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Statistical_scripts/Performance.R

+### Script for elife Paper Performance - 18/04/2024
+
+### Install Packages
+
+install.packages("GGally")
+install.packages("ggplot2")       
+install.packages("ggpubr")
+install.packages("readxl")
+install.packages("viridis")
+install.packages("dplyr")
+install.packages("viridis")
+
+
+### Load packages
+
+library(viridis)
+library("GGally")
+library("ggplot2")
+library(ggpubr)
+library(readxl)
+library(dplyr)
+library(RColorBrewer)
+
+### Figure 3 - G,H
+
+### Emotional Task ###
+
+Perf_Emo  <- read.csv("Hypo_5Con_Analysis_EMO.csv")
+
+Perf_Neut  <- read.csv("Hypo_5Con_Analysis_Neut.csv")
+
+EAS <-ggplot(Perf_Emo, aes(x=RT_Mel_mean , y=Betas_Emo , color=as.factor(Light))) + 
+  geom_point(show.legend = FALSE) +
+  scale_color_manual(values=c('#A9A9A9',"#FF8000" , "#9ECAE1", "#6BAED6", "#4292C6")) +
+  theme_classic(base_size = 14) + theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("Reaction times (ms)") + ylab("Emotional Betas (a.u.)") +
+  labs(tag = "G")+
+  scale_y_continuous(limits = c(-4,4))+
+  scale_x_continuous(limits = c(800,1600))+
+  geom_smooth(aes(x=RT_Mel_mean , y=Betas_Emo),
+              method=glm,se=FALSE, show.legend = FALSE, inherit.aes = FALSE, colour = "black")
+
+ENS <-ggplot(Perf_Neut, aes(x=RT_Mel_mean , y=Betas_Emo , color=as.factor(Light))) + 
+  geom_point(show.legend = FALSE) +
+  scale_color_manual(values=c('#A9A9A9',"#FF8000" , "#9ECAE1", "#6BAED6", "#4292C6")) +
+  theme_classic(base_size = 14) + theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("Reaction times (ms)") + ylab("Neutral Betas (a.u.)") +
+  labs(tag = "H")+
+  scale_y_continuous(limits = c(-4,4))+
+  scale_x_continuous(limits = c(800,1600))+
+  geom_smooth(aes(x=RT_Mel_mean , y=Betas_Emo),
+              method=glm,se=FALSE, show.legend = FALSE, inherit.aes = FALSE, colour = "grey38", linetype = "dashed")
+
+### Figure 3 - F
+
+RT_emo <- ggplot(Perf_Emo, aes(x=Light, y=RT_Mel_mean, fill=as.factor(Light))) + 
+  geom_boxplot(width=20, show.legend = FALSE) +
+  scale_fill_manual(values=c('#D9D9D9',"#FDAE6B" , "#9ECAE1", "#6BAED6", "#4292C6"))+
+  theme_classic(base_size = 14) +
+  theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("mel EDI") + ylab("Reaction times (ms)") +
+  labs(tag = "F")
+
+
+### Nback Performance ###
+
+Perf_N2  <- read.csv("2back_PERF.csv")
+
+Perf_N0  <- read.csv("0back_PERF.csv")
+
+N2_3 <- ggplot(Perf_N2, aes(x=Accuracy, y=Hypo3, color=as.factor(Mel))) + 
+  geom_point(show.legend = FALSE) +
+  scale_color_manual(values=c('#A9A9A9',"#FF8000" , "#9ECAE1", "#6BAED6", "#4292C6")) +
+  theme_classic(base_size = 14) +
+  theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("Accuracy") + ylab("2-back activity estimates (a.u.)") +
+  labs(tag = "B")+
+  scale_y_continuous(limits = c(-1,1))+
+  geom_smooth(aes(x=Accuracy , y=Hypo3),
+              method=glm,se=FALSE, show.legend = FALSE, inherit.aes = FALSE, colour = "black")
+
+
+N2_1 <- ggplot(Perf_N2, aes(x=Accuracy , y=Hypo1, color=as.factor(Mel))) + 
+  geom_point(show.legend = FALSE) +
+  scale_color_manual(values=c('#A9A9A9',"#FF8000" , "#9ECAE1", "#6BAED6", "#4292C6")) +
+  theme_classic(base_size = 14) +
+  theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("Accuracy") + ylab("2-back Betas (a.u.)") +
+  labs(tag = "C")+
+  scale_y_continuous(limits = c(-2,2))+
+  geom_smooth(aes(x=Accuracy , y=Hypo1),
+              method=glm,se=FALSE, show.legend = FALSE, inherit.aes = FALSE, colour = "grey38", linetype = "dashed")
+
+
+N2_4 <- ggplot(Perf_N2, aes(x=Accuracy , y=Hypo4, color=as.factor(Mel))) + 
+  geom_point(show.legend = FALSE) +
+  scale_color_manual(values=c('#A9A9A9',"#FF8000" , "#9ECAE1", "#6BAED6", "#4292C6")) +
+  theme_classic(base_size = 14) +
+  theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("Accuracy") + ylab("2-back Betas (a.u.)") +
+  labs(tag = "D")+
+  scale_y_continuous(limits = c(-2,2))+
+  geom_smooth(aes(x=Accuracy , y=Hypo4),
+              method=glm,se=FALSE, show.legend = FALSE, inherit.aes = FALSE, colour = "grey38", linetype = "dashed")
+
+
+
+N0_3 <- ggplot(Perf_N0, aes(x=Accuracy, y=Hypo3, color=as.factor(Mel))) + 
+  geom_point(show.legend = FALSE) +
+  scale_color_manual(values=c('#A9A9A9',"#FF8000" , "#9ECAE1", "#6BAED6", "#4292C6")) +
+  theme_classic(base_size = 14) +
+  theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("Accuracy") + ylab("0-back Betas (a.u.)") +
+  labs(tag = "E")+
+  scale_y_continuous(limits = c(-2,2))+
+  geom_smooth(aes(x=Accuracy , y=Hypo3),
+              method=glm,se=FALSE, show.legend = FALSE, inherit.aes = FALSE, colour = "grey38", linetype = "dashed")
+
+
+Acc_N2 <- ggplot(Perf_N2, aes(x=Mel, y=Accuracy, fill=as.factor(Mel))) + 
+  geom_boxplot(width=20, show.legend = FALSE) +
+  scale_fill_manual(values=c('#D9D9D9',"#FDAE6B" , "#9ECAE1", "#6BAED6", "#4292C6"))+
+  theme_classic(base_size = 14) +
+  theme(
+    axis.text.x = element_text(color="black"),
+    axis.ticks = element_line(color = "black")
+  )+
+  xlab("mel EDI") + ylab("Accuracy") +
+  labs(tag = "A")
+
+
+ggarrange(N0_3, RT_emo, EAS, ENS, nrow = 2, ncol=2) 
+
+ggarrange(Acc_N2, N2_3, N2_1, N2_4, nrow = 2, ncol=2) 
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