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Vitamin D Status

Source: vignettes/articles/vitamin_d_status.Rmd
vitamin_d_status.Rmd

Scope

vitamin_d_status() classifies serum 25(OH)D (assumed ng/mL) into Deficient (<20), Insufficient (20-29), or Sufficient (>=30). It validates mappings, enforces your missing-data policy, and can scan/cap implausible values.

When to use this

  • Standardize vitamin D categorization for dashboards, cohorts, or QC summaries.
  • Guard ETL steps: abort or omit rows with missing/implausible 25(OH)D before downstream models.
  • Spot potential unit mix-ups: nmol/L inputs often appear ~2.5x higher than ng/mL.

Required inputs and mapping

  • data: data frame/tibble containing a 25(OH)D column.
  • col_map: named list mapping either vitd or vitamin_d to the column name. The column must exist and be coercible to numeric; coercion that introduces NAs emits a warning.

Options that change behavior

  • na_action = c("keep","omit","error","ignore","warn") (default “keep”): policy for missing 25(OH)D. warn/ignore behave like keep; omit drops those rows; error aborts.
  • verbose = FALSE: emit progress messages.

How missingness is handled

  • Missing 25(OH)D follows na_action; omit shrinks the output; error aborts.
  • Non-numeric inputs are coerced to numeric; any NAs introduced trigger a warning.
  • Negative values and very high medians (suggesting nmol/L) warn when values appear implausible.

Outputs

  • Tibble with ordered factor vitamin_d_status (Deficient < Insufficient < Sufficient).
  • Rows drop only when na_action = "omit"; otherwise the output aligns with input row count.

Examples

Quick categorization

library(HealthMarkers)
library(tibble)

df <- tibble::tibble(VitD = c(12, 25, 36, NA))

vitamin_d_status(
  data = df,
  col_map = list(vitd = "VitD"),
  na_action = "keep"
)
#> # A tibble: 4 × 1
#>   vitamin_d_status
#>   <ord>           
#> 1 Deficient       
#> 2 Insufficient    
#> 3 Sufficient      
#> 4 NA

Pre-filter and classify

df2 <- tibble::tibble(d = c(15, 280, 8, 65, NA))  # 280 is implausible; pre-filter

# Pre-filter before calling
df2$d[df2$d > 250] <- NA

vitamin_d_status(
  data = df2,
  col_map = list(vitd = "d"),
  na_action = "warn",
  verbose = TRUE
)
#> # A tibble: 5 × 1
#>   vitamin_d_status
#>   <ord>           
#> 1 Deficient       
#> 2 NA              
#> 3 Deficient       
#> 4 Sufficient      
#> 5 NA

Verbose diagnostics 

old_opt <- options(healthmarkers.verbose = "inform")
vitamin_d_status(
  data = tibble::tibble(VitD = c(15, 32)),
  col_map = list(vitamin_d = "VitD"),
  verbose = TRUE
)
#> vitamin_d_status(): reading input 'data' — 2 rows × 1 variables
#> vitamin_d_status(): col_map (2 columns — 1 specified, 1 inferred from data)
#>   vitamin_d         ->  'VitD'
#>   vitd              ->  'VitD'    (inferred)
#> vitamin_d_status(): computing markers:
#>   vitamin_d_status [VitD]
#> vitamin_d_status(): results: vitamin_d_status 2/2
#> # A tibble: 2 × 1
#>   vitamin_d_status
#>   <ord>           
#> 1 Deficient       
#> 2 Sufficient
options(old_opt)

Troubleshooting and tips

  • If medians are ~150-250 without obvious outliers, verify units (nmol/L should be divided by 2.5 to get ng/mL).
  • Set na_action = "omit" when downstream steps cannot handle NA statuses.
  • Keep col_map named; missing or empty mappings abort with classed errors you can assert in tests.