Identify Column Types in Analytical Data

Automatically detects column types in a data frame based on naming conventions common in analytical chemistry. This helps set up recipes with appropriate roles for different column types.

Usage

measure_identify_columns(data, patterns = measure_column_patterns)

Arguments

data

A data frame to analyze.

patterns

Named list of regex patterns for column detection. Defaults to measure_column_patterns. Custom patterns can be provided as a named list where names become the detected type.

Value

A tibble with columns:

column

Column name

type

Detected type (from pattern names, or "other" if no match)

suggested_role

Suggested recipe role based on type

n_values

Number of non-NA values

class

R class of the column

Details

Column type detection uses the following naming conventions:

Prefix	Type	Suggested Role	Use Case
wn_*	wavenumber	predictor	IR spectroscopy (cm^-1)
nm_*	wavelength	predictor	UV-Vis, NIR spectroscopy
rt_*	retention_time	predictor	Chromatography
mz_*	mz	predictor	Mass spectrometry
ppm_*	ppm	predictor	NMR spectroscopy
ch_*	channel	predictor	Generic channel data
x_*	generic	predictor	Generic measurements

Columns not matching any pattern are classified as "other" and suggested as either "outcome" (if numeric), "id" (if character/factor with unique values), or "predictor".

Examples

# Wide format spectral data
df <- data.frame(
  sample_id = 1:5,
  outcome = rnorm(5),
  wn_1000 = rnorm(5),
  wn_1001 = rnorm(5),
  wn_1002 = rnorm(5)
)
measure_identify_columns(df)
#> # A tibble: 5 × 5
#>   column    type       suggested_role n_values class  
#>   <chr>     <chr>      <chr>             <int> <chr>  
#> 1 sample_id other      predictor             5 integer
#> 2 outcome   other      outcome               5 numeric
#> 3 wn_1000   wavenumber predictor             5 numeric
#> 4 wn_1001   wavenumber predictor             5 numeric
#> 5 wn_1002   wavenumber predictor             5 numeric

# Chromatography data
df2 <- data.frame(
  id = letters[1:3],
  concentration = c(1.2, 2.3, 3.4),
  rt_0.5 = rnorm(3),
  rt_1.0 = rnorm(3),
  rt_1.5 = rnorm(3)
)
measure_identify_columns(df2)
#> # A tibble: 5 × 5
#>   column        type           suggested_role n_values class    
#>   <chr>         <chr>          <chr>             <int> <chr>    
#> 1 id            other          id                    3 character
#> 2 concentration other          outcome               3 numeric  
#> 3 rt_0.5        retention_time predictor             3 numeric  
#> 4 rt_1.0        retention_time predictor             3 numeric  
#> 5 rt_1.5        retention_time predictor             3 numeric