Skip to contents

Normalise marker columns in a data frame

Source: R/globals.R
hm_normalize.Rd

A convenience wrapper around normalize_vec() that applies a normalisation method to every selected numeric column in a data frame and returns the modified data frame. The most common use-case is to normalise the output of any HealthMarkers function — especially domain functions such as glycemic_markers(), lipid_markers(), or renal_markers() whose internal normalize argument currently has no effect.

Usage

hm_normalize(
  data,
  cols = NULL,
  method = c("z", "inverse", "range", "robust"),
  skip_cols = NULL,
  ...
)

Arguments

data

A data frame (or tibble) containing marker columns to normalise.

cols

Character vector of column names to normalise. If NULL (default), all numeric columns in data are normalised.

method

One of c("z","inverse","range","robust"). Passed directly to normalize_vec():

"z"

z-score (mean 0, sd 1).

"inverse"

Rank-based inverse-normal transform (Rankit; default).

"range"

Min-max scaling to feature_range (default [0, 1]).

"robust"

Median/MAD scaling.

skip_cols

Character vector of column names to leave untouched even if they are numeric (e.g. c("age", "BMI")). Ignored when cols is explicitly supplied.

...

Additional arguments forwarded to normalize_vec() (e.g. feature_range, invnorm_denominator, ties).

Value

The input data with the selected columns replaced by their normalised values. Class (tibble, data.frame, etc.) is preserved.

See also

normalize_vec() for single-vector normalisation.

Examples

# Build a tiny data frame with pre-computed marker columns
df <- data.frame(
  age    = c(45, 52, 61),
  HOMA_IR = c(1.2, 3.4, 2.1),
  TyG     = c(8.1, 9.0, 8.6),
  NLR     = c(2.1, 3.5, 1.8)
)

marker_cols <- c("HOMA_IR", "TyG", "NLR")

# z-score normalise marker columns only
hm_normalize(df, cols = marker_cols, method = "z")
#>   age    HOMA_IR         TyG        NLR
#> 1  45 -0.9342606 -1.03490978 -0.4040951
#> 2  52  1.0548104  0.96098765  1.1388135
#> 3  61 -0.1205498  0.07392213 -0.7347184

# Inverse-normal transform, leaving age untouched
hm_normalize(df, method = "inverse", skip_cols = "age")
#>   age    HOMA_IR        TyG        NLR
#> 1  45 -0.9674216 -0.9674216  0.0000000
#> 2  52  0.9674216  0.9674216  0.9674216
#> 3  61  0.0000000  0.0000000 -0.9674216