alecristia
commented
3 years ago Many of the variables are calculated in the R code below:
its <- extractDataCR("its",tmp,LENA.OL = F)
### speech summary
summa = summary(its)
speech <- summa$data %>% filter(speaker_type %in% c("CHI","FEM","MAL","OCH")) %>%
mutate(child_id = as.character(child_id),)%>%
group_by(child_id,age_in_day)%>%
mutate(
prop_voc = voc / sum(voc) #modification of proportion
)
speech.ph <- its$data %>%
dplyr::mutate(duration = segment_offset - segment_onset,
hour = lubridate::hour(POSIXct_time_onset),) %>%
dplyr::arrange(child_id,age_in_day) %>%
dplyr::group_by(child_id,age_in_day,speaker_type,experiment,hour) %>%
dplyr::summarise(
voc = dplyr::n(),
voc_ph = dplyr::n()/(max(range_offset)/3600000),
avg_voc_dur = mean(duration),
voc_dur = sum(duration),
voc_dur_ph = sum(duration)/(max(range_offset)/3600000),
) %>%
dplyr::group_by(child_id,age_in_day,experiment) %>%
dplyr::mutate(
prop_voc = voc / sum(voc),
) %>%
dplyr::ungroup()%>% filter(speaker_type %in% c("CHI","FEM","MAL","OCH")) %>%
mutate(child_id = as.character(child_id),)%>%
group_by(child_id,age_in_day,hour)%>%
mutate(
prop_voc = voc / sum(voc) #modification of proportion
)
### cannonical summary
cannonical <- its$data %>%
mutate(child_id = as.character(child_id),
duration = segment_offset -segment_onset,
)%>%
filter(speaker_type %in% c("CHI","FEM","MAL","OCH")) %>%
group_by(child_id,age_in_day,speaker_type) %>%
mutate (
cry = ifelse(child_cry_vfx_len>0,1,0)
) %>%
summarise(
can_voc = sum(utterances_count),
can_voc_ph = sum(utterances_count)/((max(range_offset)/1000)/3600),
can_voc_dur = sum(utterances_length),
can_voc_dur_ph = sum(utterances_length)/((max(range_offset)/1000)/3600),
cry_voc = sum(cry),
cry_voc_ph = sum(cry)/((max(range_offset)/1000)/3600),
cry_voc_dur = sum(child_cry_vfx_len),
cry_voc_dur_ph = sum(child_cry_vfx_len)/((max(range_offset)/1000)/3600),
)
cannonical.ph <- its$data %>%
mutate(child_id = as.character(child_id),
duration = segment_offset -segment_onset,
hour = lubridate::hour(POSIXct_time_onset),
)%>%
filter(speaker_type %in% c("CHI","FEM","MAL","OCH")) %>%
group_by(child_id,age_in_day,speaker_type,hour) %>%
mutate (
cry = ifelse(child_cry_vfx_len>0,1,0)
) %>%
summarise(
can_voc = sum(utterances_count),
can_voc_ph = sum(utterances_count)/((max(range_offset)/1000)/3600),
can_voc_dur = sum(utterances_length),
can_voc_dur_ph = sum(utterances_length)/((max(range_offset)/1000)/3600),
cry_voc = sum(cry),
cry_voc_ph = sum(cry)/((max(range_offset)/1000)/3600),
cry_voc_dur = sum(child_cry_vfx_len),
cry_voc_dur_ph = sum(child_cry_vfx_len)/((max(range_offset)/1000)/3600),
)
word <- its$data %>%
mutate(child_id = as.character(child_id),
duration = segment_offset -segment_onset,
cond.word = ifelse(words >0,1,0), #bool absence presence
)%>%
filter(speaker_type %in% c("FEM","MAL") & cond.word == 1) %>%
group_by(child_id,age_in_day,speaker_type) %>%
summarise(
word_nbr = sum(words),
word_mean_nbr = mean(words),
word_length = sum(duration),
word_mean_length = sum(duration),
)
word.ph <- its$data %>%
mutate(child_id = as.character(child_id),
duration = segment_offset -segment_onset,
cond.word = ifelse(words >0,1,0), #bool absence presence
hour = lubridate::hour(POSIXct_time_onset),
)%>%
filter(speaker_type %in% c("FEM","MAL") & cond.word == 1) %>%
group_by(child_id,age_in_day,speaker_type,hour) %>%
summarise(
word_nbr = sum(words),
word_mean_nbr = mean(words),
word_length = sum(duration),
word_mean_length = sum(duration),
)
For each recording, derive the metrics listed in the csv attached child-recordings-variables.csv