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