step_ratio() creates a specification of a recipe step that will create
one or more ratios from selected numeric variables.
Usage
step_ratio(
recipe,
...,
role = "predictor",
trained = FALSE,
denom = denom_vars(),
naming = function(numer, denom) {
make.names(paste(numer, denom, sep = "_o_"))
},
columns = NULL,
keep_original_cols = TRUE,
skip = FALSE,
id = rand_id("ratio")
)
denom_vars(...)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 which variables will be used in the numerator of the ratio. When used with
denom_vars, the dots indicate which variables are used in the denominator. Seeselections()for more details.- 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.
- denom
A call to
denom_varsto specify which variables are used in the denominator that can include specific variable names separated by commas or different selectors (seeselections()). If a column is included in both lists to be numerator and denominator, it will be removed from the listing.- naming
A function that defines the naming convention for new ratio columns.
- columns
A character string of the selected variable names. This field is a placeholder and will be populated once
prep()is used.- keep_original_cols
A logical to keep the original variables in the output. Defaults to
TRUE.- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()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 usingskip = TRUEas 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 columns
terms (the selectors or variables selected) and denom is returned.
When you tidy() this step, a tibble is returned with
columns terms, denom , and id:
- terms
character, the selectors or variables selected
- denom
character, name of denominator selected
- id
character, id of this step
See also
Other multivariate transformation steps:
step_classdist(),
step_classdist_shrunken(),
step_depth(),
step_geodist(),
step_ica(),
step_isomap(),
step_kpca(),
step_kpca_poly(),
step_kpca_rbf(),
step_mutate_at(),
step_nnmf(),
step_nnmf_sparse(),
step_pca(),
step_pls(),
step_spatialsign()
Examples
library(recipes)
data(biomass, package = "modeldata")
biomass$total <- apply(biomass[, 3:7], 1, sum)
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen +
sulfur + total,
data = biomass_tr
)
ratio_recipe <- rec %>%
# all predictors over total
step_ratio(all_numeric_predictors(), denom = denom_vars(total),
keep_original_cols = FALSE)
ratio_recipe <- prep(ratio_recipe, training = biomass_tr)
ratio_data <- bake(ratio_recipe, biomass_te)
ratio_data
#> # A tibble: 80 × 6
#> HHV carbon_o_total hydrogen_o_total oxygen_o_total nitrogen_o_total
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 18.3 0.465 0.0568 0.473 0.00301
#> 2 17.6 0.432 0.055 0.481 0.0285
#> 3 17.2 0.427 0.055 0.491 0.024
#> 4 18.9 0.504 0.0662 0.405 0.0195
#> 5 20.5 0.497 0.0645 0.436 0.00204
#> 6 18.5 0.479 0.0595 0.451 0.00758
#> 7 15.1 0.389 0.0523 0.541 0.0119
#> 8 16.2 0.515 0.0570 0.414 0.0116
#> 9 11.1 0.419 0.0631 0.446 0.00201
#> 10 10.8 0.456 0.0619 0.389 0.0760
#> # ℹ 70 more rows
#> # ℹ 1 more variable: sulfur_o_total <dbl>