Interpolate Gaps in Measurement Data

step_measure_interpolate() creates a specification of a recipe step that fills gaps or missing values in measurement data using interpolation.

Usage

step_measure_interpolate(
  recipe,
  ranges,
  method = c("linear", "spline"),
  measures = NULL,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_interpolate")
)

Arguments

recipe

A recipe object.

ranges

A list of numeric vectors specifying ranges to interpolate. Each element should be a vector of length 2: c(min, max).

method

Interpolation method: "linear" or "spline". Default is "linear".

measures

An optional character vector of measure column names.

role

Not used.

trained

Logical indicating if the step has been trained.

skip

Logical. Should the step be skipped when baking?

id

Unique step identifier.

Value

An updated recipe with the new step added.

Details

This step is useful for:

• Filling gaps left by excluded regions that need restoration

• Handling missing or invalid data points

• Smoothing over detector saturation regions

The interpolation uses data points immediately outside the specified ranges to estimate values within the ranges.

Examples

library(recipes)

# Interpolate over a problematic region
rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_interpolate(ranges = list(c(40, 50)), method = "spline") |>
  prep()