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5) ProPerMSA_scores.Rmd
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5) ProPerMSA_scores.Rmd
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# ProPer scores (V): allocate ProPer values to manually segmented intervals, for data aggregation and stats
```{r clean_start}
rm(list = ls())
## Load required libraries
require(dplyr)
require(zoo)
comp_df <- read.csv("data_tables/comp_df.csv") %>% distinct(file, t, .keep_all = TRUE)
## interpolate observations from the TextGrid manual annotations
comp_df <- mutate(
group_by(comp_df, file),
syll_mid = na.locf(syll_mid, na.rm=F),
syll_label = na.locf(syll_label, na.rm=F),
word_label = na.locf(word_label, na.rm=F)
)
```
# Prepare the scores_df table
```{r prepare_scores_df, warning=FALSE}
## check up on correct allocations: check how many CoMs within manually segmented intervals and choose the one associated with highest mass if there are multiple CoMs
scores_df <- mutate(
group_by(comp_df, file),
## put CoM values in their time's row
CoMatCoM = ifelse(CoM == t , CoM, NA)
)
scores_df <- mutate(
## calcualte the following for each syllabic interval (manual)
group_by(scores_df, file, syll_mid),
## find the CoMs associated with each syllabic interval
syllCoMMap = ifelse( (all(is.na(CoMatCoM)) | is.na(syll_mid)) , NA,
ifelse( length(which(!is.na(CoMatCoM))) == 1, mean(CoMatCoM, na.rm = T),
# use '0' when there are multiple CoMs
ifelse( length(which(!is.na(CoMatCoM))) > 1, 0 ))),
# choose the stonger mass_rel if there are multiple CoMs
syllCoMMap2 = ifelse(syllCoMMap==0 & CoMatCoM==t, mass_rel, NA),
syllCoMMap2 = max(syllCoMMap2, na.rm = T),
syllCoMMap = ifelse(syllCoMMap==0 & CoMatCoM==t, syllCoMMap2, syllCoMMap),
syllCoMMap = ifelse(syllCoMMap==0, NA, syllCoMMap),
## convert mass_rel to corresponding CoM
syllCoMflicts = ifelse( (length(which(!is.na(CoMatCoM))) > 1 & syllCoMMap != mass_rel) , NA, CoM ),
## strectch the relevant CoM values within syllabic intervals
syllCoMMap = ifelse( length(which(!is.na(CoMatCoM))) > 1, mean(syllCoMflicts, na.rm = T), syllCoMMap ),
### the following ProPer parameters: use the values associated with the relevant CoM
intervalDuration = ifelse( syllCoMMap == t, intervalDuration, NA),
intervalDuration = ifelse( all(is.na(intervalDuration)), NA, max(intervalDuration, na.rm = T) ),
CoM = ifelse( syllCoMMap == t, CoM, NA),
CoM = ifelse( all(is.na(CoM)), NA, max(CoM, na.rm = T) ),
CoG = ifelse( syllCoMMap == t, CoG, NA),
CoG = ifelse( all(is.na(CoG)), NA, max(CoG, na.rm = T) ),
f0atCoM = ifelse( syllCoMMap == t, f0atCoM, NA),
f0atCoM = ifelse( all(is.na(f0atCoM)), NA, max(f0atCoM, na.rm = T) ),
DeltaF0 = ifelse( syllCoMMap == t, DeltaF0, NA),
DeltaF0 = max(DeltaF0, na.rm = T),
# DeltaF0 = ifelse( all(is.na(DeltaF0)), NA, max(DeltaF0, na.rm = T) ),
DeltaF0_rel = ifelse( syllCoMMap == t, DeltaF0_rel, NA),
DeltaF0_rel = ifelse( all(is.na(DeltaF0_rel)), NA, max(DeltaF0_rel, na.rm = T) ),
synchrony = ifelse( syllCoMMap == t, synchrony, NA),
synchrony = ifelse( all(is.na(synchrony)), NA, max(synchrony, na.rm = T) ),
sync_rel = ifelse( syllCoMMap == t, sync_rel, NA),
sync_rel = ifelse( all(is.na(sync_rel)), NA, max(sync_rel, na.rm = T) ),
mass_rel = ifelse( syllCoMMap == t, mass_rel, NA),
mass_rel = ifelse( all(is.na(mass_rel)), NA, max(mass_rel, na.rm = T) )
)
## reduce rows
# scores_df <- dplyr::filter(scores_df, syll_mid==t)
scores_df <- dplyr::filter(scores_df, syllCoMMap == t)
## reduce columns
mini_scores_df <- droplevels(subset(scores_df, select = -c(t, syll_start, syll_mid, syll_end, syll_bounds, word_start, word_mid, word_end, word_bounds, perFloorStatus, relToStatus, strengThreshStatus, f0_data_min, f0_data_max, f0_data_range, smogPP_20Hz, smogPP_12Hz, smogPP_8Hz, smogPP_5Hz, f0_interp_stretch_smooth, f0_interp_smooth, f0_realFloorStretch, syll_boundsSeq, useManualStatus, autoManStatus, averageSyllStatus, auto_bounds, auto_boundsSeq, f0atCoG, PERatCoM, PERatCoG, CoM_corr, CoG_corr, CoMatCoM, syllCoMMap, syllCoMMap2, syllCoMflicts)))
## clean NAs in word_label
mini_scores_df$word_label[which(is.na(mini_scores_df$word_label))] <- ""
## rename columns
colnames(mini_scores_df)[3] <- "condition"
colnames(mini_scores_df)[5] <- "syllable"
colnames(mini_scores_df)[6] <- "word"
nano_scores_df <- droplevels(subset(mini_scores_df, select = -c(f0_speaker_min, f0_speaker_max, f0_speaker_median, f0_speaker_mean, f0_speaker_range, f0_token_min, f0_token_max, f0_token_median, f0_token_mean, f0_token_range, CoM, CoG, intervalDuration_rel, DeltaF0Label, syncLabel, localSpeechRate)))
```
# Write scores_df table (not necessary)
```{r write_scores_df}
## Write the scores data file
# write.csv(scores_df, "data_tables/mini_scores_df.csv", row.names=FALSE)
# write.csv(nano_scores_df, "data_tables/tiny_scores_df.csv", row.names=FALSE)
```
# Add MSA data
```{r read MSA trial-lists}
## Read the single MSA trial list files
dir_singles <- "data_tables/trial-lists/"
files <- list.files(path=dir_singles, pattern="*.csv",full.names=T)
trial_lists_df <- plyr::ldply(files, function(f){
trials <- read.csv(f,header=T, sep=",")
})
# remove underscore from MSA names JW_2 and JW_3
trial_lists_df$speaker <- as.character(trial_lists_df$speaker)
trial_lists_df$speaker[which(trial_lists_df$speaker == "JW_2")] <- "JW2"
trial_lists_df$speaker[which(trial_lists_df$speaker == "JW_3")] <- "JW3"
trial_lists_df$speaker <- as.factor(trial_lists_df$speaker)
# ---
# locate relevant trial #
trial_lists_df$trial[which(trial_lists_df$condition=="NF" & trial_lists_df$speaker=="TS" & (trial_lists_df$target_name=="Zitrone" | trial_lists_df$target_name=="Trauben"))]
# ---
## Write the MSA combied data file
write.csv(trial_lists_df, "data_tables/trial_lists_combined.csv", row.names=FALSE)
## combine MSA data wirh ProPer scores
ProPerMSA <- left_join(nano_scores_df, trial_lists_df, by = c("speaker", "trial", "condition"))
# temporary fix
# ProPerMSA$syllable[which(ProPerMSA$syllable=="\nab")] <- "ab"
# droplevels(ProPerMSA$syllable)
## Write the ProPer MSA scores data file
write.csv(ProPerMSA, "data_tables/ProPer_MSA_scores.csv", row.names=FALSE)
```