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Tseltal-CLE.R
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library(papaja)
library(ggplot2)
library(tidyverse)
library(viridis)
library(grid)
library(gridExtra)
library(ggpirate)
library(lme4)
library(glmmTMB)
library(DHARMa)
library(bbmle)
library(broom.mixed)
source("0-Helper.R")
options(scipen=999)
data.path <- "transcripts/anon/" # text files exported from ELAN
metadata.path <- "metadata/"
plot.path <- "plots/" # output plots
shiny.input.path <- "shiny_input/"
shiny.input.img.path <- "www/"
seg.index.file <- paste0(metadata.path, "Segment-order-inventory.csv")
ptcp.info.file <- paste0(metadata.path, "recording-info.csv")
comparison.file <- paste0(metadata.path, "comparison_studies.csv")
samplelabels <- c("High activity ", "Random ")
col.sample.bu <- list(
scale_fill_manual(labels=samplelabels, values=viridis(2)),
scale_color_manual(labels=samplelabels, values=viridis(2)))
col.sample.bu3 <- list(
scale_fill_manual(labels=samplelabels, values=viridis(3)),
scale_color_manual(labels=samplelabels, values=viridis(3)))
allowed.overlap <- 1000 #ms
allowed.gap <- 2000 #ms
waking.hours <- 14
# Read in annotation files
files <- list.files(path=data.path,pattern="*.txt")
all.data <- data.frame()
for (i in 1:length(files)) {
# print(files[i])
newfile <- read_csv(paste0(data.path, files[i]),
col_types = cols(val = col_character()))
newfile$aclew_child_id <- unlist(strsplit(files[i], '\\.'))[1]
all.data <- rbind(all.data, newfile)
}
all.data$row <- c(1:nrow(all.data))
# Read in supplementary data
ptcp.info <- read_csv(ptcp.info.file, col_types = cols()) %>%
dplyr::select(-row)
seg.info <- read_csv(seg.index.file)
# Extract and convert start time of each sample
seg.info$start.hhmmss <- regmatches(seg.info$Media,
regexpr("[[:digit:]]{6}", seg.info$Media))
seg.info$start.sec <- as.numeric(substr(seg.info$start.hhmmss,1,2))*3600 +
as.numeric(substr(seg.info$start.hhmmss,3,4))*60 +
as.numeric(substr(seg.info$start.hhmmss,5,6))
seg.info$start.hr <- round(seg.info$start.sec/3600, 3)
seg.info$clipoffset.hhmmss <- regmatches(seg.info$Media,
regexpr("(?<=[[:digit:]]{6}_)[[:digit:]]{6}",
seg.info$Media, perl = TRUE))
seg.info$clipoffset.sec <- as.numeric(substr(seg.info$clipoffset.hhmmss,1,2))*3600 +
as.numeric(substr(seg.info$clipoffset.hhmmss,3,4))*60 +
as.numeric(substr(seg.info$clipoffset.hhmmss,5,6))
seg.info$clipoffset.hr <- round(seg.info$clipoffset.sec/3600, 3)
# Add mean and sd values for participant-level predictors to ptcp.info
ptcp.info <- ptcp.info %>%
mutate(
tchiyr.m = mean(age_mo_round),
motyr.m = mean(mother_age),
nsb.m = mean(number_older_sibs),
hsz.m = mean(household_size),
tchiyr.sd = sd(age_mo_round),
motyr.sd = sd(mother_age),
nsb.sd = sd(number_older_sibs),
hsz.sd = sd(household_size)
)
# Merge in participant and segment info to the main data table
codes <- all.data %>% filter(tier == "code")
all.data <- all.data %>%
filter(speaker != "") %>%
left_join(ptcp.info, by = "aclew_child_id") %>%
mutate(segment = "", sample = "",
sample_type = "", segment_dur = 0)
for (i in 1:nrow(codes)) {
rec <- codes$aclew_child_id[i]
seg <- as.character(codes$val[i])
seg.on <- codes$start[i]
seg.off <- codes$stop[i]
seg.idx <- which(all.data$aclew_child_id == rec &
all.data$start < seg.off &
all.data$stop > seg.on)
all.data$segment[seg.idx] <- seg
}
# Label samples
all.data$sample[which(
grepl('^random', all.data$segment))] <- "random"
all.data$sample[which(
grepl('tt', all.data$segment))] <- "turn-taking"
all.data$sample[which(
grepl('va', all.data$segment))] <- "high-activity"
# Label sample types and durations
random.samples <- which(grepl('^random', all.data$segment))
all.data$sample_type[random.samples] <- "random"
all.data$segment_dur[random.samples] <- 5
ext.samples <- which(grepl('^extension', all.data$segment))
all.data$sample_type[ext.samples] <- "extension"
all.data$segment_dur[ext.samples] <- 5
tt.samples <- which(grepl('^tt', all.data$segment))
all.data$sample_type[tt.samples] <- "turn-taking"
all.data$segment_dur[tt.samples] <- 1
va.samples <- which(grepl('^va', all.data$segment))
all.data$sample_type[va.samples] <- "turn-taking"
all.data$segment_dur[va.samples] <- 1
# Add in segment start time
all.data <- all.data %>%
left_join(dplyr::select(seg.info, c("aclew_id", "CodeName", "start.hhmmss",
"start.sec", "start.hr")),
by = c("aclew_child_id" = "aclew_id", "segment" = "CodeName"))
avg.utt.len.tseltal <- all.data %>%
filter(speaker != "CHI") %>%
dplyr::select(dur) %>%
summarise(mean.utt.len = mean(dur),
median.utt.len = median(dur))
sample.demog <- ptcp.info %>%
mutate(mat_age_rd = as.integer(round(mother_age, 0)),
fat_age_rd = as.integer(round(father_age, 0)),
hszrd = as.integer(round(household_size + 1, 0))) %>%
select(age_childes, child_sex, mat_age_rd, mat_ed, hszrd) %>%
rename("Age" = age_childes, "Sex" = child_sex,
"Mother's age" = mat_age_rd, "Level of maternal education" = mat_ed,
"People in house" = hszrd)
ptcp.info <- mutate(ptcp.info,
rec.start.hr = lubridate::hour(start_of_recording) +
lubridate::minute(start_of_recording)/60 +
lubridate::second(start_of_recording)/3600,
rec.stop.hr = rec.start.hr + length_of_recording/3600) %>%
arrange(age_mo_round) %>%
mutate(order = seq(1:10))
used.clips <- seg.info %>%
filter(Include == 1) %>%
left_join(ptcp.info, by = c("aclew_id" = "aclew_child_id")) %>%
mutate(clip.dur = ifelse(grepl(('random'), CodeName), 5,
ifelse(grepl('extension', CodeName), 6, 1)),
sample.type = ifelse(grepl(('random'), CodeName), "Random",
ifelse(grepl('tt', CodeName),
"Turn taking", "Vocal activity")))
used.clips$sample.type = factor(used.clips$sample.type,
levels = c("Random", "Turn taking", "Vocal activity"))
clip.distribution <- ggplot() +
geom_segment(data = ptcp.info,
aes(x = rec.start.hr, y = order, xend = rec.stop.hr, yend = order)) +
geom_segment(data = used.clips,
aes(x = start.hr, y = order, xend = start.hr + clip.dur/60, yend = order,
color = sample.type), size = 3) +
theme_apa() +
theme(legend.position = "bottom") +
scale_x_continuous(breaks = 7:21) +
scale_y_continuous(breaks = 1:10, labels = ptcp.info$age_mo_round) +
ylab("Child age (mo)") + xlab("Time of day (hr)") + labs(color = "Sample type") +
scale_color_manual(values = c(viridis(3)[1], viridis(3)[2], "blue"))
clip.distribution
# RANDOM
# Get min/hr speech measures
n.unique.rand.segs <- length(unique(all.data$segment[grepl("random", all.data$segment)]))
n.unique.recs <- length(unique(all.data$aclew_child_id))
all.rand.segments <- tibble(
aclew_child_id = rep(unique(all.data$aclew_child_id),
n.unique.rand.segs),
segment = rep(unique(all.data$segment[grepl("random", all.data$segment)]),
n.unique.recs))
# XDS
xds.per.seg.rand <- all.data %>%
filter(sample == "random" & speaker != "CHI" &
grepl("xds@", tier)) %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(xds_min = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 5, xds_min = 0)) %>%
mutate(xds_mph = (xds_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# ODS
ods.per.seg.rand <- all.data %>%
filter(sample == "random" & speaker != "CHI" &
grepl("xds@", tier) & val != "T") %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(ods_min = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 5, ods_min = 0)) %>%
mutate(ods_mph = (ods_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# TDS
tds.per.seg.rand <- all.data %>%
filter(sample == "random" & speaker != "CHI" &
grepl("xds@", tier) & val == "T") %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(tds_min = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 5, tds_min = 0)) %>%
mutate(tds_mph = (tds_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# All CDS
cds.per.seg.rand <- all.data %>%
filter(sample == "random" & speaker != "CHI" &
grepl("xds@", tier) & (val == "T" | val == "C")) %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(cds_min = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 5, cds_min = 0)) %>%
mutate(cds_mph = (cds_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# Number of speakers per clip
spkrs.per.seg.rand <- all.data %>%
filter(sample == "random" & speaker != "CHI" &
!(grepl("@", tier))) %>%
group_by(aclew_child_id, segment) %>%
summarise(n_spkrs_clip = length(unique(speaker)))
# All together
quantity.rand <- xds.per.seg.rand %>%
full_join(ods.per.seg.rand, by = c("aclew_child_id", "segment", "segment_dur")) %>%
full_join(tds.per.seg.rand, by = c("aclew_child_id", "segment", "segment_dur")) %>%
full_join(cds.per.seg.rand, by = c("aclew_child_id", "segment", "segment_dur")) %>%
full_join(spkrs.per.seg.rand, by = c("aclew_child_id", "segment")) %>%
left_join(dplyr::select(seg.info, c("aclew_id", "CodeName", "start.hr")),
by = c("aclew_child_id" = "aclew_id", "segment" = "CodeName")) %>%
full_join(ptcp.info, by = "aclew_child_id") %>%
replace_na(list(xds_min = 0, xds_mph = 0,
tds_min = 0, tds_mph = 0,
cds_min = 0, cds_mph = 0,
n_spkrs_clip = 0)) %>%
mutate(prop_tds = tds_min/xds_min)
# Don't replace NAs with 0s in this case; proportion is not meaningful w/o any speech
quantity.rand.bychild <- quantity.rand %>%
group_by(aclew_child_id) %>%
summarise(
xds_min = mean(xds_min),
xds_mph = mean(xds_mph),
ods_min = mean(ods_min),
ods_mph = mean(ods_mph),
tds_min = mean(tds_min),
tds_mph = mean(tds_mph),
cds_min = mean(cds_min),
cds_mph = mean(cds_mph),
prop_tds = mean(prop_tds, na.rm = TRUE),
m_n_spkrs = mean(n_spkrs_clip)) %>%
full_join(ptcp.info, by = "aclew_child_id")
# Get xds and tds min/hr by speaker type
all.data$SpkrAge <- "Not known"
all.data$SpkrAge[grepl("FA|MA|UA", all.data$speaker)] <- "Adult"
all.data$SpkrAge[grepl("FC|MC|UC", all.data$speaker)] <- "Child"
all.rand.segments.sa <- tibble(
aclew_child_id = rep(unique(all.data$aclew_child_id),
2*n.unique.rand.segs),
segment = rep(unique(all.data$segment[grepl("random", all.data$segment)]),
2*n.unique.recs),
SpkrAge = c(rep("Adult", (n.unique.rand.segs * n.unique.recs)),
rep("Child", (n.unique.rand.segs * n.unique.recs))))
# Add in speaker sex
all.data$SpkrSex <- "Not known"
all.data$SpkrSex[grepl("FA|FC|FU", all.data$speaker)] <- "Female"
all.data$SpkrSex[grepl("MA|MC|MU", all.data$speaker)] <- "Male"
all.rand.segments.age.sx <- tibble(
aclew_child_id = rep(unique(all.data$aclew_child_id),
4*n.unique.rand.segs),
segment = rep(unique(all.data$segment[grepl("random", all.data$segment)]),
4*n.unique.recs),
SpkrAge = c(rep("Adult", (n.unique.rand.segs * n.unique.recs)),
rep("Child", (n.unique.rand.segs * n.unique.recs)),
rep("Adult", (n.unique.rand.segs * n.unique.recs)),
rep("Child", (n.unique.rand.segs * n.unique.recs))),
SpkrSex = c(rep("Female", (n.unique.rand.segs * (2 * n.unique.recs))),
rep("Male", (n.unique.rand.segs * (2 * n.unique.recs)))))
# XDS
xds.per.seg.rand.sa <- all.data %>%
filter(sample == "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier)) %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(xds_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 5, xds_min.sa = 0)) %>%
mutate(xds_mph.sa = (xds_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# ODS
ods.per.seg.rand.sa <- all.data %>%
filter(sample == "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier) & val != "T") %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(ods_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 5, ods_min.sa = 0)) %>%
mutate(ods_mph.sa = (ods_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# TDS
tds.per.seg.rand.sa <- all.data %>%
filter(sample == "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier) & val == "T") %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(tds_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 5, tds_min.sa = 0)) %>%
mutate(tds_mph.sa = (tds_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# All CDS
cds.per.seg.rand.sa <- all.data %>%
filter(sample == "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier) & (val == "T" | val == "C")) %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(cds_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.rand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 5, cds_min.sa = 0)) %>%
mutate(cds_mph.sa = (cds_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# Number of speakers per clip
spkrs.per.seg.rand.sa <- all.data %>%
filter(sample == "random" & speaker != "CHI" & SpkrAge != "Not known" &
!(grepl("@", tier))) %>%
group_by(aclew_child_id, SpkrAge, segment) %>%
summarise(n_spkrs_clip = length(unique(speaker)))
# All together
quantity.rand.sa <- xds.per.seg.rand.sa %>%
full_join(ods.per.seg.rand.sa, by = c("aclew_child_id", "SpkrAge",
"segment", "segment_dur")) %>%
full_join(tds.per.seg.rand.sa, by = c("aclew_child_id", "SpkrAge",
"segment", "segment_dur")) %>%
full_join(cds.per.seg.rand.sa, by = c("aclew_child_id", "SpkrAge",
"segment", "segment_dur")) %>%
full_join(dplyr::select(quantity.rand, c("aclew_child_id", "segment", "tds_min")),
by = c("aclew_child_id", "segment")) %>%
full_join(spkrs.per.seg.rand.sa, by = c("aclew_child_id", "SpkrAge", "segment")) %>%
left_join(dplyr::select(seg.info, c("aclew_id", "CodeName", "start.hr")),
by = c("aclew_child_id" = "aclew_id", "segment" = "CodeName")) %>%
full_join(ptcp.info, by = "aclew_child_id") %>%
replace_na(list(xds_min.sa = 0, xds_mph.sa = 0,
ods_min.sa = 0, ods_mph.sa = 0,
tds_min.sa = 0, tds_mph.sa = 0,
cds_min.sa = 0, cds_mph.sa = 0,
n_spkrs_clip = 0)) %>%
mutate(prop_tds.sa = tds_min.sa/xds_min.sa,
prop_sa.tds = tds_min.sa/tds_min)
# Don't replace NAs with 0s in this case; proportion is not meaningful w/o any speech
# NON-RANDOM
# Get min/hr speech measures
all.nonrand.segments <- seg.info %>%
filter(!(grepl("random", CodeName))) %>%
select(aclew_id, CodeName) %>%
rename(aclew_child_id = aclew_id, segment = CodeName)
all.nonrand.segments$sample <- ifelse(grepl("va", all.nonrand.segments$segment),
"high-activity","turn-taking")
all.nonrand.segments$sample_type <- ifelse(
grepl("^va", all.nonrand.segments$segment), "high-activity",
ifelse(grepl("^tt", all.nonrand.segments$segment),"turn-taking",
"extension"))
# XDS
xds.per.seg.nonrand <- all.data %>%
filter(sample != "random" & speaker != "CHI" &
grepl("xds@", tier)) %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(xds_min = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 1, xds_min = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
xds_mph = (xds_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# ODS
ods.per.seg.nonrand <- all.data %>%
filter(sample != "random" & speaker != "CHI" &
grepl("xds@", tier) & val != "T") %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(ods_min = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 1, ods_min = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
ods_mph = (ods_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# TDS
tds.per.seg.nonrand <- all.data %>%
filter(sample != "random" & speaker != "CHI" &
grepl("xds@", tier) & val == "T") %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(tds_min = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 1, tds_min = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
tds_mph = (tds_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# All CDS
cds.per.seg.nonrand <- all.data %>%
filter(sample != "random" & speaker != "CHI" &
grepl("xds@", tier) & (val == "T" | val == "C")) %>%
group_by(aclew_child_id, segment, segment_dur) %>%
summarise(cds_min = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments, by = c("aclew_child_id", "segment")) %>%
replace_na(list(segment_dur = 1, cds_min = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
cds_mph = (cds_min/segment_dur)*60) %>%
arrange(aclew_child_id, segment)
# Number of speakers per clip
spkrs.per.seg.nonrand <- all.data %>%
filter(sample != "random" & speaker != "CHI" & SpkrAge != "Not known" &
!(grepl("@", tier))) %>%
group_by(aclew_child_id, segment) %>%
summarise(n_spkrs_clip = length(unique(speaker)))
# All together
quantity.nonrand <- xds.per.seg.nonrand %>%
full_join(ods.per.seg.nonrand, by = c("aclew_child_id", "segment",
"segment_dur", "sample", "sample_type")) %>%
full_join(tds.per.seg.nonrand, by = c("aclew_child_id", "segment",
"segment_dur", "sample", "sample_type")) %>%
full_join(cds.per.seg.nonrand, by = c("aclew_child_id", "segment",
"segment_dur", "sample", "sample_type")) %>%
full_join(ptcp.info, by = "aclew_child_id") %>%
full_join(spkrs.per.seg.nonrand, by = c("aclew_child_id", "segment")) %>%
left_join(dplyr::select(seg.info, c("aclew_id", "CodeName", "start.hr")),
by = c("aclew_child_id" = "aclew_id", "segment" = "CodeName")) %>%
replace_na(list(xds_min = 0, xds_mph = 0,
ods_min = 0, ods_mph = 0,
tds_min = 0, tds_mph = 0,
cds_min = 0, cds_mph = 0,
n_spkrs_clip = 0)) %>%
mutate(prop_tds = tds_min/xds_min)
# Don't replace NAs with 0s in this case; proportion is not meaningful w/o any speech
quantity.nonrand.bychild <- quantity.nonrand %>%
group_by(aclew_child_id, sample) %>%
summarise(
xds_min = mean(xds_min),
xds_mph = mean(xds_mph),
ods_min = mean(ods_min),
ods_mph = mean(ods_mph),
tds_min = mean(tds_min),
tds_mph = mean(tds_mph),
cds_min = mean(cds_min),
cds_mph = mean(cds_mph),
prop_tds = mean(prop_tds, na.rm = TRUE),
m_n_spkrs = mean(n_spkrs_clip)) %>%
full_join(ptcp.info, by = "aclew_child_id")
# Get xds and tds min/hr by speaker type
all.nonrand.segments.sa <- tibble(
aclew_child_id = rep(all.nonrand.segments$aclew_child_id, 2),
segment = rep(all.nonrand.segments$segment, 2),
sample = rep(all.nonrand.segments$sample, 2),
sample_type = rep(all.nonrand.segments$sample_type, 2),
SpkrAge = c(rep("Adult", nrow(all.nonrand.segments)),
rep("Child", nrow(all.nonrand.segments))))
# XDS
xds.per.seg.nonrand.sa <- all.data %>%
filter(sample != "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier)) %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(xds_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 1, xds_min.sa = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
xds_mph.sa = (xds_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# ODS
ods.per.seg.nonrand.sa <- all.data %>%
filter(sample != "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier) & val != "T") %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(ods_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 1, ods_min.sa = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
ods_mph.sa = (ods_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# TDS
tds.per.seg.nonrand.sa <- all.data %>%
filter(sample != "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier) & val == "T") %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(tds_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 1, tds_min.sa = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
tds_mph.sa = (tds_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# All CDS
cds.per.seg.nonrand.sa <- all.data %>%
filter(sample != "random" & speaker != "CHI" & SpkrAge != "Not known" &
grepl("xds@", tier) & (val == "T" | val == "C")) %>%
group_by(aclew_child_id, SpkrAge, segment, segment_dur) %>%
summarise(cds_min.sa = round(sum(dur)/60000,3)) %>%
full_join(all.nonrand.segments.sa, by = c("aclew_child_id", "segment", "SpkrAge")) %>%
replace_na(list(segment_dur = 1, cds_min.sa = 0)) %>%
mutate(segment_dur = ifelse(grepl("ext", segment), 5, 1),
cds_mph.sa = (cds_min.sa/segment_dur)*60) %>%
arrange(aclew_child_id, segment, SpkrAge)
# Number of speakers per clip
spkrs.per.seg.nonrand.sa <- all.data %>%
filter(sample != "random" & speaker != "CHI" & SpkrAge != "Not known" &
!(grepl("@", tier))) %>%
group_by(aclew_child_id, SpkrAge, segment) %>%
summarise(n_spkrs_clip = length(unique(speaker)))
# All together
quantity.nonrand.sa <- xds.per.seg.nonrand.sa %>%
full_join(ods.per.seg.nonrand.sa, by = c("aclew_child_id", "SpkrAge",
"segment", "segment_dur",
"sample", "sample_type")) %>%
full_join(tds.per.seg.nonrand.sa, by = c("aclew_child_id", "SpkrAge",
"segment", "segment_dur",
"sample", "sample_type")) %>%
full_join(cds.per.seg.nonrand.sa, by = c("aclew_child_id", "SpkrAge",
"segment", "segment_dur",
"sample", "sample_type")) %>%
full_join(select(quantity.nonrand, c("aclew_child_id", "segment", "tds_min")),
by = c("aclew_child_id", "segment")) %>%
full_join(ptcp.info, by = "aclew_child_id") %>%
full_join(spkrs.per.seg.nonrand.sa, by = c("aclew_child_id", "SpkrAge", "segment")) %>%
left_join(dplyr::select(seg.info, c("aclew_id", "CodeName", "start.hr")),
by = c("aclew_child_id" = "aclew_id", "segment" = "CodeName")) %>%
replace_na(list(xds_min.sa = 0, xds_mph.sa = 0,
ods_min.sa = 0, ods_mph.sa = 0,
tds_min.sa = 0, tds_mph.sa = 0,
cds_min.sa = 0, cds_mph.sa = 0,
n_spkrs_clip = 0)) %>%
mutate(prop_tds.sa = tds_min.sa/xds_min.sa,
prop_sa.tds = tds_min.sa/tds_min)
# Don't replace NAs with 0s in this case; proportion is not meaningful w/o any speech
# Subset the non-random samples (used for differnt purposes)
quantity.nonrand.tt <- filter(quantity.nonrand, sample == "turn-taking")
quantity.nonrand.tt.sa <- filter(quantity.nonrand.sa, sample == "turn-taking")
quantity.nonrand.va <- filter(quantity.nonrand, sample != "turn-taking")
quantity.nonrand.va.sa <- filter(quantity.nonrand.sa, sample != "turn-taking")
## Get variables ready for modeling
# random sample
quantity.rand$child_sex <- as.factor(quantity.rand$child_sex)
quantity.rand$mat_ed <- as.factor(quantity.rand$mat_ed)
nspkrs.m <- mean(quantity.rand$n_spkrs_clip)
nspkrs.sd <- sd(quantity.rand$n_spkrs_clip)
quantity.rand <- quantity.rand %>%
mutate(
xds_mph.nz = ifelse(xds_mph > 0, 1, 0),
ods_mph.nz = ifelse(ods_mph > 0, 1, 0),
tds_mph.nz = ifelse(tds_mph > 0, 1, 0),
cds_mph.nz = ifelse(cds_mph > 0, 1, 0),
tchiyr.std = ((age_mo_round - tchiyr.m)/tchiyr.sd),
chisx.std = recode_factor(child_sex,
"M" = "M", "F" = "F"),
mated.std = recode_factor(mat_ed,
"none" = "none", "primary" = "primary",
"secondary" = "secondary", "preparatory" = "preparatory"),
mated.bin = recode_factor(mat_ed,
"none" = "0-5", "primary" = "0-5",
"secondary" = "6+", "preparatory" = "6+"),
motyr.std = ((mother_age - motyr.m)/motyr.sd),
nsb.std = ((number_older_sibs - nsb.m)/nsb.sd),
hsz.std = ((household_size - hsz.m)/hsz.sd),
nsk.std = ((n_spkrs_clip - nspkrs.m)/nspkrs.sd),
stthr.std = (start.hr - 12)/12,
stthr.tri = ifelse(start.hr < 11, "morning",
ifelse(start.hr > 13, "afternoon", "midday")))
quantity.rand$stthr.tri <- factor(quantity.rand$stthr.tri,
levels = c("midday", "morning", "afternoon"))
quantity.rand$stthr.tri.a <- factor(quantity.rand$stthr.tri,
levels = c("afternoon", "midday", "morning"))
quantity.rand$stthr.tri.o <- factor(quantity.rand$stthr.tri,
levels = c("morning", "afternoon", "midday"))
quantity.rand.sa$child_sex <- as.factor(quantity.rand.sa$child_sex)
quantity.rand.sa$mat_ed <- as.factor(quantity.rand.sa$mat_ed)
nspkrs.sa.m <- mean(quantity.rand.sa$n_spkrs_clip)
nspkrs.sa.sd <- sd(quantity.rand.sa$n_spkrs_clip)
quantity.rand.sa <- quantity.rand.sa %>%
mutate(
xds_mph.sa.nz = ifelse(xds_mph.sa > 0, 1, 0),
ods_mph.sa.nz = ifelse(ods_mph.sa > 0, 1, 0),
tds_mph.sa.nz = ifelse(tds_mph.sa > 0, 1, 0),
cds_mph.sa.nz = ifelse(cds_mph.sa > 0, 1, 0),
tchiyr.std = ((age_mo_round - tchiyr.m)/tchiyr.sd),
chisx.std = recode_factor(child_sex,
"M" = "M", "F" = "F"),
mated.std = recode_factor(mat_ed,
"none" = "none", "primary" = "primary",
"secondary" = "secondary", "preparatory" = "preparatory"),
mated.bin = recode_factor(mat_ed,
"none" = "0-5", "primary" = "0-5",
"secondary" = "6+", "preparatory" = "6+"),
motyr.std = ((mother_age - motyr.m)/motyr.sd),
nsb.std = ((number_older_sibs - nsb.m)/nsb.sd),
hsz.std = ((household_size - hsz.m)/hsz.sd),
nsk.std = ((n_spkrs_clip - nspkrs.sa.m)/nspkrs.sa.sd),
stthr.std = (start.hr - 12)/12,
stthr.tri = ifelse(start.hr < 11, "morning",
ifelse(start.hr > 13, "afternoon", "midday")))
quantity.rand.sa$stthr.tri <- factor(quantity.rand.sa$stthr.tri,
levels = c("midday", "morning", "afternoon"))
quantity.rand.sa$stthr.tri.a <- factor(quantity.rand.sa$stthr.tri,
levels = c("afternoon", "midday", "morning"))
quantity.rand.sa$stthr.tri.o <- factor(quantity.rand.sa$stthr.tri,
levels = c("morning", "afternoon", "midday"))
# tt sample
quantity.nonrand.tt$child_sex <- as.factor(quantity.nonrand.tt$child_sex)
quantity.nonrand.tt$mat_ed <- as.factor(quantity.nonrand.tt$mat_ed)
nspkrs.m <- mean(quantity.nonrand.tt$n_spkrs_clip)
nspkrs.sd <- sd(quantity.nonrand.tt$n_spkrs_clip)
quantity.nonrand.tt <- quantity.nonrand.tt %>%
mutate(
xds_mph.nz = ifelse(xds_mph > 0, 1, 0),
ods_mph.nz = ifelse(ods_mph > 0, 1, 0),
tds_mph.nz = ifelse(tds_mph > 0, 1, 0),
cds_mph.nz = ifelse(cds_mph > 0, 1, 0),
tchiyr.std = ((age_mo_round - tchiyr.m)/tchiyr.sd),
chisx.std = recode_factor(child_sex,
"M" = "M", "F" = "F"),
mated.std = recode_factor(mat_ed,
"none" = "none", "primary" = "primary",
"secondary" = "secondary", "preparatory" = "preparatory"),
mated.bin = recode_factor(mat_ed,
"none" = "0-5", "primary" = "0-5",
"secondary" = "6+", "preparatory" = "6+"),
motyr.std = ((mother_age - motyr.m)/motyr.sd),
nsb.std = ((number_older_sibs - nsb.m)/nsb.sd),
hsz.std = ((household_size - hsz.m)/hsz.sd),
nsk.std = ((n_spkrs_clip - nspkrs.m)/nspkrs.sd),
stthr.std = (start.hr - 12)/12,
stthr.tri = ifelse(start.hr < 11, "morning",
ifelse(start.hr > 13, "afternoon", "midday")))
quantity.nonrand.tt$stthr.tri <- factor(quantity.nonrand.tt$stthr.tri,
levels = c("midday", "morning", "afternoon"))
quantity.nonrand.tt$stthr.tri.a <- factor(quantity.nonrand.tt$stthr.tri,
levels = c("afternoon", "midday", "morning"))
quantity.nonrand.tt$stthr.tri.o <- factor(quantity.nonrand.tt$stthr.tri,
levels = c("morning", "afternoon", "midday"))
quantity.nonrand.tt.sa$child_sex <- as.factor(quantity.nonrand.tt.sa$child_sex)
quantity.nonrand.tt.sa$mat_ed <- as.factor(quantity.nonrand.tt.sa$mat_ed)
nspkrs.sa.m <- mean(quantity.nonrand.tt.sa$n_spkrs_clip)
nspkrs.sa.sd <- sd(quantity.nonrand.tt.sa$n_spkrs_clip)
quantity.nonrand.tt.sa <- quantity.nonrand.tt.sa %>%
mutate(
xds_mph.sa.nz = ifelse(xds_mph.sa > 0, 1, 0),
ods_mph.sa.nz = ifelse(ods_mph.sa > 0, 1, 0),
tds_mph.sa.nz = ifelse(tds_mph.sa > 0, 1, 0),
cds_mph.sa.nz = ifelse(cds_mph.sa > 0, 1, 0),
tchiyr.std = ((age_mo_round - tchiyr.m)/tchiyr.sd),
chisx.std = recode_factor(child_sex,
"M" = "M", "F" = "F"),
mated.std = recode_factor(mat_ed,
"none" = "none", "primary" = "primary",
"secondary" = "secondary", "preparatory" = "preparatory"),
mated.bin = recode_factor(mat_ed,
"none" = "0-5", "primary" = "0-5",
"secondary" = "6+", "preparatory" = "6+"),
motyr.std = ((mother_age - motyr.m)/motyr.sd),
nsb.std = ((number_older_sibs - nsb.m)/nsb.sd),
hsz.std = ((household_size - hsz.m)/hsz.sd),
nsk.std = ((n_spkrs_clip - nspkrs.sa.m)/nspkrs.sa.sd),
stthr.std = (start.hr - 12)/12,
stthr.tri = ifelse(start.hr < 11, "morning",
ifelse(start.hr > 13, "afternoon", "midday")))
quantity.nonrand.tt.sa$stthr.tri <- factor(quantity.nonrand.tt.sa$stthr.tri,
levels = c("midday", "morning", "afternoon"))
quantity.nonrand.tt.sa$stthr.tri.a <- factor(quantity.nonrand.tt.sa$stthr.tri,
levels = c("afternoon", "midday", "morning"))
quantity.nonrand.tt.sa$stthr.tri.o <- factor(quantity.nonrand.tt.sa$stthr.tri,
levels = c("morning", "afternoon", "midday"))
quantity.nonrand.tt.minimum <- dplyr::select(quantity.nonrand.tt,
age_mo_round, xds_mph, ods_mph, tds_mph,
prop_tds, n_spkrs_clip) %>%
mutate(Sample = "Turn taking")
quantity.rand.minimum <- dplyr::select(quantity.rand,
age_mo_round, xds_mph, ods_mph, tds_mph,
prop_tds, n_spkrs_clip) %>%
mutate(Sample = "Random")
quantity.rand_and_tt <- bind_rows(quantity.nonrand.tt.minimum, quantity.rand.minimum)
quantity.nonrand.sa.tt.minimum <- dplyr::select(quantity.nonrand.tt.sa,
age_mo_round, prop_sa.tds, SpkrAge) %>%
mutate(Sample = "Turn taking")
quantity.rand.sa.minimum <- dplyr::select(quantity.rand.sa,
age_mo_round, prop_sa.tds, SpkrAge) %>%
mutate(Sample = "Random")
quantity.sa.rand_and_tt <- bind_rows(
quantity.nonrand.sa.tt.minimum, quantity.rand.sa.minimum)
write_csv(quantity.rand_and_tt,
paste0(shiny.input.path,
"quantity-scores_rand-and-tt.csv"))
# ODS min/hr
odsmph.segments.rand_and_tt <- ggplot(quantity.rand_and_tt,
aes(x = age_mo_round, y = ods_mph, lty = Sample)) +
geom_boxplot(aes(group = interaction(age_mo_round, Sample),
color = Sample), fill = "white", outlier.shape = NA,
lty = "solid", alpha = 0.4) +
geom_smooth(aes(fill = Sample, color = Sample), method = "lm") +
ylab("ODS (min/hr)") + xlab("Child age (mo)") +
scale_y_continuous(limits=c(-10,80),
breaks=seq(0,80,20)) +
scale_x_continuous(limits=c(0,38),
breaks=seq(0,38,6)) +
coord_cartesian(ylim=c(0,80),xlim=c(0,38)) +
scale_color_manual(values = viridis(3)) +
scale_fill_manual(values = viridis(3)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4))
# TDS min/hr
tdsmph.segments.rand_and_tt <- ggplot(quantity.rand_and_tt,
aes(x = age_mo_round, y = tds_mph, lty = Sample)) +
geom_boxplot(aes(group = interaction(age_mo_round, Sample),
color = Sample), fill = "white", outlier.shape = NA,
lty = "solid", alpha = 0.4) +
geom_smooth(aes(fill = Sample, color = Sample), method = "lm") +
ylab("TCDS (min/hr)") + xlab("Child age (mo)") +
scale_y_continuous(limits=c(0,80),
breaks=seq(0,80,20)) +
scale_x_continuous(limits=c(0,38),
breaks=seq(0,38,6)) +
coord_cartesian(ylim=c(0,80),xlim=c(0,38)) +
scale_color_manual(values = viridis(3)) +
scale_fill_manual(values = viridis(3)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4))
# TDS min/hr - zoomed in
tdsmph.segments.rand_and_tt.zoomedin <- ggplot(quantity.rand_and_tt,
aes(x = age_mo_round, y = tds_mph, lty = Sample)) +
geom_boxplot(aes(group = interaction(age_mo_round, Sample),
color = Sample), fill = "white", outlier.shape = NA,
lty = "solid", alpha = 0.4) +
geom_smooth(aes(fill = Sample, color = Sample), method = "lm") +
ylab("TCDS (min/hr)") + xlab("Child age (mo)") +
scale_y_continuous(limits=c(0,40),
breaks=seq(0,40,10)) +
scale_x_continuous(limits=c(0,38),
breaks=seq(0,38,6)) +
coord_cartesian(ylim=c(0,40),xlim=c(0,38)) +
scale_color_manual(values = viridis(3)) +
scale_fill_manual(values = viridis(3)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4))
# TDS prop
tdsprp.segments.rand_and_tt <- ggplot(quantity.rand_and_tt,
aes(x = age_mo_round, y = prop_tds, lty = Sample)) +
geom_boxplot(aes(group = interaction(age_mo_round, Sample),
color = Sample), fill = "white", outlier.shape = NA,
lty = "solid", alpha = 0.4) +
geom_smooth(aes(fill = Sample, color = Sample), method = "lm") +
ylab("TCDS/All spch") + xlab("Child age (mo)") +
scale_y_continuous(limits=c(-.2,1.2),
breaks=seq(0,1,0.2)) +
scale_x_continuous(limits=c(0,38),
breaks=seq(0,38,6)) +
coord_cartesian(ylim=c(0,1),xlim=c(0,38)) +
scale_color_manual(values = viridis(3)) +
scale_fill_manual(values = viridis(3)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4))
# prop TDS from children
tdsprp.segments.rand_and_tt.sa <- ggplot(subset(quantity.sa.rand_and_tt,
SpkrAge == "Child"),
aes(x = age_mo_round, y = prop_sa.tds, lty = Sample)) +
geom_boxplot(aes(group = interaction(age_mo_round, Sample),
color = Sample), fill = "white", outlier.shape = NA,
lty = "solid", alpha = 0.4) +
geom_smooth(aes(fill = Sample, color = Sample), method = "lm") +
ylab("Prop of TCDS") + xlab("Child age (mo)") +
scale_y_continuous(limits=c(-.2,1.2),
breaks=seq(0,1,0.2)) +
scale_x_continuous(limits=c(0,38),
breaks=seq(0,38,6)) +
coord_cartesian(ylim=c(0,1), xlim=c(0,38)) +
scale_color_manual(values = viridis(3)) +
scale_fill_manual(values = viridis(3)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4))
## TOD plots
quantity.nonrand.tt$Sample <- "Turn taking"
quantity.rand$Sample <- "Random"
quantity.rand_and_tt.all <- bind_rows(quantity.nonrand.tt, quantity.rand)
quantity.rand_and_tt.all$stthr.tri.centered <- factor(
quantity.rand_and_tt.all$stthr.tri,
levels = c("morning", "midday", "afternoon"))
quantity.rand_and_tt.all$SplitAge <- ifelse(
quantity.rand_and_tt.all$age_mo_round < 13, "Under 1;0", "1;0+")
quantity.rand_and_tt.all$SplitAge <- factor(quantity.rand_and_tt.all$SplitAge,
levels = c("Under 1;0", "1;0+"))
write_csv(quantity.rand_and_tt.all,
paste0(shiny.input.path,
"quantity-scores_rand-and-tt-TOD.csv"))
tod.tcds <- ggplot(data = quantity.rand_and_tt.all, aes(
y = round(tds_mph, 0), x = stthr.tri.centered,
group = interaction(SplitAge, stthr.tri.centered),
color = Sample,
fill = Sample,
alpha = SplitAge)) +
geom_jitter() +
geom_boxplot(color = "black") +
facet_grid(~ Sample) +
ylab("TCDS (min/hr)") + xlab("Time of day") +
labs(alpha = "Age") +
scale_y_continuous(limits=c(-10,40),
breaks=c(0,10,20, 30, 40)) +
scale_color_manual(guide = FALSE, values = viridis(3)) +
scale_fill_manual(guide = FALSE, values = viridis(3)) +
scale_alpha_discrete(range = c(0.2, 0.8)) +
coord_cartesian(ylim=c(0,40)) +
theme_apa() +
theme(legend.position="right",
axis.line = element_line(color="black", size = 0.4)) +
guides(alpha = guide_legend(override.aes = list(
fill = c("gray80", "gray30"))))
tod.ods <- ggplot(data = quantity.rand_and_tt.all, aes(
y = round(ods_mph, 0), x = stthr.tri.centered,
group = interaction(SplitAge, stthr.tri.centered),
color = Sample,
fill = Sample,
alpha = SplitAge)) +
geom_jitter() +
geom_boxplot(color = "black") +
facet_grid(~ Sample) +
ylab("ODS (min/hr)") + xlab("Time of day") +
labs(alpha = "Age") +
scale_y_continuous(limits=c(-10,80),
breaks=seq(0,80,20)) +
scale_color_manual(guide = FALSE, values = viridis(3)) +
scale_fill_manual(guide = FALSE, values = viridis(3)) +
scale_alpha_discrete(range = c(0.2, 0.8)) +
coord_cartesian(ylim=c(0,80)) +
theme_apa() +
theme(legend.position="right",
axis.line = element_line(color="black", size = 0.4)) +
guides(alpha = guide_legend(override.aes = list(
fill = c("gray80", "gray30"))))
# individual TOD plots
tod.tcds.rand <- ggplot(
data = subset(quantity.rand_and_tt.all, Sample == "Random"), aes(
y = round(tds_mph, 0), x = stthr.tri.centered,
group = interaction(SplitAge, stthr.tri.centered),
color = Sample,
fill = Sample,
alpha = SplitAge)) +
geom_jitter() +
geom_boxplot(color = "black") +
ylab("TCDS (min/hr)") + xlab("") +
ggtitle("Random") +
labs(alpha = "Age") +
scale_y_continuous(limits=c(-10,40),
breaks=c(0,10,20, 30, 40)) +
scale_color_manual(guide = FALSE, values = viridis(3)) +
scale_fill_manual(guide = FALSE, values = viridis(3)) +
scale_alpha_discrete(range = c(0.2, 0.8)) +
coord_cartesian(ylim=c(0,40)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4),
plot.title = element_text(hjust = 0.5))
tod.ods.rand <- ggplot(
data = subset(quantity.rand_and_tt.all, Sample == "Random"), aes(
y = round(ods_mph, 0), x = stthr.tri.centered,
group = interaction(SplitAge, stthr.tri.centered),
color = Sample,
fill = Sample,
alpha = SplitAge)) +
geom_jitter() +
geom_boxplot(color = "black") +
ylab("ODS (min/hr)") + xlab("") +
ggtitle("Random") +
labs(alpha = "Age") +
scale_y_continuous(limits=c(-10,80),
breaks=seq(0,80,20)) +
scale_color_manual(guide = FALSE, values = viridis(3)) +
scale_fill_manual(guide = FALSE, values = viridis(3)) +
scale_alpha_discrete(range = c(0.2, 0.8)) +
coord_cartesian(ylim=c(0,80)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4),
plot.title = element_text(hjust = 0.5))
tod.tcds.tt <- ggplot(
data = subset(quantity.rand_and_tt.all, Sample == "Turn taking"), aes(
y = round(tds_mph, 0), x = stthr.tri.centered,
group = interaction(SplitAge, stthr.tri.centered),
color = Sample,
fill = Sample,
alpha = SplitAge)) +
geom_jitter() +
geom_boxplot(color = "black") +
ylab("TCDS (min/hr)") + xlab("Time of day") +
ggtitle("Turn taking") +
labs(alpha = "Age") +
scale_y_continuous(limits=c(-10,40),
breaks=c(0,10,20, 30, 40)) +
scale_color_manual(guide = FALSE,
values = c("#21908CFF", "#21908CFF", "#FDE725FF")) +
scale_fill_manual(guide = FALSE,
values = c("#21908CFF", "#21908CFF", "#FDE725FF")) +
scale_alpha_discrete(range = c(0.2, 0.8)) +
coord_cartesian(ylim=c(0,40)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4),
plot.title = element_text(hjust = 0.5))
tod.ods.tt <- ggplot(
data = subset(quantity.rand_and_tt.all, Sample == "Turn taking"), aes(
y = round(ods_mph, 0), x = stthr.tri.centered,
group = interaction(SplitAge, stthr.tri.centered),
color = Sample,
fill = Sample,
alpha = SplitAge)) +
geom_jitter() +
geom_boxplot(color = "black") +
ylab("ODS (min/hr)") + xlab("Time of day") +
ggtitle("Turn taking") +
labs(alpha = "Age") +
scale_y_continuous(limits=c(-10,80),
breaks=seq(0,80,20)) +
scale_color_manual(guide = FALSE,
values = c("#21908CFF", "#21908CFF", "#FDE725FF")) +
scale_fill_manual(guide = FALSE,
values = c("#21908CFF", "#21908CFF", "#FDE725FF")) +
scale_alpha_discrete(range = c(0.2, 0.8)) +
coord_cartesian(ylim=c(0,80)) +
theme_apa() +
theme(legend.position="none",
axis.line = element_line(color="black", size = 0.4),
plot.title = element_text(hjust = 0.5))
grid.arrange(tdsmph.segments.rand_and_tt.zoomedin,
odsmph.segments.rand_and_tt, nrow=1, ncol=2)
# Prep comparison data for matching figures
comparison.data <- read_csv(comparison.file)
tds.mph.tofill <- which(is.na(comparison.data$tds_mph) &
!(is.na(comparison.data$tds_uph)))
comparison.data$tds_mph[tds.mph.tofill] <- ((comparison.data$tds_uph[tds.mph.tofill] *
avg.utt.len.tseltal$median.utt.len)/60000)
ods.mph.tofill <- which(is.na(comparison.data$ods_mph) &
!(is.na(comparison.data$ods_uph)))
comparison.data$ods_mph[ods.mph.tofill] <- ((comparison.data$ods_uph[ods.mph.tofill] *
avg.utt.len.tseltal$median.utt.len)/60000)
xds.mph.tofill <- which(is.na(comparison.data$xds_mph) &
!(is.na(comparison.data$ods_mph)) &
!(is.na(comparison.data$tds_mph)))
comparison.data$xds_mph[xds.mph.tofill] <- comparison.data$ods_mph[xds.mph.tofill] +
comparison.data$tds_mph[xds.mph.tofill]
# Add in current data
by.chi.rand.avgs <- quantity.rand %>%
ungroup() %>%
select(age_mo_round, ods_mph, cds_mph, tds_mph, xds_mph, prop_tds) %>%
group_by(age_mo_round) %>%
summarise(ods_mph = mean(ods_mph),
cds_mph = mean(cds_mph),
tds_mph = mean(tds_mph),
xds_mph = mean(xds_mph),
prp_tds = mean(prop_tds, na.rm = TRUE)) %>%
rename(AgeMonths = age_mo_round) %>%
mutate(ods_uph = NA,
tds_uph = NA,