Apply a Custom Function to Measurements

step_measure_map() creates a specification of a recipe step that applies a custom function to each sample's measurements. Use this when the built-in preprocessing steps (SNV, MSC, Savitzky-Golay) don't cover your needs.

Usage

step_measure_map(
  recipe,
  fn,
  ...,
  measures = NULL,
  verbosity = 1L,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_map")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

fn

A function to apply to each sample's measurement tibble. The

function should accept a tibble with location and value columns and return a tibble with the same structure. Can also be a formula (e.g., ~ { .x$value <- log1p(.x$value); .x }) which will be converted via rlang::as_function().

...

Additional arguments passed to fn during baking.

measures

An optional character vector of measure column names to

process. If NULL (the default), all measure columns will be processed.

verbosity

An integer controlling output verbosity:

• 0: Silent - suppress all messages and output from fn

• 1: Normal (default) - show output from fn

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the step has been trained.

skip

A logical. Should the step be skipped when the recipe is baked?

id

A character string that is unique to this step.

Value

An updated version of recipe with the new step added.

Details

This step is the "escape hatch" for custom sample-wise transformations that aren't covered by the built-in steps. It integrates fully with the recipes framework, meaning your custom transformation will be:

• Applied consistently during prep() and bake()

• Included when bundling recipes into workflows

• Reproducible across sessions

Function Requirements

The function fn must:

• Accept a tibble with location and value columns

• Return a tibble with location and value columns

• Not change the number of rows (measurements must remain aligned)

When to Use This Step

Use step_measure_map() for domain-specific transformations not covered by the built-in steps:

• Custom baseline correction algorithms

• Specialized normalization methods

• Instrument-specific corrections

• Experimental preprocessing techniques

For common operations, prefer the built-in steps:

• Scatter correction → step_measure_snv() or step_measure_msc()

• Smoothing/derivatives → step_measure_savitzky_golay()

Prototyping with measure_map()

When developing a custom transformation, you may find it helpful to prototype using measure_map() on baked data before wrapping it in a step. Once your function works correctly, use `step_measure_

for production pipelines.

See also

• step_measure_snv(), step_measure_msc(), step_measure_savitzky_golay() for built-in preprocessing steps

• measure_map() for prototyping custom transformations

Other measure-preprocessing: step_measure_absorbance(), step_measure_calibrate_x(), step_measure_calibrate_y(), step_measure_derivative(), step_measure_derivative_gap(), step_measure_emsc(), step_measure_kubelka_munk(), step_measure_log(), step_measure_msc(), step_measure_normalize_istd(), step_measure_osc(), step_measure_ratio_reference(), step_measure_snv(), step_measure_subtract_blank(), step_measure_subtract_reference(), step_measure_transmittance()

Examples

library(recipes)

# Example 1: Custom log transformation
log_transform <- function(x) {
  x$value <- log1p(x$value)
  x
}

rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_map(log_transform) |>
  step_measure_snv() |>
  prep()

bake(rec, new_data = NULL)
#> # A tibble: 215 × 5
#>       id water   fat protein .measures
#>    <int> <dbl> <dbl>   <dbl>    <meas>
#>  1     1  60.5  22.5    16.7 [100 × 2]
#>  2     2  46    40.1    13.5 [100 × 2]
#>  3     3  71     8.4    20.5 [100 × 2]
#>  4     4  72.8   5.9    20.7 [100 × 2]
#>  5     5  58.3  25.5    15.5 [100 × 2]
#>  6     6  44    42.7    13.7 [100 × 2]
#>  7     7  44    42.7    13.7 [100 × 2]
#>  8     8  69.3  10.6    19.3 [100 × 2]
#>  9     9  61.4  19.9    17.7 [100 × 2]
#> 10    10  61.4  19.9    17.7 [100 × 2]
#> # ℹ 205 more rows

# Example 2: Using formula syntax for inline transformations
rec2 <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_map(~ {
    # Subtract minimum to remove offset
    .x$value <- .x$value - min(.x$value)
    .x
  }) |>
  prep()

# Example 3: Using external package functions
# (e.g., custom baseline from a spectroscopy package)
if (FALSE) { # \dontrun{
rec3 <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_map(my_baseline_correction, method = "als") |>
  step_measure_output_wide()
} # }