step_ratio
creates a specification of a recipe
step that will create one or more ratios out of 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_vars
to 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
The column names used in the ratios. This argument is not populated until
prep()
is executed.- 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 = 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 columns
terms
(the selectors or variables selected) and denom
is returned.
See also
Other multivariate transformation steps:
step_classdist()
,
step_depth()
,
step_geodist()
,
step_ica()
,
step_isomap()
,
step_kpca_poly()
,
step_kpca_rbf()
,
step_kpca()
,
step_mutate_at()
,
step_nnmf_sparse()
,
step_nnmf()
,
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)) %>%
# get rid of the original predictors
step_rm(all_predictors(), -ends_with("total"))
ratio_recipe <- prep(ratio_recipe, training = biomass_tr)
ratio_data <- bake(ratio_recipe, biomass_te)
ratio_data
#> # A tibble: 80 × 7
#> total HHV carbon_o_total hydrogen_o_total oxygen_o_…¹ nitro…² sulfu…³
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 99.7 18.3 0.465 0.0568 0.473 0.00301 0.00221
#> 2 100 17.6 0.432 0.055 0.481 0.0285 0.0034
#> 3 100 17.2 0.427 0.055 0.491 0.024 0.003
#> 4 92.1 18.9 0.504 0.0662 0.405 0.0195 0.00543
#> 5 98.0 20.5 0.497 0.0645 0.436 0.00204 0
#> 6 92.4 18.5 0.479 0.0595 0.451 0.00758 0.00216
#> 7 100 15.1 0.389 0.0523 0.541 0.0119 0.0051
#> 8 81.7 16.2 0.515 0.0570 0.414 0.0116 0.00245
#> 9 69.7 11.1 0.419 0.0631 0.446 0.00201 0.0703
#> 10 60.9 10.8 0.456 0.0619 0.389 0.0760 0.0172
#> # … with 70 more rows, and abbreviated variable names ¹oxygen_o_total,
#> # ²nitrogen_o_total, ³sulfur_o_total