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Copy path2) ProPer preparation (Praat-to-R).Rmd
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2) ProPer preparation (Praat-to-R).Rmd
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# ProPer preparation (II): Praat-to-R
Collect data from Praat objects into an R dataframe.
```{r clean_start, warning=FALSE}
rm(list = ls())
## Load required libraries
require(rPraat)
require(stringr)
require(dplyr)
```
# Harvest acoustic data
```{r pre_prepare_raw_df, warning=FALSE}
##########--- Read and collect Praat data into R tables ---##########
##### Intensity
# (get intensity data)
files_intensity <- list.files(path="praat_data/intensity_tiers/", pattern="*.IntensityTier",full.names=T)
intensity_df <- plyr::ldply(files_intensity, function(f){
filename <- str_match(f,".*/([^/.]*)\\.[^/]*$")
file <- filename[,2]
intensitier <- it.read(f)
time = round(intensitier$t,3)*1000
data.frame(file, t=as.integer(as.character(time)), intensity = round(intensitier$i,4))
})
##### Full-time
# (get the full time of the file from the intensity tier)
# fullTime_df <- plyr::ldply(files_intensity, function(f){
# filename <- str_match(f,".*/([^/.]*)\\.[^/]*$")
# file <- filename[,2]
# filenameBits <- str_match(f,".*/([^/._]*)_([^/._]*)\\.[^/]*$")
# speaker <- filenameBits[,2]
# it <- it.read(f)
# time <- seq(it[["tmin"]], it[["tmax"]], 0.001) * 1000
# data.frame(file, t=as.integer(as.character(time)), speaker)
# })
################
# (get the full time of the file from the intensity tier)
fullTime_df <- plyr::ldply(files_intensity, function(f){
filename <- str_match(f,".*/([^/.]*)\\.[^/]*$")
file <- filename[,2]
filenameBits <- str_match(f,".*/([^/._]*)_([^/._]*)_([^/._]*)_([^/._]*)_([^/._]*)_([^/._]*)_([^/._]*)_([^/._]*)_([^/._]*)\\.[^/]*$")
speaker <- filenameBits[,2]
clause <- filenameBits[,3]
sex <- filenameBits[,4]
setting <- filenameBits[,5]
addressee <- filenameBits[,6]
floorChange <- filenameBits[,7]
answer <- filenameBits[,8]
mandFulfilled <- filenameBits[,9]
excuse <- filenameBits[,10]
it <- it.read(f)
time <- seq(it[["tmin"]], it[["tmax"]], 0.001) * 1000
data.frame(file, t=as.integer(as.character(time)), speaker, clause, sex, setting, addressee, floorChange, answer, mandFulfilled, excuse)
})
################
##### F0: Pitch Tier
# (get the smooth F0 curve)
files_pitchTier <- list.files(path="praat_data/pitch_tiers/", pattern="*.PitchTier",full.names=T)
f0_smooth_df <- plyr::ldply(files_pitchTier, function(f){
filename <- str_match(f,".*/([^/.]*)\\.[^/]*$")
file <- filename[,2]
pt <- pt.read(f)
time = round(pt[["t"]],3)*1000
f0 = pt[["f"]]
data.frame(file, t=as.integer(as.character(time)), f0_smooth=round(f0,2))
})
##### Pitch object
# (get the Strength, i.e. the periodic fraction from Praat's autocorrelation, and frequency of selected pitch candidates: rowmax = highest strength value within the frequency range (up to 'pitch_ceiling'); row1 = Praat's path finder choice for F0 candidates
# (Note: this may take longer to process!!!)
files_pitchObject <- list.files(path="praat_data/pitch_objects/", pattern="*.Pitch",full.names=T)
pitchObject_df <- plyr::ldply(files_pitchObject, function(f){
filename <- str_match(f,".*/([^/.]*)\\.[^/]*$")
file <- filename[,2]
pitch_object <- pitch.read(f)
time <- round(pitch_object$t,3)*1000
pitch_ceiling <- 1000 #fixed to periods up to 1000Hz
strength_row1 <- apply(pitch.toArray(pitch_object)[["strengthArray"]], 2, function(x) x[1])
f0_row1 <- apply(pitch.toArray(pitch_object)[["frequencyArray"]], 2, function(x) x[1])
f0_row1[which(f0_row1==0)] <- NA
strengthArray <- apply(as.data.frame(pitch.toArray(pitch_object)[["strengthArray"]]), 2, function(x) ifelse(x==0,NA,x))
freqArray <- apply(as.data.frame(pitch.toArray(pitch_object)[["frequencyArray"]]), 2, function(x) ifelse(x==0,NA,x))
zero_one_freqs <- apply(freqArray, 2, function(x) ifelse(x>pitch_ceiling, 0, 1))
strength_limited <- strengthArray
strength_limited[, -1] <- mapply(`*`, strengthArray[, -1], zero_one_freqs[, -1])
strength_rowmax <- apply(strength_limited, 2, max, na.rm=T)
strength_rowmax[is.infinite(strength_rowmax)] <- 0
data.frame(file, t=as.integer(as.character(time)), strength_row1=round(strength_row1,7), f0_row1=round(f0_row1,2), strength_rowmax=round(strength_rowmax,7))
})
```
# Read TextGrids: 'Syllable' tier
```{r pre_prepare_TextGrids_Syllable, warning=FALSE}
##### TextGrid
# TextGrids are optional! They are useful for exposition and to improve the automatic detection. By default, TextGrids are expected with at least one interval tier demarcating syllabic boundaries
#
files_textGrid <- list.files(path="praat_data/textgrids/", pattern="*.TextGrid",full.names=T)
# The following chunk takes syllabic intervals and labels from the "Syllable" tier
if(length(files_textGrid)>0) textGridSyll_df <- plyr::ldply(files_textGrid, function(f){
filename <- str_match(f,".*/([^/.]*)\\.[^/]*$")
file <- filename[,2]
tg <- tg.read(f, encoding = "auto")
syll_tier <- data.frame(tg$seg)
t1 <- ifelse(syll_tier$label=="", NA, round(syll_tier$t1,3)*1000)
t2 <- ifelse(syll_tier$label=="", NA, round(syll_tier$t2,3)*1000)
t_mid <- round((t1+t2)/2)
syll_label <- syll_tier$label
data.frame(file, t=as.integer(as.character(t1)), syll_start=as.integer(as.character(t1)), syll_mid=as.integer(as.character(t_mid)), syll_end=as.integer(as.character(t2)), syll_bounds=as.integer(as.character(t1)), syll_label)
})
if(exists("textGridSyll_df")) textGridSyll_df <- mutate(
group_by(textGridSyll_df,file),
syll_bounds = ifelse(
(is.na(syll_bounds) & !is.na(lag(syll_end,1))),
lag(syll_end,1),
syll_bounds),
t = syll_bounds
)
#
if(exists("textGridSyll_df")) textGridSyll_df <- filter(textGridSyll_df, !is.na(t))
```
# Read TextGrids: 'Word' tier
```{r pre_prepare_TextGrids_Word, warning=FALSE}
# The following chunk takes word intervals and labels from the "Word" tier (for exposition purposes)
if(length(files_textGrid)>0) textGridWord_df <- plyr::ldply(files_textGrid, function(f){
filename <- str_match(f,".*/([^/.]*)\\.[^/]*$")
file <- filename[,2]
tg <- tg.read(f, encoding = "auto")
word_tier <- data.frame(tg$Word)
word_t1 <- ifelse(word_tier$label=="", NA, round(word_tier$t1,3)*1000)
word_t2 <- ifelse(word_tier$label=="", NA, round(word_tier$t2,3)*1000)
word_t_mid <- round((word_t1+word_t2)/2)
word_label <- word_tier$label
data.frame(file, t=as.integer(as.character(word_t1)), word_start=as.integer(as.character(word_t1)), word_mid=as.integer(as.character(word_t_mid)), word_end=as.integer(as.character(word_t2)), word_bounds=as.integer(as.character(word_t1)), word_label)
})
if(exists("textGridWord_df")) textGridWord_df <- mutate(
group_by(textGridWord_df,file),
word_bounds = ifelse(
(is.na(word_bounds) & !is.na(lag(word_end,1))),
lag(word_end,1),
word_bounds),
t = word_bounds
)
#
if(exists("textGridWord_df")) textGridWord_df <- filter(textGridWord_df, !is.na(t))
```
# Combine data into raw_df
```{r prepare_raw_df, warning=FALSE}
##### Combine all data
raw_df <- left_join(fullTime_df, f0_smooth_df, by = c("file", "t"))
raw_df <- left_join(raw_df, intensity_df, by = c("file", "t"))
raw_df <- left_join(raw_df, pitchObject_df, by = c("file", "t"))
if(exists("textGridSyll_df")) raw_df <- left_join(raw_df, textGridSyll_df, by = c("file", "t"))
if(exists("textGridWord_df")) raw_df <- left_join(raw_df, textGridWord_df, by = c("file", "t"))
##### Calculate and add the total-power and the periodic-power vectors
raw_df <- mutate(group_by(raw_df,file),
total_power = round(4e-10 * 10^(intensity / 10), 9),
periodic_power = round(total_power * strength_rowmax, 9)
)
```
# Write raw_df table
```{r write_raw_df, warning=FALSE}
##### Write the raw data
write.csv(raw_df, "data_tables/raw_df.csv", row.names=FALSE)
```