Using tibbles
A common use case of the phonetise() function is with
tibble columns that have IPA transcriptions. The phonetisr package comes
with kl_swades, a tibble with 195 Klingon words and their
IPA transcription.
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.5
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.4
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
data("kl_swadesh")
kl_swadesh
#> # A tibble: 195 × 4
#> id gloss translit ipa
#> <int> <chr> <chr> <chr>
#> 1 1 I jIH dʒɪx
#> 2 2 you (singular) SoH ʂox
#> 3 3 he ghaH ɣɑx
#> 4 4 we maH mɑx
#> 5 5 you (plural) tlhIH tɬɪx
#> 6 6 they chaH tʃɑx
#> 7 7 this -vam -vɑm
#> 8 8 that -vetlh -vɛtɬ
#> 9 9 here naDev nɑɖɛv
#> 10 10 there pa' pʰɑʔ
#> # ℹ 185 more rowsLet’s phonetise the ipa column. We first want to define
multi-character phones.
Then, we can use mutate() to create a new column
phones with phonetised transcriptions.
kl_swadesh <- kl_swadesh |>
mutate(
phones = phonetise(ipa, multi = kl_multi)
)
#> ℹ The following non-IPA characters were found and removed: -
#>
kl_swadesh
#> # A tibble: 195 × 5
#> id gloss translit ipa phones
#> <int> <chr> <chr> <chr> <list>
#> 1 1 I jIH dʒɪx <chr [3]>
#> 2 2 you (singular) SoH ʂox <chr [3]>
#> 3 3 he ghaH ɣɑx <chr [3]>
#> 4 4 we maH mɑx <chr [3]>
#> 5 5 you (plural) tlhIH tɬɪx <chr [3]>
#> 6 6 they chaH tʃɑx <chr [3]>
#> 7 7 this -vam -vɑm <chr [3]>
#> 8 8 that -vetlh -vɛtɬ <chr [3]>
#> 9 9 here naDev nɑɖɛv <chr [5]>
#> 10 10 there pa' pʰɑʔ <chr [3]>
#> # ℹ 185 more rowsphones is a “list” column, so kl_swadesh is
“nested”: see the Nested data
vignette of tidyr.
A common operation is to count the number of occurrences of each
phone. We can easily do that by first “unnesting” the
phones column.
kl_unnest <- kl_swadesh |>
unnest(phones)
kl_unnest
#> # A tibble: 651 × 5
#> id gloss translit ipa phones
#> <int> <chr> <chr> <chr> <chr>
#> 1 1 I jIH dʒɪx dʒ
#> 2 1 I jIH dʒɪx ɪ
#> 3 1 I jIH dʒɪx x
#> 4 2 you (singular) SoH ʂox ʂ
#> 5 2 you (singular) SoH ʂox o
#> 6 2 you (singular) SoH ʂox x
#> 7 3 he ghaH ɣɑx ɣ
#> 8 3 he ghaH ɣɑx ɑ
#> 9 3 he ghaH ɣɑx x
#> 10 4 we maH mɑx m
#> # ℹ 641 more rowsThen we can count each phone with count().
kl_unnest |>
count(phones, sort = TRUE)
#> # A tibble: 26 × 2
#> phones n
#> <chr> <int>
#> 1 ɑ 65
#> 2 o 50
#> 3 ʔ 40
#> 4 ɪ 38
#> 5 u 34
#> 6 x 31
#> 7 ɣ 30
#> 8 ɛ 29
#> 9 ʂ 28
#> 10 m 25
#> # ℹ 16 more rowsAnd also plot the counts.
kl_unnest |>
count(phones, sort = TRUE) |>
ggplot(aes(reorder(phones, desc(n)), n)) +
geom_bar(stat = "identity")
