.167 0.417 0.0678 0.0417 setosa
#> 3 0.111 0.5 0.0508 0.0417 setosa
#> 4 0.0833 0.458 0.0847 0.0417 setosa
#> 5 0.194 0.667 0.0678 0.0417 setosa
#> 6 0.306 0.792 0.119 0.125 setosa
Created on 2022-11-12 with reprex v2.0.2