Ingest Measurements from a Single Column

step_measure_input_long creates a specification of a recipe step that converts measures organized in a column for the analytical results (and one or more columns of numeric indices) into an internal format used by the package.

Usage

step_measure_input_long(
  recipe,
  ...,
  location,
  col_name = ".measures",
  dim_names = NULL,
  dim_units = NULL,
  pad = FALSE,
  role = "measure",
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("measure_input_long")
)

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 single column contains the analytical measurements. The selection should be in the order of the measurement's profile.
location: One or more selector functions to choose which column(s) have the locations of the analytical values. For 1D data (spectra, chromatograms), select a single location column. For 2D or higher dimensional data (LC-DAD, 2D NMR, EEM), select multiple location columns. Columns will be renamed to location_1, location_2, etc. in order.
col_name: A single character string specifying the name of the output column that will contain the measure data. Defaults to ".measures". Use different names when creating multiple measure columns (e.g., ".uv_spectrum" and ".ms_spectrum").
dim_names: Optional character vector of semantic names for each dimension (e.g., c("retention_time", "wavelength")). Only used for multi-dimensional data.
dim_units: Optional character vector of units for each dimension (e.g., c("min", "nm")). Only used for multi-dimensional data.
pad: Whether to pad the measurements to ensure that they all have the same number of values. This is useful when there are missing values in the measurements.
role: Not used by this step since no new variables are created.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
columns: A character vector of column names determined by the recipe.
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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 using skip = TRUE as it may affect the computations for subsequent operations.
id: A character string that is unique to this step to identify it.

Details

This step is designed for data in a format where there is a column for the analytical measurement (e.g., absorption, etc.) and one or more columns with the location of the value (e.g., wave number, retention time, wavelength, etc.).

step_measure_input_long() will collect those data and put them into a format used internally by this package. The data structure has a row for each independent experimental unit and a nested tibble with that sample's measure (measurement and location). It assumes that there are unique combinations of the other columns in the data that define individual patterns associated with the pattern. If this is not the case, the special values might be inappropriately restructured.

The best advice is to have a column of any type that indicates the unique sample number for each measure. For example, if there are 200 values in the measure and 7 samples, the input data (in long format) should have 1,400 rows. We advise having a column with 7 unique values indicating which of the rows correspond to each sample.

Multi-Dimensional Data

For 2D or higher dimensional data, provide multiple location columns:

# LC-DAD data with retention time and wavelength
step_measure_input_long(
  absorbance,
  location = vars(retention_time, wavelength),
  dim_names = c("time", "wavelength"),
  dim_units = c("min", "nm")
)

The result will be a measure_nd_list column instead of a measure_list.

Missing Data

Currently, measure assumes that there are equal numbers of values within a sample. If there are missing values in the measurements, you'll need to pad them with missing values (as opposed to an absent row in the long format). If not, an error will occur.

Tidying

When you tidy() this step, a tibble indicating which of the original columns were used to reformat the data.

Examples

library(recipes)

# 1D data (traditional usage)
rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  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