/
make_figures.R
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make_figures.R
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# Make the Figures for Model 1
library(ggplot2)
library(reshape2)
library(forestplot)
require(tidyverse)
require(glue)
#setwd("/Users/blakemcshane/Documents/Academic Research/MetaAnalysis/RRR/fischer_att_snarc/analysis")
ReplicationProjectTools::GetLabNames()
require(ReplicationProjectTools)
EyeTrackerTable = read_csv(here::here("data/processed_data/EyeTrackerTable.csv"))
EyeTrackerTable %>% group_by(Node) %>% summarise(LabsWithEye = sum(HasEye)) %>% filter(LabsWithEye > 0) %>% pull(Node) -> LabsWithEye
LabNames[Nodes %in% LabsWithEye] -> LabsWithEye
load(here::here("data/processed_rdata/results1.Rdata"))
Fischer.dat = read.csv(here::here("other_info/Original_estimates.csv"))
Fischer.dat %>% mutate(lower = RT - (qnorm(.95) * se), upper = RT + (qnorm(.95) * se)) -> Fischer.dat
# Get estimates for best model fit for Model 1
m1.idx <- which(fit.stats$AIC[,2] == min(fit.stats$AIC[,2]))
estimates$EAMMCS[[m1.idx]]
dat = read.csv(here::here("data/meta_data/model1.meta.csv"))
DependentVariables = c("d250","d500","d750","d1000")
for(this.DependentVariable in DependentVariables){
this.DependentVariable.idx = grepl(estimates$EAMMCS[[m1.idx]]$fe.estimates %>% row.names(),pattern = this.DependentVariable) %>% which()
summary.mean = estimates$EAMMCS[[m1.idx]]$fe.estimates$Estimate[this.DependentVariable.idx]
summary.se = estimates$EAMMCS[[m1.idx]]$fe.estimates$SE[this.DependentVariable.idx]
select<- dplyr::select
dat %>% filter(DependentVariable == this.DependentVariable) %>% mutate(se = sqrt(v)) %>% select(LabID,y,se,Freq) %>% mutate(lower = y - (qnorm(.95) * se), upper = y + (qnorm(.95) * se)) %>% rename(mean = y) %>% select(LabID,mean,lower,upper,Freq,se) %>% rename(n = Freq) %>%
mutate(t = mean / se, df = n - 1, p = 2 * pt(abs(t), df = df, lower.tail = FALSE),
`t-test` = glue("t({df}) = {round(t,2)}, p = {round(p,3)}")) %>% select(LabID,mean,lower,upper,n,`t-test`) -> meta.dat
GetLabNames()
LabIDNames = data.frame(LabID = Nodes, names = LabNames)
LabIDNames %>% mutate(names = ifelse(names %in% LabsWithEye, paste0(names,"*"), paste0(names))) -> LabIDNames
merge(meta.dat,LabIDNames) %>% select(names,mean,lower,upper,n,`t-test`) %>% rename(LabID = names) -> meta.dat
meta.dat %>% arrange(LabID) -> meta.dat
summary.n = meta.dat$n %>% sum()
Fischer.dat %>% filter(delay == this.DependentVariable %>% substr(start = 2, stop = 99) %>% as.numeric()) %>%
mutate(LabID = "Fischer et al. (2003)", mean = RT) %>% mutate(n = df + 1, `t-test` = glue("t({df}) = {round(t,2)}, p = {round(p.value,3)}")) %>% select(LabID,mean,lower,upper,n,`t-test`) -> Fischer.sub
rbind(Fischer.sub,meta.dat) -> meta.dat
n.labs = dim(meta.dat)[1]
as.padded.text<-function(x){
if (is.na(x) == FALSE) {
return(ifelse(x > 0,glue(" {sprintf('%.2f',x)}"),sprintf('%.2f',x)))
} else {
return(NA)
}
}
rbind(data.frame(LabID = "Study", mean = NA, lower = NA, upper = NA, n = NA),
meta.dat %>% select(LabID, mean, lower, upper, n),
data.frame(LabID = c(NA,NA,"Summary",NA),
mean = c(NA,NA,summary.mean,NA),
lower = c(NA,NA,summary.mean - (qnorm(.95) * summary.se),NA),
upper = c(NA,NA,summary.mean + (qnorm(.95) * summary.se),NA),
n = c(NA,NA,summary.n,NA))) -> meta.dat
map_chr(1:dim(meta.dat)[1],function(x) as.padded.text(x = (meta.dat %>% pull(mean))[x])) -> means
map_chr(1:dim(meta.dat)[1],function(x) as.padded.text(x = (meta.dat %>% pull(upper))[x])) -> uppers
map_chr(1:dim(meta.dat)[1],function(x) as.padded.text(x = (meta.dat %>% pull(lower))[x])) -> lowers
meta.dat$meantext = means
meta.dat$lowertext = lowers
meta.dat$uppertext = uppers
meta.dat %>% mutate(estimate = glue("{meantext} ms [{lowertext}, {uppertext}]")) -> meta.dat
meta.dat$estimate[1] = "Estimate [90% CI]"
meta.dat$n[1] = "n"
#meta.dat$estimate[dim(meta.dat)[1]-1] = ""
meta.dat$estimate[dim(meta.dat)[1]-2] = ""
meta.dat$estimate[dim(meta.dat)[1]-3] = ""
meta.dat$estimate[dim(meta.dat)[1]] = ""
min.value = floor(meta.dat$lower[meta.dat$lower %>% is.na() == FALSE] %>% min())
max.value = ceiling(meta.dat$upper[meta.dat$upper %>% is.na() == FALSE] %>% max())
nearestEvenDown<-function(x){
if(mod(x,10) != 0){
x = x - mod(x,10)
return(x)
} else {
return(x)
}
}
nearestEvenUp<-function(x){
if(mod(x,10) != 0){
x = x + mod(x,10)
return(x)
} else {
return(x)
}
}
xticks = seq(nearestEvenDown(min.value), nearestEvenUp(max.value), 10)
hrzl_lines = list(upper = gpar(lty =1),
middle = gpar(lty =2),
lower = gpar(lty =1))
names(hrzl_lines) <- c("1","3",as.character(n.labs + 3))
png(filename = glue("{here::here('manuscript_files')}/{this.DependentVariable}.png"), width =20, height = 15, units = "cm",res = 300)
#cairo_pdf(file = glue("manuscript_files/{this.DependentVariable}.pdf"), width =8, height = 6, onefile = F, pointsize = 10)
forestplot(labeltext = meta.dat %>% select(LabID,estimate,n) %>% mutate(LabID = as.vector(LabID), estimate = as.vector(estimate)),
align = c("l","r","r"),
graph.pos = 2,xticks = xticks,
hrzl_lines = hrzl_lines,
meta.dat %>% select(mean,lower,upper),
is.summary = c(TRUE,rep(FALSE,n.labs),TRUE,TRUE),
xlab = "Estimate (ms)",txt_gp = fpTxtGp(xlab = gpar(cex= 1), ticks = gpar(cex = .9)))#,
#title = glue("{substr(this.DependentVariable,2,99)} ms ISI Condition"))
dev.off()
}
#library(magick)
#d250 = image_read("d250.png") %>% image_annotate("A", size = 85, font = "Helvetica")
#d500 = image_read("d500.png") %>% image_annotate("B", size = 85, font = "Helvetica")
#d750 = image_read("d750.png") %>% image_annotate("C", size = 85, font = "Helvetica")
#d1000 = image_read("d1000.png")%>% image_annotate("D", size = 85, font = "Helvetica")
#image_append(c(d250,d500)) -> topRow
#image_append(c(d750,d1000)) -> bottomRow
#image_append(c(topRow,bottomRow),stack = T) -> model1
#image_write(model1,"manuscript_files/model1.png",format = "png")
#system("rm d250.png")
#system("rm d500.png")
#system("rm d750.png")
#system("rm d1000.png")
# NEW FIGURES
theme_blank <- function (base_size = 12, base_family = "") {
(ggplot2::theme_minimal(base_size = base_size, base_family = base_family) +
ggplot2::theme(axis.line.x = ggplot2::element_line(colour = "white"),
axis.line.y = ggplot2::element_line(colour = "white"),
axis.text = ggplot2::element_text(colour = "grey50"),
axis.title = ggplot2::element_text(colour = "grey30"),
panel.grid.minor = ggplot2::element_line(colour = "white",
linetype = 1), panel.grid.major = ggplot2::element_line(colour = "white",
linetype = 1)))
}
require(ReplicationProjectTools)
require(glue)
require(tidyverse)
meta.files = c(here::here("data/meta_data/model1.meta.csv"),
here::here("data/meta_data/model2.meta.csv"),
here::here("data/meta_data/model3.meta.csv"),
here::here("data/meta_data/model4.meta.csv"))
map(meta.files, function(x) read_csv(x) %>% group_by(ConditionDescription, DependentVariable) %>% summarise(n = sum(Freq))) %>% bind_rows() -> subj.nums
results.files = c(here::here("data/processed_rdata/results1.Rdata"),
here::here("data/processed_rdata/results2.Rdata"),
here::here("data/processed_rdata/results3.Rdata"),
here::here("data/processed_rdata/results4.Rdata"))
estimates.list = list()
for (x in 1:length(results.files)) {
load(results.files[x])
eval(parse(text = glue("estimates{x} = estimates")))
eval(parse(text = glue("fit.stats{x} = fit.stats")))
m.idx <- which(fit.stats$AIC[, 2] == min(fit.stats$AIC[, 2]))
estimates$EAMMCS[[m.idx]]
estimates.list[[x]] = estimates$EAMMCS[[m.idx]]$fe.estimates %>% as.data.frame() %>% rownames_to_column("Condition.Full")
}
estimates.list = bind_rows(estimates.list)
merge.data.frame(subj.nums %>% unite(Condition.Full,c("DependentVariable","ConditionDescription"), sep = " * "),estimates.list, by = "Condition.Full") -> estimates.list
estimates.list %>% mutate(
Model = case_when(
grepl(Condition.Full, pattern = "Not.Applicable") == TRUE ~ "Model 1",
grepl(Condition.Full, pattern = "LS") == TRUE ~ "Model 2",
grepl(Condition.Full, pattern = "RS") == TRUE ~ "Model 2",
grepl(Condition.Full, pattern = "LH") == TRUE ~ "Model 4",
grepl(Condition.Full, pattern = "RH") == TRUE ~ "Model 4",
grepl(Condition.Full, pattern = "LTR") == TRUE ~ "Model 3",
grepl(Condition.Full, pattern = "NLR") == TRUE ~ "Model 3"
)
) %>%
mutate(
Condition = case_when(
grepl(Condition.Full, pattern = "d250") == TRUE ~ "250 ms",
grepl(Condition.Full, pattern = "d500") == TRUE ~ "500 ms",
grepl(Condition.Full, pattern = "d750") == TRUE ~ "750 ms",
grepl(Condition.Full, pattern = "d1000") == TRUE ~ "1000 ms"
)
) %>%
mutate(
Moderator = case_when(
grepl(Condition.Full, pattern = "Not.Applicable") == TRUE ~ "No moderators",
grepl(Condition.Full, pattern = "LS") == TRUE ~ "Consistent left-starter",
grepl(Condition.Full, pattern = "RS") == TRUE ~ "Consistent right-starter",
grepl(Condition.Full, pattern = "LH") == TRUE ~ "Left-handed",
grepl(Condition.Full, pattern = "RH") == TRUE ~ "Right-handed",
grepl(Condition.Full, pattern = "LTR") == TRUE ~ "Left-to-right",
grepl(Condition.Full, pattern = "NLR") == TRUE ~ "Not left-to-right"
)
) -> estimates.list
sprintf.p<-function(fmt,value){
ReplicationProjectTools::as.padded.text(value, pad.length = 6)
}
read_csv(here::here("other_info/Original_estimates.csv")) -> Orginal.estimates
Orginal.estimates %>% mutate(Condition.Full = glue("d{delay} * Not.Applicable"),
ConditionDescription = "Not.Applicable",
Estimate = RT,
SE = se,
n = 10,
Model = "Fischer et al. (2003)",
Moderator = "No moderators",
DependentVariable = glue("d{delay}"),
Condition = glue("{delay} ms")) %>% select(Condition.Full, n, Estimate, SE, Model, Condition, Moderator) -> Orginal.estimates
rbind(Orginal.estimates,estimates.list) -> estimates.list
estimates.list = as.data.frame(estimates.list)
estimates.list$Condition = as.factor(estimates.list$Condition)
estimates.list$Model = as.factor(estimates.list$Model)
estimates.list$Condition <- factor(estimates.list$Condition,
levels = c("250 ms","500 ms","750 ms","1000 ms"))
estimates.list$Model <- factor(estimates.list$Model,
levels = c("Fischer et al. (2003)","Model 1","Model 2","Model 3","Model 4"))
estimates.list$mid = pmap_chr(as.list(estimates.list)[c("Estimate")], function(Estimate,SE) ifelse(Estimate - (qnorm(.95) * 0) >0, paste0(" " ,sprintf('%.2f',Estimate - (qnorm(.95) * 0))), sprintf('%.2f',Estimate - (qnorm(.95) * 0))))
estimates.list$ll = pmap_chr(as.list(estimates.list)[c("Estimate","SE")], function(Estimate,SE) ifelse(Estimate - (qnorm(.95) * SE) >0, paste0(" " ,sprintf('%.2f',Estimate - (qnorm(.95) * SE))), sprintf('%.2f',Estimate - (qnorm(.95) * SE))))
estimates.list$ul = pmap_chr(as.list(estimates.list)[c("Estimate","SE")], function(Estimate,SE) ifelse(Estimate + (qnorm(.95) * SE) >0, paste0(" " ,sprintf('%.2f',Estimate + (qnorm(.95) * SE))), sprintf('%.2f',Estimate + (qnorm(.95) * SE))))
estimates.list$mid[1] <- paste0(" ",estimates.list$mid[1])
estimates.list$ll[2] <- paste0(" ",estimates.list$ll[2])
estimates.list$ll[3] <- paste0(" ",estimates.list$ll[3])
estimates.list$ll[4] <- paste0(" ",estimates.list$ll[4])
estimates.list$ul[1] <- paste0(" ",estimates.list$ul[1])
estimates.list$text = as.list(estimates.list)[c("mid","ll","ul")] %>% glue_data("{mid} ms [{ll},{ul}]")
##### DRAW THE PLOTS For the remaining models
##### VERSION 4
glue("{estimates.list$mid[1] %>% stringr::str_trim()} ms [{estimates.list$ll[1] %>% stringr::str_trim()}, {estimates.list$ul[1] %>% stringr::str_trim()}]") -> estimates.list$text[1]
glue("{estimates.list$mid[2] %>% stringr::str_trim()} ms [{estimates.list$ll[2] %>% stringr::str_trim()}, {estimates.list$ul[2] %>% stringr::str_trim()}]") -> estimates.list$text[2]
glue("{estimates.list$mid[3] %>% stringr::str_trim()} ms [{estimates.list$ll[3] %>% stringr::str_trim()}, {estimates.list$ul[3] %>% stringr::str_trim()}]") -> estimates.list$text[3]
glue("{estimates.list$mid[4] %>% stringr::str_trim()} ms [{estimates.list$ll[4] %>% stringr::str_trim()}, {estimates.list$ul[4] %>% stringr::str_trim()}]") -> estimates.list$text[4]
estimates.list %>% mutate(y = case_when(Model == "Fischer et al. (2003)" & Moderator == "No moderators" ~ 8,
Model == "Model 1" & Moderator == "No moderators" ~ 7,
Model == "Model 2" & Moderator == "Consistent left-starter" ~ 6,
Model == "Model 2" & Moderator == "Consistent right-starter" ~ 5,
Model == "Model 3" & Moderator == "Left-to-right" ~ 4,
Model == "Model 3" & Moderator == "Not left-to-right" ~ 3,
Model == "Model 4" & Moderator == "Left-handed" ~ 2,
Model == "Model 4" & Moderator == "Right-handed" ~ 1,
FALSE ~ NA_real_)) %>%
mutate(label2 = pmap_chr(.l = list(Ml = as.character(Model), Mr = as.character(Moderator)), .f = function(Ml,Mr) glue("{Ml} ({Mr})"))) %>% ggplot() +
geom_errorbarh(aes(xmax = Estimate + (qnorm(.95) * SE), xmin = Estimate - (qnorm(.95) * SE), y = y), height = 0) +
geom_vline(xintercept = 0, linetype = 2, size = .2, colour = "grey") +
facet_grid(Condition ~ .) + scale_y_continuous(labels = NULL, limits = c(0,9)) + geom_point(aes(x = Estimate, y = y), shape = 15) +
xlab("Estimate (ms)") + geom_text(aes(65, y = y, label = text, hjust = 1)) +
geom_text(aes(-40, y = y, label = label2), size = 3, hjust = 0) +
scale_x_continuous(limits = c(-40,65), breaks = seq(-20,40,5)) + theme_blank() + ylab(NULL) +
theme(axis.title.x.bottom = element_text(hjust = (1/abs(-40-65)) * (abs(-40-10)))) + ggtitle(label = "Estimate [90% CI]", subtitle = "Model (Moderator group)") +
theme(plot.title = element_text(hjust = .94,size = 12, vjust = -6), plot.subtitle = element_text(vjust = 0, hjust = 0.05), legend.position = "none", text = element_text(family = "Helvetica", colour = "black")) + theme(panel.border = element_rect(fill = NA, colour = 'black'),
panel.spacing.y = unit(x = 1.1, units = "picas")) ->p
p
ggsave(plot = p, filename = here::here("manuscript_files/meta_summaryv4.png"), height = 12, width = 10)
ggsave(plot = p, filename = "manuscript_files/meta_summaryv4.pdf", height = 12, width = 10, device = cairo_pdf)