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new_paper_production.R
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library(cowplot)
source("analysis.R")
source("easy_util.R")
#source("ui.R")
flatten_corr_matrix <- function(hmisc_cor) {
cor_mat <- hmisc_cor$r
p_mat <- hmisc_cor$P
n_mat <- hmisc_cor$n
ut <- upper.tri(cor_mat)
data.frame(
x = rownames(cor_mat)[row(cor_mat)[ut]],
y = rownames(cor_mat)[col(cor_mat)[ut]],
r =(cor_mat)[ut],
p = p_mat[ut],
n_obs = n_mat[ut]
)
}
get_min_degree <- function(data, criterion = "AIC", threshold = -5){
Ns <- unique(data$N)
max_pos_range <- unique(data$max_pos)
criterion <- sym(criterion)
map_dfr(Ns, function(x)
map_dfr(max_pos_range, function(y){
data %>%
filter(N == x, max_pos == y) %>%
arrange(degree) %>%
mutate(d_IC = c(NA, diff(!!criterion))) %>%
#filter(d_IC == min(d_IC, na.rm = T)) %>%
filter(d_IC < threshold | d_IC == min(d_IC, na.rm = T))
}))
}
compare_regressions <- function(data, min_N = 3,
max_pos_range = seq(10, 50, 10), standardize = T,
aggregate = TRUE,
easiness = "combined_easiness",
degrees = 1:5){
map_dfr(max_pos_range, function(max_pos){
map_dfr(degrees, function(degree){
tmp <- get_regressions(data,
min_N = min_N,
max_pos = max_pos,
standardize = standardize,
aggregate = aggregate,
easiness = easiness,
degree = degree)
tmp$max_pos <- max_pos
tmp$degree <- degree
tmp
})
})
}
make_poly_degree_table <- function(max_pos = 30,
p_thresh = .01,
d_AIC = -10,
easiness_measures = c("combined_easiness", "z_surprise"),
MLA_types = c("lick", "line")){
map_dfr(easiness_measures, function(em){
map_dfr(MLA_types, function(mt){
compare_regressions(wjd_inphrase %>% filter(MLA_main_type == mt),
max_pos_range = max_pos,
easiness = em) %>%
get_min_degree("AIC", d_AIC) %>%
group_by(N, max_pos) %>%
filter(adj.r.squared == max(adj.r.squared),
max_pos == max_pos,
p.value < p_thresh) %>%
ungroup() %>%
mutate(type = mt, easiness = em)
})
})%>%
select(easiness, type, N, degree, p.value, adj.r.squared, AIC, -max_pos) %>%
mutate_if(is.numeric, round, 3)
}
make_single_phrase_correlations <-function(data = wjd_all, simul_data = wjd_simul,
max_pos = 15,
size = 1000,
easiness_measure = "combined_easiness",
recalc = T){
if(length(grep("p_cor", x = ls(envir = globalenv()))) == 0){
recalc = T
}
if(recalc){
data <- data %>% filter(phrase_len >= max_pos + 9)
phrase_ids <- unique(data$g_phrase_id) %>% sample(size)
p_cor_all <- map_dfr(3:10, function(x){
get_all_correlations(data = data,
phrase_N = x,
easiness_measure = easiness_measure,
method = "spearman",
phrase_ids = phrase_ids)
})
p_cor_all_simul <- map_dfr(3:10, function(x){
get_all_correlations(data = simul_data,
phrase_N = x,
easiness_measure = easiness_measure,
method = "spearman",
phrase_ids = phrase_ids)
})
p_cor_all <- p_cor_all %>% mutate(type = "wjd")
p_cor_all_simul <- p_cor_all_simul %>% mutate(type = "sim")
p_cor <- bind_rows(p_cor_all, p_cor_all_simul) %>% mutate(N = factor(N))
assign("p_cor_all", p_cor_all, globalenv())
assign("p_cor_all_simul", p_cor_all_simul, globalenv())
assign("p_cor", p_cor, globalenv())
}
#wjd_ids <- unique(p_cor_all$phrase_id) %>% sample(size)
#simul_ids <- unique(p_cor_all_simul$phrase_id) %>% sample(size)
#return(p_cor)
fit <- lm(estimate ~ N * type, data = p_cor)
#print(fit %>% broom::tidy())
#print(fit %>% broom::glance())
require(yarrr)
q <- yarrr::pirateplot(formula = `Spearman's rho` ~ type + N,
data = p_cor %>% rename(`Spearman's rho` = estimate) %>% mutate(type = gsub("Simul", "sim", type)),
theme = 1, inf.disp = "bean")
return(q)
q <- p_cor %>%
filter(easiness == easiness_measure) %>%
ggplot(aes(x = N, y = estimate, fill = type))
q <- q + geom_boxplot(alpha = .5)
q <- q + geom_point(aes(colour = type), alpha = .1, position = position_dodge())
q <- q + get_default_theme(default_text_size = 12, keep_legend = T)
q <- q + labs(y = "Pearsons' correlation coefficient")
q
}
plot_linear_betas <- function(data = wjd_inphrase_comb,
MLA_main_types = c("lick", "line"),
easiness = "combined_easiness",
max_pos_range = 10:30, min_N = 3,
p_thresh = .001,
alpha = 1){
#browser()
lb_wjd <- get_linear_betas(data = data %>% filter(type == "wjd"),
min_N = min_N,
easiness = easiness,
MLA_main_type = MLA_main_types,
max_pos_range = max_pos_range) %>% mutate(type = "wjd")
lb_simul <- get_linear_betas(data = data %>% filter(type == "simul"),
min_N = min_N,
easiness = easiness,
MLA_main_type = MLA_main_types,
max_pos_range = max_pos_range) %>% mutate(type = "simul")
lb <- bind_rows(lb_wjd , lb_simul) %>% mutate(comb_type = sprintf("%s-%s", type, MLA_main_type))
assign("lb", lb, globalenv())
q <- lb %>% filter(p.value < p_thresh) %>%
ggplot(aes(x = max_pos, y = estimate))
q <- q + geom_ribbon(aes(ymin = estimate-std.error, ymax = estimate + std.error, fill = type, group = comb_type),
alpha = alpha)
q <- q + geom_line(aes(linetype = MLA_main_type, group = comb_type), colour = "black")
q <- q + facet_wrap(~N)
q <- q + get_default_theme(keep_legend = T)
q <- q + theme(legend.key.size = unit(0.5, "cm"))
q <- q + theme(legend.key.width = unit(1.0, "cm"))
q <- q + theme(legend.position = c(.85, .1), legend.background = element_rect(colour = "black"))
q <- q + geom_hline(yintercept = 0)
q <- q + labs(x = "End Position", y = get_easiness_label(easiness))
q
}
get_global_correlations <- function(data, N_range = 3:10, easiness_measures){
ret <- map_dfr(N_range, function(k){
data %>%
filter(N == k) %>%
select(easiness_measures) %>%
as.matrix() %>%
Hmisc::rcorr() %>%
flatten_corr_matrix() %>%
mutate(N = k)
})
ret %>% arrange(x, y, N)
}
get_effect_sizes <- function(data, max_pos = 30, easiness_measure = "combined_easiness"){
easiness_measure <- sym(easiness_measure)
se <- function(x) sd(x)/sqrt(length(x))
tmp <- data %>%
filter(phrase_pos == 1, N >= 3) %>%
group_by(N) %>%
summarise(pos1 = mean(!!easiness_measure), .groups = "drop") %>%
ungroup()
tmp <- data %>%
filter(phrase_pos == max_pos, N >= 3) %>%
group_by(N) %>%
summarise(pos_last = mean(!!easiness_measure), .groups = "drop") %>%
ungroup() %>%
left_join(tmp)
eff_sizes <-
data %>%
filter(phrase_pos <= max_pos, N >= 3) %>%
group_by(N, phrase_pos) %>%
summarise(m = mean(!!easiness_measure), .groups = "drop") %>%
ungroup() %>%
group_by(N) %>%
summarise(d = diff(range(m)), mean = mean(m), .groups = "drop") %>%
ungroup() %>%
left_join(tmp, by = "N")
eff_sizes
}
interval_between_phrase_comparisons <- function(fig_dir){
### intervals between phrases
if(length(grep("wjd_all", x = ls(envir = globalenv()))) == 0){
return()
}
interval_in <- wjd_all %>%
filter(rev_phrase_pos != 1, N == 1) %>%
count(value) %>%
mutate(freq = n/sum(n), type = "in phrases")
interval_end <- wjd_all %>%
filter(rev_phrase_pos == 1, N == 1) %>%
count(value) %>%
mutate(freq = n/sum(n), type = "between phrases")
tmp <-
bind_rows(interval_in, interval_end) %>%
mutate(value = gsub("\\[", "", value)) %>%
mutate(value = as.numeric(gsub("\\]", "", value)))
q_interval_cmp <- tmp %>% ggplot(aes(x = value, y = freq, fill = type)) +
geom_col(colour = "black") +
facet_wrap(~type, ncol = 1) +
get_default_theme() +
scale_fill_manual(values = c( "#F2DFCE", "cadetblue3")) +
scale_y_continuous(labels=scales::percent) +
labs(x = "Interval", y = "Rel. Frequency")
save_plot(fig_dir, "interval_phrase_cmp.png", dpi = 600)
mean_interval <- tmp %>%
group_by(type) %>%
summarise(mean_abs_int = sum(abs(value*freq)), .groups = "drop")
q_diff_diff <-
wjd_all %>%
filter(N >= 3) %>%
group_by(N, in_phrase) %>%
summarise(extrinsic = mean(z_surprise),
intrinsic = mean(combined_easiness), .groups = "drop") %>%
ungroup() %>%
mutate(in_phrase = factor(in_phrase, labels = c("between phrases", "in phrases"))) %>%
pivot_longer(cols = c(intrinsic, extrinsic)) %>%
ggplot(aes(x = factor(N), y = value, colour = in_phrase)) +
geom_point() +
geom_line(aes(group = in_phrase)) +
get_default_theme(keep_legend = T) +
labs(x = "N", y = "Mean Difficulty") +
facet_wrap(~name)
save_plot(fig_dir, "inphrase_cmp_difficulties.png", dpi = 600)
}
plot_phrase_position_count <- function(max_pos = NULL){
tmp <- wjd_inphrase %>%
filter(phrase_len <= 200, N == 1, !is.na(MLA_main_type)) %>%
count(MLA_main_type, phrase_pos) %>%
bind_rows(
wjd_inphrase %>%
filter(phrase_len <= 200, N == 1) %>%
count(phrase_pos) %>%
mutate(MLA_main_type = "all")
)
if(!is.null(max_pos)){
q <- tmp %>%
filter(phrase_pos <= max_pos) %>%
ggplot(aes(x = phrase_pos, y = n))
}
else{
q <- tmp %>%
ggplot(aes(x = phrase_pos, y = n))
}
q <- q + geom_line(size = 1)
q <- q + get_default_theme()
q <- q + labs(x = "Phrase position", y = "Count")
q <- q + scale_y_log10(limits = c(1, 11400))
q <- q + geom_smooth(method = "lm", se = F, color = "indianred")
q <- q + facet_wrap(~MLA_main_type)
q
}
MLA_pirate_plots <- function(fig_dir){
messagef("### diffculty pirate plots over MLA")
## diffculty pirate plots over MLA
q_ce <- yarrr::pirateplot(formula = combined_easiness ~ MLU + N,
data = wjd_inphrase %>% filter(MLA_main_type %in% c("lick", "line"),
N >= 3,
abs(combined_easiness) < 3) %>%
rename(MLU = MLA_main_type),
theme = 1,
inf.disp = "bean",
point.o = .005,
ylab = "Intrinsic Difficulty")
q_ce <- recordPlot()
save_png_plot(fig_dir, "pirate_intrinsic_lick_line.png", width = 8.89, height = 6.6,
units = "in", res = 600)
q_ce
dev.off()
q_zs <- yarrr::pirateplot(formula = z_surprise ~ MLU + N,
data = wjd_inphrase %>% filter(MLA_main_type %in% c("lick", "line"),
N >= 3,
abs(z_surprise) < 3) %>%
rename(MLU = MLA_main_type),
theme = 1,
inf.disp = "bean",
point.o = .005,
ylab = "Extrinsic Difficulty")
q_zs <- recordPlot()
save_png_plot(fig_dir, "pirate_extrinsic_lick_line.png", width = 8.89, height = 6.6,
units = "in", res = 600)
q_zs
dev.off()
}
get_plot_file_name <- function(fig_dir, file_name){
file.path(fig_dir, file_name)
}
save_plot <- function(fig_dir, file_name, dpi = 600){
ggsave(file.path(fig_dir, file_name), dpi = 600)
}
save_png_plot <- function(fig_dir, file_name, ...){
png(file.path(fig_dir, file_name), ...)
}
new_paper_version <- function(fig_dir = "docs/paper/figs/new"){
##Load wjd_inphrase, wjd_inphrase_simul, wjd_inphrase_comb if no present.
if(length(grep("wjd_inphrase", x = ls(envir = globalenv()))) == 0){
browser()
messagef("Loading data...")
load("data/inphrase_data.rda",envir = globalenv())
}
###### SI #######
messagef("### phrase position counts")
### phrase position counts
q <- plot_phrase_position_count()
save_plot(fig_dir, "phrase_pos_by_MLA.png", dpi = 600)
messagef("### difficulty distributions")
### difficulty distributions
q_wjd_int <- plot_distribution(wjd_inphrase, max_N = 10, easiness = "combined_easiness") + ggtitle("WJD")
save_plot(fig_dir, "wjd_intrinsic_dist.png", dpi = 600)
q_wjd_ext <- plot_distribution(wjd_inphrase, max_N = 10, easiness = "z_surprise") + ggtitle("WJD")
save_plot(fig_dir, "wjd_extrinsic_dist.png", dpi = 600)
q_simul_int <- plot_distribution(wjd_inphrase_simul, max_N = 10, easiness = "combined_easiness") + ggtitle("Simulated")
save_plot(fig_dir, "simul_intrinsic_dist.png", dpi = 600)
q_simul_ext <- plot_distribution(wjd_inphrase_simul, max_N = 10, easiness = "z_surprise") + ggtitle("Simulated")
save_plot(fig_dir, "simul_extrinsic_dist.png", dpi = 600)
messagef("### rhythm and melody phrase plots ")
### rhythm and melody phrase plots
q_rhythm_melody_int <- plot_easiness_by_type_MLA(wjd_inphrase_comb,
MLA_main_types = c("rhythm", "melody"),
easiness = "combined_easiness")
#q_rhythm_melody_int <- q_rhythm_melody_int + scale_fill_manual(values = c( "#F2DFCE", "cadetblue3"))
save_plot(fig_dir, "phrase_plot_intrinsic_rhythm_melody.png", dpi = 600)
q_rhythm_melody_ext <- plot_easiness_by_type_MLA(wjd_inphrase_comb,
MLA_main_types = c("rhythm", "melody"),
easiness = "z_surprise")
#q_rhythm_melody_ext <- q_rhythm_melody_ext + scale_fill_manual(values = c( "#F2DFCE", "cadetblue3"))
save_plot(fig_dir, "phrase_plot_extrinsic_rhythm_melody.png", dpi = 600)
messagef("### no MLA phrase plots ")
### no MLA phrase plots
q_all_int <- plot_easiness_by_type(wjd_inphrase_comb, smooth = "none", plot_type = "ribbon", easiness = "combined_easiness")
save_plot(fig_dir, "phrase_plot_intrinsic_no_MLA.png", dpi = 600)
q_all_ext <- plot_easiness_by_type(wjd_inphrase_comb, smooth = "none", plot_type = "ribbon", easiness = "z_surprise")
save_plot(fig_dir, "phrase_plot_extrinsic_no_MLA.png", dpi = 600)
#currently disabled
interval_between_phrase_comparisons()
messagef("### effect sizes ")
### effect sizes
line_eff_int <- get_effect_sizes(wjd_inphrase %>% filter(MLA_main_type == "line")) %>% mutate(MLA_main_type = "line", easiness = "intrinsic")
lick_eff_int <- get_effect_sizes(wjd_inphrase %>% filter(MLA_main_type == "lick")) %>% mutate(MLA_main_type = "lick", easiness = "intrinsic")
line_eff_ext <- get_effect_sizes(wjd_inphrase %>% filter(MLA_main_type == "line"), easiness_measure = "z_surprise") %>% mutate(MLA_main_type = "line", easiness = "extrinsic")
lick_eff_ext <- get_effect_sizes(wjd_inphrase %>% filter(MLA_main_type == "lick"), easiness_measure = "z_surprise") %>% mutate(MLA_main_type = "lick", easiness = "extrinsic")
MLA_eff <- bind_rows(line_eff_int, lick_eff_int, line_eff_ext, lick_eff_ext)
write.csv(MLA_eff %>%
mutate_if(is.numeric, round, 2) %>%
select(MLA_main_type, easiness, d, pos1, mean), file = "MLA_effect_size.csv", row.names = F)
#temporarily disabled
#MLA_pirate_plots(fig_dir)
messagef("### correlations ")
#### correlations
corr_diff_wjd <- get_global_correlations(wjd_inphrase,
easiness_measures = c("int_variety",
"pitch_variety",
"dir_change",
"mean_int_size",
"int_range",
"mean_run_length",
"combined_easiness", "z_surprise"))
corr_diff_simul <- get_global_correlations(wjd_inphrase_simul,
easiness_measures = c("int_variety",
"pitch_variety",
"dir_change",
"mean_int_size",
"int_range",
"mean_run_length",
"combined_easiness", "z_surprise"))
corr_diff_ext_wjd <- corr_diff_wjd %>%
filter(x == "z_surprise" | y == "z_surprise") %>%
group_by(x, y) %>%
summarise(r = mean(r), .groups = "drop")
corr_diff_ext_simul <- corr_diff_simul %>% filter(x == "z_surprise" | y == "z_surprise") %>%
group_by(x, y) %>%
summarise(r = mean(r), .groups = "drop")
messagef("### phrase plots")
### phrase plots
q_intrinsic <- plot_easiness_by_type_MLA(wjd_inphrase_comb, easiness = "combined_easiness")
save_plot(fig_dir, "phrase_plot_intrinsic_lick_line.png", dpi = 600)
q_extrinsic <- plot_easiness_by_type_MLA(wjd_inphrase_comb, easiness = "z_surprise")
save_plot(fig_dir, "phrase_plot_extrinsic_lick_line.png", dpi = 600)
messagef("### significant linear betas")
#### significant linear betas
q_beta_intrinsic <- plot_linear_betas(easiness = "combined_easiness", alpha = .5)
save_plot(fig_dir, "linear_beta_intrinsic_lick_line.png", dpi = 600)
q_beta_extrinsic <- plot_linear_betas(easiness = "z_surprise" , alpha = .5)
save_plot(fig_dir, "linear_beta_extrinsic_lick_line.png", dpi = 600)
messagef("### single phrase correlations pirate plots")
### single phrase correlations pirate plots
q_cor_all_line <- make_single_phrase_correlations(wjd_inphrase %>% filter(MLA_main_type == "line"),
wjd_inphrase_simul %>% filter(MLA_main_type == "line"),
recalc = F)
q_cor_all_line <- recordPlot()
png(file.path(fig_dir, "cor_all_intrinsic_line.png"), width = 8.89, height = 6.6,
units = "in", res = 600)
q_cor_all_line
dev.off()
q_cor_all_lick <- make_single_phrase_correlations(wjd_inphrase %>% filter(MLA_main_type == "lick"),
wjd_inphrase_simul %>% filter(MLA_main_type == "lick"),
recalc = F)
q_cor_all_lick <- recordPlot()
save_png_plot(fig_dir, "cor_all_intrinsic_lick.png", width = 8.89, height = 6.6,
units = "in", res = 600)
q_cor_all_lick
dev.off()
}