    library(tidynorm)

If you’ve carried out a few different normalization procedures on a single dataset, you can double check what the sequence of operations was with `check_norm()`. For example, in this workflow, we’ve given the new normalized data columns uninformative names.

    norm_data <- speaker_data |>
      norm_lobanov(
        F1:F3,
        .by = speaker,
        .names = "{.formant}_norm1",
        .silent = T
      ) |>
      norm_nearey(
        F1:F3,
        .by = speaker,
        .names = "{.formant}_norm2",
        .silent = T
      )

We can review which normalization procedure produced which normalized column like so.

    check_norm(norm_data)
    #> Normalization Step
    #> • normalized with `tidynorm::norm_lobanov()`
    #> • normalized `F1`, `F2`, and `F3`
    #> • normalized values in `F1_norm1`, `F2_norm1`, and `F3_norm1`
    #> • grouped by `speaker`
    #> • within formant: TRUE
    #> • (.formant - mean(.formant, na.rm = T))/(sd(.formant, na.rm = T))
    #>
    #>
    #> Normalization Step
    #> • normalized with `tidynorm::norm_nearey()`
    #> • normalized `F1`, `F2`, and `F3`
    #> • normalized values in `F1_norm2`, `F2_norm2`, and `F3_norm2`
    #> • grouped by `speaker`
    #> • within formant: FALSE
    #> • (.formant - mean(.formant, na.rm = T))/(1)