Resample Measurements to New Grid

step_measure_resample() creates a specification of a recipe step that interpolates measurements to a new regular x-axis grid.

Usage

step_measure_resample(
  recipe,
  n = NULL,
  spacing = NULL,
  range = NULL,
  method = c("linear", "spline"),
  measures = NULL,
  role = NA,
  trained = FALSE,
  new_locations = NULL,
  skip = FALSE,
  id = recipes::rand_id("measure_resample")
)

Arguments

recipe

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

n

A positive integer specifying the number of points in the new grid. Mutually exclusive with spacing.

spacing

A positive numeric value specifying the spacing between points in the new grid. Mutually exclusive with n.

range

Optional numeric vector of length 2 specifying the range for the new grid as c(min, max). If NULL (default), uses the range of the existing measurements.

method

The interpolation method. One of:

"linear": Linear interpolation (default)
"spline": Cubic spline interpolation (smoother)

measures

An optional character vector of measure column names to process. If NULL (the default), all measure columns will be processed.

role

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

trained

A logical to indicate if the step has been trained.

new_locations

The computed new grid locations (after training).

skip

A logical. Should the step be skipped when baking?

id

A character string that is unique to this step.

Value

An updated version of recipe with the new step added.

Details

This step interpolates measurements to a new regular grid of x-axis values. This is useful for:

Aligning data from different instruments with different sampling rates
Reducing data density for faster processing
Ensuring uniform spacing for methods that require it
Matching measurements to a reference grid

The new grid is determined during prep() based on the training data. If range is not specified, the grid spans from the minimum to maximum location values in the training data.

Interpolation methods:

"linear": Fast and simple, may introduce slight distortion at peaks
"spline": Smoother interpolation that preserves peak shape better

Examples

library(recipes)

# Resample to 50 evenly spaced points
rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_resample(n = 50) |>
  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  [50 × 2]
#>  2     2  46    40.1    13.5  [50 × 2]
#>  3     3  71     8.4    20.5  [50 × 2]
#>  4     4  72.8   5.9    20.7  [50 × 2]
#>  5     5  58.3  25.5    15.5  [50 × 2]
#>  6     6  44    42.7    13.7  [50 × 2]
#>  7     7  44    42.7    13.7  [50 × 2]
#>  8     8  69.3  10.6    19.3  [50 × 2]
#>  9     9  61.4  19.9    17.7  [50 × 2]
#> 10    10  61.4  19.9    17.7  [50 × 2]
#> # ℹ 205 more rows

# Resample with specific spacing
rec2 <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_resample(spacing = 2, method = "spline") |>
  prep()

bake(rec2, 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  [51 × 2]
#>  2     2  46    40.1    13.5  [51 × 2]
#>  3     3  71     8.4    20.5  [51 × 2]
#>  4     4  72.8   5.9    20.7  [51 × 2]
#>  5     5  58.3  25.5    15.5  [51 × 2]
#>  6     6  44    42.7    13.7  [51 × 2]
#>  7     7  44    42.7    13.7  [51 × 2]
#>  8     8  69.3  10.6    19.3  [51 × 2]
#>  9     9  61.4  19.9    17.7  [51 × 2]
#> 10    10  61.4  19.9    17.7  [51 × 2]
#> # ℹ 205 more rows

Usage

Arguments

Value

Details

See also

Examples