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Star_graph_&_Forest_plot.R
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#### This script was created to create a forest plot for a meta-analysis (Marie Pittet, CHUV, Lausanne, Fall 2022)-------------------------------------------------
# Setting up the environment, uncomment package installation if needed ----------------------------------------------
install.packages("metaviz")
library(metaviz)
install.packages("MBESS")
library(MBESS)
install.packages("grid")
library(grid)
install.packages("forestploter")
library(forestploter)
install.packages("esc")
library(esc)
install.packages("ggplotify")
library(ggplotify)
install.packages("patchwork")
library(patchwork)
install.packages("gridExtra")
library(gridExtra)
install.packages("grid")
library(grid)
install.packages("ggpubr")
library(ggpubr)
install.packages("meta")
library(meta)
install.packages("metafor")
library(metafor)
library(psych)
library(reshape)
library(ggpattern)
library(tidyverse)
library(RColorBrewer)
# Setting up the data frames -----------------------------------------------
#Load data
df<- read.csv2("Studies_list.csv", header = T, sep=",")
df<- df[1:88,]
df$Population[83]<- "mTBI"
df$Population[88]<- "various neurol. conditions"
#Reorder in a relevant way for tool-wise display
df<-df[order(df$Population_type, df$Tool),]
#Create a data frame for the Forest plot and assigning a population_type=6 to mixed populations
df_forest<- df[!is.na(df$Cs),] # Removing lines with no control group
df_forest[is.na(df_forest$Population_type),3]<- 6
#Apply Hedges g correction for small samples
total_groupsize<- df_forest$Cs+df_forest$Patho
hedges_g<- hedges_g(df_forest$Effect_size, total_groupsize)
df_forest<- data.frame(df_forest,hedges_g)
#Naming population type levels
df_forest$Population_type<- factor(df_forest$Population_type, levels = c(1,2,3,4,5,6),labels = c("SZ", "ASD", "M&A", "NDD", "ABI", "Mixed"))
# Forest plot -------------------------------------------------------------
# separate studies with covariate-adjusted hedges g from others
df_forest1<- df_forest[(df_forest$g_ANCOVA ==""),] # no covariate analyses
df_forest2<- df_forest[!(df_forest$g_ANCOVA ==""),] # covariate analyses
# compute standard error and confidence interval for studies without covariate analyses
df_forest1$Cs<- as.numeric(df_forest1$Cs)
df_forest1$Patho<- as.numeric(df_forest1$Patho)
# Yes, I am very aware that this is the least elegant code to have ever existed but, hey, it works.
for (i in 1:nrow(df_forest1)){
ci<- hedges_g(cohen.d.ci(df_forest1$Effect_size[i], df_forest1$Cs[i]+df_forest1$Patho[i],df_forest1$Cs[i],df_forest1$Patho[i]),df_forest1$Cs[i]+df_forest1$Patho[i])
df_forest1$g_lower[i]<- ci[1]
df_forest1$g_upper[i]<- ci[3]
}
# Merge things again
df_forest<- rbind(df_forest1,df_forest2)
df_forest$g_lower<- as.numeric(df_forest$g_lower)
df_forest$g_upper<- as.numeric(df_forest$g_upper)
for (i in 1:nrow(df_forest)){ # replacing unadjusted hedges g values with covariate adjusted values
if(df_forest$g_ANCOVA[i]!= ""){
df_forest$hedges_g[i]<- df_forest$g_ANCOVA[i]
}
}
df_forest$hedges_g<- as.numeric(df_forest$hedges_g)
for (i in 1:nrow(df_forest)){ # computing confidence interval
df_forest$"SMD [95% CI]"[i]<- sprintf("%.2f [%.2f;%.2f]", df_forest$hedges_g[i],df_forest$g_lower[i],df_forest$g_upper[i])
df_forest$g_se[i]<- (df_forest$g_upper[i] - df_forest$hedges_g[i]) / 1.96
}
df_forest<-df_forest[order(df_forest$Population_type, df_forest$Tool),]
# Visualization
df_forest_emorecog<- df_forest[df_forest$Tool_type==1,]
# Emotion recognition
m.gen_emo <- metagen(TE = hedges_g,
seTE = g_se,
lower = g_lower,
upper = g_upper,
studlab = Study,
data = df_forest_emorecog,
sm = "SMD",
fixed = FALSE,
random = TRUE,
subgroup = Population_type,
method.tau = "REML",
hakn = TRUE,
title = "Emotion recognition")
forest_emo<- forest(m.gen_emo,
layout ="meta",
clip = c(-1.5, 4),
xlim = c(-1.5,4),
type.subgroup = "square",
leftcols = (c("Study","Population","Tool")),
leftlabs = (c("Study","Condition", "Test", "Condition vs controls")),
rightcols= NULL,
col.square = "black",
col.square.lines = "black",
col.diamond.random = "gray",
col.predict = "black",
subgroup.name = c("Population type"))
forest_emo
update(m.gen_emo,
subgroup = Population_type,
tau.common = FALSE)
# ToM
df_forest_ToM<- df_forest[df_forest$Tool_type==2,]
m.gen_ToM <- metagen(TE = hedges_g,
lower = g_lower,
upper = g_upper,
seTE = g_se,
studlab = Study,
data = df_forest_ToM,
subgroup = Population_type,
sm = "SMD",
fixed = FALSE,
random = TRUE,
method.tau = "REML",
hakn = TRUE,
title = "Theory of Mind")
forest_ToM<- forest(m.gen_ToM,
layout ="meta",
leftcols = (c("Study","Population","Tool")),
leftlabs = (c("Study","Condition", "Test", "Condition vs controls")),
rightcols= NULL,
clip = c(-1.5, 4),
xlim = c(-1.5,4),
type.subgroup = "square",
col.square = "black",
col.square.lines = "black",
col.diamond.random = "gray",
col.predict = "black",
subgroup.name = c("Population type"))
forest_ToM
update(m.gen_ToM,
subgroup = Population_type,
tau.common = FALSE)
# Batteries
df_forest_batteries<- df_forest[df_forest$Tool_type==3,]
m.gen_bat <- metagen(TE = hedges_g,
seTE = g_se,
lower = g_lower,
upper = g_upper,
studlab = Study,
data = df_forest_batteries,
sm = "SMD",
subgroup = Population_type,
fixed = FALSE,
random = TRUE,
method.tau = "REML",
hakn = TRUE,
title = "Batteries")
forest_bat<- forest(m.gen_bat,
layout ="meta",
leftcols = (c("Study","Population","Tool")),
leftlabs = (c("Study","Condition", "Test")),
clip = c(-1.5, 4),
xlim = c(-1.5,4),
type.subgroup = "square",
rightcols= NULL,
col.square = "black",
col.square.lines = "black",
col.diamond.random = "gray",
col.predict = "black",
subgroup.name = c("Population type"))
forest_bat
update(m.gen_bat,
subgroup = Population_type,
tau.common = FALSE)
# Funnel plots (to investigate publication bias)
col.contour = c("white","orange", "red")
# Generate funnel plot (we do not include study labels here)
res <- rma(hedges_g,g_se, data=df_forest)
funnel_plot<- funnel(res)
funnel(res, refline=0, level=c(90, 95, 99),
shade=c("white", "gray55", "gray75"),
legend=FALSE,
xlab="Effect size (Hedges' g)",
ylab = "Precision (SE)")
title(main="Publication bias: Funnel plot" )
legend(x = "topright", inset = c(0.1, 0.2),
legend = c("p > .10", "p < .10", "p < .05", "p < .01", "Studies"))
m.gen_overall <- metagen(TE = hedges_g,
lower = g_lower,
upper = g_upper,
seTE = g_se,
studlab = Study,
data = df_forest,
sm = "SMD",
fixed = FALSE,
random = TRUE,
method.tau = "REML",
hakn = TRUE,
title = "Overall")
metabias(m.gen_overall, method.bias = "linreg")
# Horizontal bar graph ----------------------------------------------------
df_bar<- read.csv2("studies_list_validation_status.csv", header = T, sep=",")
df_bar[55,1]<- "oMDD"
df_bar[56,1]<- "yMDD"
# for emotion recognition tests
df_bar_emo<- df_bar[df_bar$Tool_type == 1,]
df_bar_emo <- df_bar_emo[order(df_bar_emo$Population_type,-df_bar_emo$Criteria.fulfilled),]
df1<- data.frame(Population = df_bar_emo$Population, Population_type=df_bar_emo$Population_type, x = df_bar_emo$Tool, y = df_bar_emo$Criteria.tested)
df2<- data.frame(Population = df_bar_emo$Population, Population_type=df_bar_emo$Population_type, x = df_bar_emo$Tool, y = df_bar_emo$Criteria.fulfilled)
df1$Population_type<- factor(df1$Population_type, levels = c(1,2,3,4,5,6),labels = c("Schizophrenia", "Autism", "Mood disorders", "Neurodegenerative diseases", "Acquired brain injury", "Mixed populations"))
df2$Population_type<- factor(df2$Population_type, levels = c(1,2,3,4,5,6),labels = c("Schizophrenia", "Autism", "Mood disorders", "Neurodegenerative diseases", "Acquired brain injury", "Mixed populations"))
df1$x<- as.factor(df1$x)
df2$x<- as.factor(df2$x)
df1$y<- as.factor(df1$y)
df2$y<- as.factor(df2$y)
df1$Population<- as.factor(df1$Population)
df2$Population<- as.factor(df2$Population)
my_colors<- c(brewer.pal(6, "Set2")[c(1,2,4,5)]) # specifyng colors manually to match other graphs
p1 <- ggplot(df2,aes(fct_rev(fct_inorder(interaction(factor(x), Population))),y, fill = Population_type)) +
geom_bar(stat="identity", position=position_dodge(width = 1), alpha=1) +
scale_fill_manual(values=my_colors) +
scale_x_discrete(labels = rev(df2$x)) +
coord_flip() +
labs(title = "Validation status of emotion recognition tasks",
y = "Number of criteria tested (gray) / fulfilled (colored)",
x = "Test",
fill = "Population type") +
geom_bar(data = df1, stat="identity", position=position_dodge(width= 1), alpha=0.3, fill = "gray") +
theme_classic2() +
theme(plot.title = element_text(hjust = 0.5)) +
theme(axis.title.y = element_blank()) +
theme(axis.text.y =element_text(size=12,hjust=1, vjust = 0.5, angle = 0)) +
theme(plot.title =element_text(size=14,hjust=0.5, vjust = 0.5, angle = 0)) +
theme(legend.position = "None")+
geom_text(aes(label = Population),hjust=1.2, vjust=0.5, fontface="italic", colour = "white")
p1
# for ToM tasks
df_bar_ToM<- df_bar[df_bar$Tool_type == 2,]
df_bar_ToM <- df_bar_ToM[order(df_bar_ToM$Population_type,-df_bar_ToM$Criteria.fulfilled),]
df1<- data.frame(Population = df_bar_ToM$Population, Population_type=df_bar_ToM$Population_type, x = df_bar_ToM$Tool, y = df_bar_ToM$Criteria.tested)
df2<- data.frame(Population = df_bar_ToM$Population, Population_type=df_bar_ToM$Population_type, x = df_bar_ToM$Tool, y = df_bar_ToM$Criteria.fulfilled)
df1$Population_type<- factor(df1$Population_type, levels = c(1,2,3,4,5,6),labels = c("Schizophrenia", "Autism", "Mood disorders", "Neurodegenerative diseases", "Acquired brain injury", "Mixed populations"))
df2$Population_type<- factor(df2$Population_type, levels = c(1,2,3,4,5,6),labels = c("Schizophrenia", "Autism", "Mood disorders", "Neurodegenerative diseases", "Acquired brain injury", "Mixed populations"))
df1$x<- as.factor(df1$x)
df2$x<- as.factor(df2$x)
df1$Population<- as.factor(df1$Population)
df2$Population<- as.factor(df2$Population)
p2 <- ggplot(df2,aes(fct_rev(fct_inorder(interaction(x, Population))),y, fill = Population_type)) +
geom_bar(stat="identity", position=position_dodge(width = 1), alpha=1) +
scale_fill_brewer(palette = "Set2") +
scale_x_discrete(labels = rev(df2$x)) +
scale_y_continuous(expand=c(0,0)) +
coord_flip() +
labs(title = "Validation status of ToM tasks",
y = "Number of criteria tested (gray) / fulfilled (colored)",
x = "Test",
fill = "Population type") +
geom_bar(data = df1, stat="identity", position=position_dodge(width= 1), alpha=0.3, fill = "gray") +
theme_classic2() +
theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "right") +
theme(axis.title.y = element_blank()) +
theme(axis.text.y =element_text(size=12,hjust=1, vjust = 0.5, angle = 0)) +
theme(plot.title =element_text(size=14,hjust=0.5, vjust = 0.5, angle = 0)) +
geom_text(aes(label = Population),hjust=1.2, vjust=0.5, size= 4, fontface="italic", colour = "white")
p2
# for batteries
df_bar_bat<- df_bar[df_bar$Tool_type == 3,]
df_bar_bat <- df_bar_bat[order(df_bar_bat$Population_type,-df_bar_bat$Criteria.fulfilled),]
df1<- data.frame(Population = df_bar_bat$Population, Population_type=df_bar_bat$Population_type, x = df_bar_bat$Tool, y = df_bar_bat$Criteria.tested)
df2<- data.frame(Population = df_bar_bat$Population, Population_type=df_bar_bat$Population_type, x = df_bar_bat$Tool, y = df_bar_bat$Criteria.fulfilled)
df1$Population_type<- factor(df1$Population_type, levels = c(1,2,3,4,5,6),labels = c("Schizophrenia", "Autism", "Mood disorders", "Neurodegenerative diseases", "Acquired brain injury", "Mixed populations"))
df2$Population_type<- factor(df2$Population_type, levels = c(1,2,3,4,5,6),labels = c("Schizophrenia", "Autism", "Mood disorders", "Neurodegenerative diseases", "Acquired brain injury", "Mixed populations"))
df1$x<- as.factor(df1$x)
df2$x<- as.factor(df2$x)
df1$Population<- as.factor(df1$Population)
df2$Population<- as.factor(df2$Population)
p3 <- ggplot(df2,aes(fct_rev(fct_inorder(interaction(x, Population))),y, fill = Population_type)) +
geom_bar(stat="identity", position=position_dodge(width = 1), alpha=1) +
scale_fill_brewer(palette = "Set2") +
scale_x_discrete(labels = rev(df2$x)) +
scale_y_continuous(expand=c(0,0)) +
coord_flip() +
labs(title = "Validation status of batteries",
y = "Number of criteria tested (gray) / fulfilled (colored)",
x = "Test",
fill = "Population type") +
geom_bar(data = df1, stat="identity", position=position_dodge(width= 1), alpha=0.3, fill = "gray") +
theme_classic2() +
theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "right") +
theme(axis.title.y = element_blank()) +
theme(axis.text.y =element_text(size=12,hjust=1, vjust = 0.5, angle = 0)) +
theme(plot.title =element_text(size=14,hjust=0.5, vjust = 0.5, angle = 0)) +
geom_text(aes(label = Population),hjust=1.2, vjust=0.5, size= 4, fontface="italic", colour = "white")
p3
# Table --------------------------------------------------------------
# Arranging data frames
df_stars_emo<- df[!is.na(df$Stars), ]
df_stars_emo<- df_stars_emo[df_stars_emo$Tool_type==1,]
df_stars_emo<- df_stars_emo[order(-df_stars_emo$Stars,df_stars_emo$Population_type),c(14,4,2,1)]
names(df_stars_emo)[names(df_stars_emo) == "Stars"] <- "Validation"
df_stars_ToM<- df[!is.na(df$Stars), ]
df_stars_ToM<- df_stars_ToM[df_stars_ToM$Tool_type==2,]
df_stars_ToM<- df_stars_ToM[order(-df_stars_ToM$Stars,df_stars_ToM$Population_type),c(14,4,2,1)]
names(df_stars_ToM)[names(df_stars_ToM) == "Stars"] <- "Validation"
df_stars_bat<- df[!is.na(df$Stars), ]
df_stars_bat<- df_stars_bat[df_stars_bat$Tool_type==3,]
df_stars_bat<- df_stars_bat[order(-df_stars_bat$Stars,df_stars_bat$Population_type),c(14,4,2,1)]
names(df_stars_bat)[names(df_stars_bat) == "Stars"] <- "Validation"
# Setting up visualization themes
tm1<- ttheme(base_size=8,
padding=unit(c(1.5, 1.5), "mm"))
my_palette<- terrain.colors(8)
my_palette<- my_palette[2:8]
# Setting up a common interpretation key
lgnd = data.frame(
"Sum of validation criteria" = c("5", "4", "3", "2","1", "0"),
"Validation status" = c("Strong", "Good", "Fair", "Questionable", "Insufficient", "Absent"),
check.names = FALSE)
lgnd_table<- ggtexttable(lgnd, theme = ttheme("blank", padding=unit(c(1.5, 1.5), "mm"),base_size=8),rows= NULL)
lgnd_table <- table_cell_bg(lgnd_table, row = 2, column = 1, fill = my_palette[1], alpha = 0.3, col="white", linewidth = 2)
lgnd_table <- table_cell_bg(lgnd_table, row = 3, column = 1, fill = my_palette[2], alpha = 0.3, col="white", linewidth = 2)
lgnd_table <- table_cell_bg(lgnd_table, row = 4, column = 1, fill = my_palette[3], alpha = 0.3, col="white", linewidth = 2)
lgnd_table <- table_cell_bg(lgnd_table, row = 5, column = 1, fill = my_palette[4], alpha = 0.3, col="white", linewidth = 2)
lgnd_table <- table_cell_bg(lgnd_table, row = 6, column = 1, fill = my_palette[5], alpha = 0.3, col="white", linewidth = 2)
lgnd_table <- table_cell_bg(lgnd_table, row = 7, column = 1, fill = my_palette[6], alpha = 0.3, col="white", linewidth = 2)
# Visualization: emotion recognition tasks
p_emo<- ggtexttable(df_stars_emo, theme = tm1, rows=NULL)
p_emo <- table_cell_bg(p_emo, row = 2:5, column = 1, fill = my_palette[2], alpha = 0.3, col="white", linewidth = 2)
p_emo <- table_cell_bg(p_emo, row = 6:12, column = 1, fill = my_palette[3], alpha = 0.3, col="white", linewidth = 2)
p_emo <- table_cell_bg(p_emo, row = 13:18, column = 1, fill = my_palette[4], alpha = 0.3, col="white", linewidth = 2)
p_emo <- table_cell_bg(p_emo, row = 19:22, column = 1, fill = my_palette[5], alpha = 0.3, col="white", linewidth = 2)
# Visualization: ToM tasks
p_ToM<- ggtexttable(df_stars_ToM, theme = tm1, rows=NULL)
p_ToM <- table_cell_bg(p_ToM, row = 2:6, column = 1, fill = my_palette[1], alpha = 0.3, col="white", linewidth = 2)
p_ToM <- table_cell_bg(p_ToM, row = 7:15, column = 1, fill = my_palette[2], alpha = 0.3, col="white", linewidth = 2)
p_ToM <- table_cell_bg(p_ToM, row = 16:24, column = 1, fill = my_palette[3], alpha = 0.3, col="white", linewidth = 2)
p_ToM <- table_cell_bg(p_ToM, row = 25:33, column = 1, fill = my_palette[4], alpha = 0.3, col="white", linewidth = 2)
p_ToM <- table_cell_bg(p_ToM, row = 34:41, column = 1, fill = my_palette[5], alpha = 0.3, col="white", linewidth = 2)
p_ToM <- table_cell_bg(p_ToM, row = 42, column = 1, fill = my_palette[6], alpha = 0.3, col="white", linewidth = 2)
# Visualization: batteries
p_bat<- ggtexttable(df_stars_bat, theme = tm1, rows=NULL)
p_bat <- table_cell_bg(p_bat, row = 2, column = 1, fill = my_palette[1], alpha = 0.3, col="white", linewidth = 2)
p_bat <- table_cell_bg(p_bat, row = 3:4, column = 1, fill = my_palette[2], alpha = 0.3, col="white", linewidth = 2)
p_bat <- table_cell_bg(p_bat, row = 5:10, column = 1, fill = my_palette[3], alpha = 0.3, col="white", linewidth = 2)
p_bat <- table_cell_bg(p_bat, row = 11:16, column = 1, fill = my_palette[4], alpha = 0.3, col="white", linewidth = 2)
p_bat <- table_cell_bg(p_bat, row = 17:19, column = 1, fill = my_palette[5], alpha = 0.3, col="white", linewidth = 2)
#Arranging the graphs
valid_graph<- ggarrange(p_emo,
p_ToM,
p_bat,
#hjust = -1, vjust = 1.5,
ncol = 2, nrow = 2)
valid_graph
# New graph ---------------------------------------------------------------
df<- read.csv2("Studies_list_validation_status2.csv", header = T, sep=",")
df<- df[,2:12]
# Computing psychometric criteria tested
Content_validity_t <- ifelse(!is.na(df$Content.Validity),1,0)
Convergent_validity_t<- ifelse(!is.na(df$Convergent.validity),1,0)
Knowngroup_validity_t<-ifelse(!is.na(df$Known.group.validity),1,0)
SensSpecAUC_t<- ifelse(!is.na(df$Sens.Spec.AUC),1,0)
Testretest_reliability_t<- ifelse(!is.na(df$Test.retest.reliability),1,0)
Interrater_reliability_t<- ifelse(!is.na(df$Inter.rater.reliability),1,0)
Internal_consistency_t<- ifelse(!is.na(df$Internal.consistency),1,0)
# Computing psychometric variables fulfilled
Content_validity_f <- ifelse(is.na(df$Content.Validity),0,df$Content.Validity)
Convergent_validity_f<- ifelse(is.na(df$Convergent.validity),0,df$Convergent.validity)
Knowngroup_validity_f<-ifelse(is.na(df$Known.group.validity),0,df$Known.group.validity)
SensSpecAUC_f<- ifelse(is.na(df$Sens.Spec.AUC),0,df$Sens.Spec.AUC)
Testretest_reliability_f<- ifelse(is.na(df$Test.retest.reliability),0,df$Test.retest.reliability)
Interrater_reliability_f<- ifelse(is.na(df$Inter.rater.reliability),0,df$Inter.rater.reliability)
Internal_consistency_f<- ifelse(is.na(df$Internal.consistency),0,df$Internal.consistency)
df<- data.frame(df, Content_validity_t,Content_validity_f, Convergent_validity_t,Convergent_validity_f,
Knowngroup_validity_t,Knowngroup_validity_f, SensSpecAUC_t,SensSpecAUC_f,Testretest_reliability_t,
Testretest_reliability_f,Interrater_reliability_t,Interrater_reliability_f,Internal_consistency_t,
Internal_consistency_f)
# Summing criteria for same test in same group
library(tidyverse)
df$Population<- as.factor(df$Population)
df$Population_type<- factor(df$Population_type, levels = c(1:6), labels = c("SZ", "ASD", "Mood and Anxiety", "Neurodegenerative", "ABI", "Others"))
df$Tool_type<- factor(df$Tool_type, levels = c(1,2,3), labels = c("emotion recognition", "ToM", "Batteries"))
df1<- df %>%
group_by(Tool,Population, Tool_type) %>%
summarise(across(where(is.numeric), sum))
df2<- df1[,c(1:3,11:24)]
# Reformatting for plot
df3<- df2[rep(seq_len(nrow(df2)), each = 7),1:3]
Psych_crit<- rep(c(1:7),74)
df3<- data.frame(df3, Psych_crit)
df2_t<- df2[,c(4,6,8,10,12,14,16)]
df2_f<- df2[,c(5,7,9,11,13,15,17)]
values<- c()
for (i in 1:nrow(df2_t)){
new_values<- c(unlist(df2_t[i,], use.names= FALSE))
values<- append(values, new_values)
}
df3_t<- data.frame(df3,values)
values<- c()
for (i in 1:nrow(df2_f)){
new_values<- c(unlist(df2_f[i,], use.names= FALSE))
values<- append(values, new_values)
}
df3_f<- data.frame(df3,values)
df3_t_emo<- df3_t[df3_t$Tool_type=="emotion recognition",]
df3_f_emo<- df3_f[df3_f$Tool_type=="emotion recognition",]
df3_t_emo$Tool<- as.factor(df3_t_emo$Tool)
df3_t_emo$Psych_crit<- as.factor(df3_t_emo$Psych_crit)
df3_f_emo$Tool<- as.factor(df3_f_emo$Tool)
df3_f_emo$Psych_crit<- as.factor(df3_f_emo$Psych_crit)
# Plotting
ggplot(df3_f_emo,aes(fct_rev(fct_inorder(interaction(Tool, Population))),values, fill = fct_rev(Psych_crit))) +
geom_bar(stat="identity", position=position_stack(vjust = 1), alpha=1) +
scale_fill_brewer(palette = "Set2") +
#scale_x_discrete(labels = rev(df2$x)) +
scale_y_continuous(expand=c(0,0)) +
coord_flip() +
labs(title = "Validation status of emotion recognition tests",
y = "Number of criteria tested (gray) / fulfilled (colored)",
x = "Test",
fill = "Psychometric criteria") +
#geom_bar(data = df3_t_emo, stat="identity", position = position_nudge(x=0,y=0), alpha=0.3, fill= "gray") +
theme_classic2() +
theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "right") +
theme(axis.title.y = element_blank()) +
theme(axis.text.y =element_text(size=12,hjust=1, vjust = 0.5, angle = 0)) +
theme(plot.title =element_text(size=14,hjust=0.5, vjust = 0.5, angle = 0)) +
geom_text(aes(label = Population),hjust=1.2, vjust=0.5, size= 4, fontface="italic", colour = "white")