library(dplyr)
library(tidyr)
library(readr)
library(readxl)
library(stringr)
# read in data
# Lev-Ari sentences and conversations
fowler_items <-
read_csv('fowler-items.csv') %>%
gather(phoneme, word, p:k) %>%
mutate(item = str_c(item, phoneme))
lev_ari_sentences <-
read_excel('VOT sentnece results.xlsx') %>%
left_join(fowler_items, by = c('item', 'phoneme')) %>%
transmute(source = 'levari-sent',
subject = str_c('la_', subject),
phoneme = phoneme,
vot = vot,
prevoiced = NA,
word = word,
sex = sex,
age = age,
age_group = ifelse(age < 40, 'y', 'o'),
bilingual = TRUE)
lev_ari_convo <- read_excel('VOT conversation results.xlsx') %>%
transmute(source = 'levari-convo',
subject = str_c('la_', subject),
phoneme = phoneme,
vot = vot,
prevoiced = NA,
word = word,
sex = sex,
age_group = ifelse(age < 40, 'y', 'o'),
age = age,
bilingual = TRUE)
# Goldrick et al. (2013) and Baese-Berk & Goldrick (2009)
goldrick2013_sex <- read_tsv('voiced-initial-gender.tsv') %>%
mutate(Subject = as.numeric(Subject),
Sex = tolower(Sex))
goldrick2013 <- read_tsv('Goldrick2013_voicedInitial.txt') %>%
left_join(goldrick2013_sex, by='Subject') %>%
transmute(source = 'gva13',
subject = str_c('gva13_', Subject),
phoneme = Consonant,
vot = VOT * 1000,
prevoiced = Prevoiced %>% round() %>% as.logical(),
word = Word,
sex = Sex,
age = NA,
bilingual = FALSE)
bbg <- read_csv('BBG-Trimmed.csv')
baeseberk_goldrick2009 <- read_csv('BBG-Trimmed.csv') %>%
mutate(study = ifelse(consonant == 'p', 'a', 'b')) %>%
transmute(source = 'bbg09',
subject = str_c('bbg09', study, Subject, sep='_'),
phoneme = consonant,
vot = VOT * 1000,
prevoiced = NA,
word = Word,
sex = NA,
age = NA,
bilingual = FALSE)
# Wedel/Buckeye
buckeye_speakers <- read_csv('buckeye-speakers.csv') %>%
mutate(speaker = tolower(speaker))
buckeye <- bind_rows(read_csv('Wedel_VoicelessStopDataFromBuckeye.csv'),
read_csv('Wedel_VoicedStopDataFromBuckeye.csv')) %>%
left_join(buckeye_speakers, by=c(Speaker='speaker')) %>%
transmute(source = 'buckeye',
subject = str_c('buckeye_', Speaker),
phoneme = Phoneme,
vot = VOT * 1000,
prevoiced = NA,
word = Ortho,
sex = speaker_gender,
age_group = speaker_age,
bilingual = FALSE,
speech_rate = SpeechRate,
stop_length = StopLength * 1000)
# Allen and Miller (1999)
allen_miller <- read_csv('allen_miller_JASA_1999_data.csv')
allen_miller <- allen_miller %>%
transmute(source = 'allen-miller',
subject = str_c("am", sub_id, exp_num, sep="_"),
phoneme = str_sub(word, 1, 1),
vot = VOT,
vowel_duration = vowel,
final_phoneme =
word %>%
str_split(pattern="[aeiou]+") %>%
map_chr(~.[2]) %>%
factor() %>%
fct_recode("k"="ck", "d"="ld"),
final_closure = closure,
final_aspiration = aspiration,
speech_rate_condition = factor(speed, levels=c("slow", "fast")),
prevoiced = NA,
word = word,
sex = NA,
age = NA,
bilingual = FALSE)
# Put it all together
stops <- tibble(phoneme = c('b', 'd', 'g', 'p', 't', 'k'),
voicing = rep(factor(c('voiced', 'voiceless')), each=3),
place = rep(factor(c('lab', 'cor', 'dor'),
levels = c('lab', 'cor', 'dor')),
times=2))
vot <- bind_rows(lev_ari_sentences,
lev_ari_convo,
goldrick2013,
baeseberk_goldrick2009,
buckeye,
allen_miller) %>%
mutate(phoneme = tolower(phoneme)) %>%
left_join(stops, by="phoneme") %>%
mutate(phoneme = factor(phoneme, levels = c('b', 'd', 'g', 'p', 't', 'k')))
usethis::use_data(vot, overwrite=TRUE)
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