###############  B011  ######################
# set path
path = "C:/Experiments/B011/"
setwd(path)


# Read CSV into R
#Note: Data from Subj# 2 (left-hander) and Subj#15 (recording error) are not included in the csv file
data <- read.csv(file="B011_Data_for_Analysis.csv", header=TRUE, sep=';')

#replace commas by dots in column "maxGA"
data$maxGA <- as.numeric(gsub(",", ".", gsub("\\.", "", data$maxGA)))

#Frequencies of correct/error trials
Error_table <- table(data$Subj,data$correct,data$Compatibility)
EF <- prop.table(Error_table, c(1,3))
Error_Freq <- as.data.frame(EF)
names(Error_Freq) <- c("Subj", "Correct", "Compatibility", "Freq")

require(ggplot2)
EFBar <- ggplot(Error_Freq,aes(Subj,Freq, fill = Correct)) + facet_wrap(~Compatibility)
EFBar + stat_summary(fun.y = mean, geom = "bar", position = "dodge", color = "black") + scale_fill_manual(values = c("black","white")) + labs(x="Subjects", y = "Frequency")+ theme(panel.background = element_rect(fill = "white"))


#Exclude subjects# 16,19,22,29,30
Subj.to.remove <- c(16,19,22,29,30)
data <- data[! data$Subj %in% Subj.to.remove,]

#Frequencies of different errors
#errortype: 0	no error                      kinematic errors: 0 no error
#           1	too fast                                        1 velocity off
#           2	wrong hand                                      2 bimodal GA
#           3	too slow RT                                     3 acceleration off
#           4	correct hand but then error                     4 marker loss
#           5	too slow AT
#           6 marker loss
etf <- table(data$errortype)
Errortype_Freq <- prop.table(etf)
kin_err <- table(data$kinematic.error)
Kin_Errortype_Freq <- prop.table(kin_err)

# Exclude errors (i.e., errortypes 1-5 & kinematic errors 1 + 4)
data <- data[data$errortype==0,]
KinErr.to.remove <- c(1,4)
data <- data[! data$kinematic.error %in% KinErr.to.remove,]


#Factorize
data$Subj <- factor(data$Subj)
data$Target <- factor(data$Target)
data$cong <- factor(data$cong)
data$errortype <- factor(data$errortype)
data$Block <- factor(data$Block)
data$trial <- factor(data$trial)
data$kinematic.error <-factor(data$kinematic.error)
data$hand <-factor(data$hand)
summary(data)


###############Using graphs to explore the data#################################

#Histograms
Histo <- ggplot(data, aes(RT))
Histo  + geom_histogram(aes(fill = Compatibility),binwidth = 20,position = "identity",alpha = 0.7) + labs(x = "RT" , y = "Frequency") +facet_wrap(~hand) + theme(panel.background = element_rect(fill = "white"))
Histo  + geom_histogram(aes(y=..density..,fill = Compatibility),binwidth = 20,position = "identity",alpha = 0.7) + labs(x = "RT" , y = "Frequency") +facet_wrap(~hand) + theme(panel.background = element_rect(fill = "white"))
Histo  + geom_histogram(aes(fill = Subj),binwidth = 20,position = "identity",alpha = 0.7) + labs(x = "RT" , y = "Frequency") + theme(panel.background = element_rect(fill = "white"))

#Density plots
B011Density <- ggplot(data, aes(RT))
B011Density + geom_density(aes(fill = Compatibility),alpha = 0.5)+ labs(x = "RT" , y = "Frequency") +facet_wrap(~hand)
B011Density + geom_density(aes(fill = Subj),alpha = 0.5)+ labs(x = "RT" , y = "Frequency") 


#Boxplots
B011Box <- ggplot(data, aes(Subj,RT))
B011Box + geom_boxplot(aes(fill = Compatibility), coef = 3.0) #whiskers = 3.0 x IQR
############################################################################

#Exclude trials in which RT < 200ms or > 750ms and in which MT >1000m
table(data$RT<200)
table(data$RT>750)
table(data$MT>1000)

data <- data[data$RT>=200,]
data <- data[data$RT<=750,]
data <- data[data$MT<=1000,]


################# DATA PLOTTING #########################

#################### RT #################################
#aggregate data
agg_data_RT <- with(data, aggregate(RT, list(Subj, Compatibility, hand), mean))
names(agg_data_RT) <- c("Subj", "Compatibility", "hand", "MeanRT")
summary(agg_data_RT)

#rearrange and export for JAPS
library(reshape2)
agg_data_wide_RT <- dcast(agg_data_RT, Subj ~Compatibility+hand, value.var = "MeanRT")
write.csv2(agg_data_wide_RT, "agg_data_B011_RT.csv")

#calculate condition means + credible intervals for plotting
agg_data_RT_MCI <- with(agg_data_RT, aggregate(MeanRT, list(Compatibility, hand), mean)) #conditions means + credible intervals inserted from JASP output
names(agg_data_RT_MCI) <- c("Compatibility", "hand", "MeanRT")
agg_data_RT_MCI$upperCI <- c(331,370,342,368) #these values come from JASP
agg_data_RT_MCI$lowerCI <- c(305,334,307,323) #these values come from JASP
summary(agg_data_RT_MCI)  


##### calculate RT difference (incongruent minus congruent for each hand) #####
MeanRTdiff_LH <- agg_data_RT$MeanRT[agg_data_RT$Compatibility=="incompatible" & agg_data_RT$hand=="left"]  - agg_data_RT$MeanRT[agg_data_RT$Compatibility=="compatible" & agg_data_RT$hand=="left"] 
MeanRTdiff_RH <- agg_data_RT$MeanRT[agg_data_RT$Compatibility=="incompatible" & agg_data_RT$hand=="right"] - agg_data_RT$MeanRT[agg_data_RT$Compatibility=="compatible" & agg_data_RT$hand=="right"] 
agg_data_RT_diff <- as.data.frame(agg_data_RT[1:24,"Subj"])
agg_data_RT_diff <- cbind(agg_data_RT_diff,MeanRTdiff_LH,MeanRTdiff_RH)
names(agg_data_RT_diff) <- c("Subj","left","right")

require("tidyr")
agg_data_RT_diff <- gather(agg_data_RT_diff, hand, MeanRT_diff, left,right, factor_key=TRUE)

agg_data_wide_RT_diff <- dcast(agg_data_RT_diff, Subj ~hand, value.var = "MeanRT_diff")
write.csv2(agg_data_wide_RT_diff, "agg_data_B011_RT_diff.csv")

agg_data_RT_diff_MCI <- with(agg_data_RT_diff, aggregate(MeanRT_diff, list(hand), mean)) #conditions means + credible intervals inserted from JASP output
names(agg_data_RT_diff_MCI) <- c("hand", "MeanRT_diff")
agg_data_RT_diff_MCI$upperCI <- c(50,36) #these values come from JASP
agg_data_RT_diff_MCI$lowerCI <- c(19,7) #these values come from JASP
summary(agg_data_RT_diff_MCI)  
##


#RT Point plots of aggregated data:
agg_data_RT$hand <- as.numeric(agg_data_RT$hand)
agg_data_RT_MCI$hand <- as.numeric(agg_data_RT_MCI$hand)

B011Point <- ggplot(agg_data_RT,aes(hand,MeanRT, fill = Compatibility)) +
  geom_point(position = position_jitterdodge(jitter.width=0.05, dodge.width = 0.5),
            shape = 21,
            size =5,stroke = 0) + #plot individual data points
   geom_errorbar(data = agg_data_RT_MCI,aes(ymin=lowerCI, ymax=upperCI),
            position = position_dodge(width = 0.50),
            width = 0.2, size=2,alpha = 0.5,
            color = c("#8b0000","#6A6A6A","#8b0000","#6A6A6A")) + 
                #B011Point <- B011Point + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width=0.50), width = 0.2, size =1,color = c("#003070","#007030","#003070","#007030"))
  stat_summary(fun.y = mean, geom = "point",
            shape = 21,
            size = 15,stroke = 0,alpha = 0.5,
            position = position_dodge(width=0.50)) + 
  scale_fill_manual(name = "", values=c("#6A6A6A","#8b0000")) +
    labs(x="Response hand", y = "Reaction time [ms]") +
              #B011Point <- B011Point + theme(panel.background = element_rect(fill = "white"),text = element_text(size=20))
              #theme_classic(base_size = 25)
  theme(panel.background = element_rect(fill = "white", colour = "black", size =1, linetype = "blank"),
        axis.line = element_line(color="black", size = 1),
        text = element_text(size=35,color = "black"),
        axis.ticks = element_line(color = "black", size = 1),
        axis.text = element_text(color = "black", size =30),
        axis.ticks.length = unit(0.3,"cm"),
        legend.position = "top")+
        coord_cartesian(ylim=c(250,400)) +
  scale_x_continuous(labels=c("left","right"), 
                     breaks=c(1,2), limits=c(0.5,2.5), expand=c(0,0))
B011Point
ggsave("B011_RT.pdf", width = 28, height = 20, units = "cm")




agg_data_RT_diff$hand <- as.integer(agg_data_RT_diff$hand)
agg_data_RT_diff_MCI$hand <- as.integer(agg_data_RT_diff_MCI$hand)
#RT Point plots of aggregated difference data:
B011_diffPoint <- ggplot(agg_data_RT_diff,aes(hand, MeanRT_diff)) +
  geom_point(#position = position_jitterdodge(jitter.width=0.05, dodge.width = 0.5),
              size =5,stroke = 0,
              colour = c("#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F",
                         "#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F","#2F4F4F",
                         "#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969",
                         "#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969","#696969")) + #plot individual data points
  geom_errorbar(data = agg_data_RT_diff_MCI,aes(ymin=lowerCI, ymax=upperCI),
                position = position_dodge(width = 0.50),
                width = 0.2, size=2,
                color = c("#2F4F4F","#696969")) + 
  stat_summary(fun.y = mean, geom = "point",
               size = 15,stroke = 0,alpha = 0.5,
               position = position_dodge(width=0.50),
               colour = c("#2F4F4F","#696969")) + 
  scale_fill_manual(values=c("#2F4F4F","#696969")) +
  labs(x="Response hand", y = "Reaction time difference [ms]") +
  theme(panel.background = element_rect(fill = "white", colour = "black", size =1, linetype = "blank"),
        axis.line = element_line(color="black", size = 1),
        text = element_text(size=35,color = "black"),
        axis.ticks = element_line(color = "black", size = 1),
        axis.text = element_text(color = "black", size =30),
        axis.ticks.length = unit(0.3,"cm")) + 
  coord_cartesian(ylim=c(-50,150))+
  scale_x_continuous(labels=c("left","right"), 
                     breaks=c(1,2), limits=c(0.5,2.5), expand=c(0.1,0.1))
B011_diffPoint
ggsave("B011_RT_diff.pdf", width = 28, height = 20, units = "cm")

#########################################################


#################### MT #################################
agg_data_MT <- with(data, aggregate(MT, list(Subj, Compatibility, hand), mean))
names(agg_data_MT) <- c("Subj", "Compatibility", "hand", "MeanMT")
summary(agg_data_MT)

#rearrange and export for JAPS
library(reshape2)
agg_data_wide_MT <- dcast(agg_data_MT, Subj ~Compatibility+hand, value.var = "MeanMT")
write.csv2(agg_data_wide_MT, "agg_data_B011_MT.csv")

#calculate condition means + cfredible intervals
agg_data_MT_MCI <- with(agg_data_MT, aggregate(MeanMT, list(Compatibility, hand), mean)) #conditions means + credible intervals inserted from JASP output
names(agg_data_MT_MCI) <- c("Compatibility", "hand", "MeanMT")
agg_data_MT_MCI$upperCI <- c(660,645,612,672) #these values come from JASP
agg_data_MT_MCI$lowerCI <- c(600,586,557,611) #these values come from JASP
summary(agg_data_MT_MCI)  

#MT Point plots of aggregated data:
agg_data_MT$hand <- as.numeric(agg_data_MT$hand)
agg_data_MT_MCI$hand <- as.numeric(agg_data_MT_MCI$hand)

B011Point <- ggplot(agg_data_MT,aes(hand,MeanMT, fill = Compatibility)) +
  geom_point(position = position_jitterdodge(jitter.width=0.05, dodge.width = 0.5),
             shape = 21,
             size =5,stroke = 0) + #plot individual data points
  geom_errorbar(data = agg_data_MT_MCI,aes(ymin=lowerCI, ymax=upperCI),
                position = position_dodge(width = 0.50),
                width = 0.2, size=2,alpha = 0.5,
                color = c("#8b0000","#6A6A6A","#8b0000","#6A6A6A")) + 
  #B011Point <- B011Point + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width=0.50), width = 0.2, size =1,color = c("#003070","#007030","#003070","#007030"))
  stat_summary(fun.y = mean, geom = "point",
               shape = 21,
               size = 15,stroke = 0,alpha = 0.5,
               position = position_dodge(width=0.50)) + 
  scale_fill_manual(name = "", values=c("#6A6A6A","#8b0000")) +
  labs(x="Response hand", y = "Movement time [ms]") +
  #B011Point <- B011Point + theme(panel.background = element_rect(fill = "white"),text = element_text(size=20))
  #theme_classic(base_size = 25)
  theme(panel.background = element_rect(fill = "white", colour = "black", size =1, linetype = "blank"),
        axis.line = element_line(color="black", size = 1),
        text = element_text(size=35,color = "black"),
        axis.ticks = element_line(color = "black", size = 1),
        axis.text = element_text(color = "black", size =30),
        axis.ticks.length = unit(0.3,"cm"),
        legend.position = "top")+
  coord_cartesian(ylim=c(500,800)) +
  scale_x_continuous(labels=c("left","right"), 
                     breaks=c(1,2), limits=c(0.5,2.5), expand=c(0,0))
B011Point
ggsave("B011_MT.pdf", width = 28, height = 20, units = "cm")



########## peak vel ######

agg_data_peakvel <- with(data, aggregate(peak.vel, list(Subj, Compatibility, hand), mean))
names(agg_data_peakvel) <- c("Subj", "Compatibility", "hand", "Meanpeakvel")
summary(agg_data_peakvel)

#rearrange and export for JASP
library(reshape2)
agg_data_wide_peakvel <- dcast(agg_data_peakvel, Subj ~Compatibility+hand, value.var = "Meanpeakvel")
write.csv2(agg_data_wide_peakvel, "agg_data_B011_peakvel.csv")

#calculate condition means + credible intervals
agg_data_peakvel_MCI <- with(agg_data_peakvel, aggregate(Meanpeakvel, list(Compatibility, hand), mean)) #conditions means + credible intervals inserted from JASP output
names(agg_data_peakvel_MCI) <- c("Compatibility", "hand", "Meanpeakvel")
agg_data_peakvel_MCI$upperCI <- c(1291,1360,1392,1308) #these values come from JASP
agg_data_peakvel_MCI$lowerCI <- c(1176,1218,1259,1178) #these values come from JASP
summary(agg_data_peakvel_MCI) 

#Peak vel Point plots of aggregated data:
agg_data_peakvel$hand <- as.numeric(agg_data_peakvel$hand)
agg_data_peakvel_MCI$hand <- as.numeric(agg_data_peakvel_MCI$hand)

B011Point <- ggplot(agg_data_peakvel,aes(hand,Meanpeakvel, fill = Compatibility)) +
  geom_point(position = position_jitterdodge(jitter.width=0.05, dodge.width = 0.5),
             shape = 21,
             size =5,stroke = 0) + #plot individual data points
  geom_errorbar(data = agg_data_peakvel_MCI,aes(ymin=lowerCI, ymax=upperCI),
                position = position_dodge(width = 0.50),
                width = 0.2, size=2,alpha = 0.5,
                color = c("#8b0000","#6A6A6A","#8b0000","#6A6A6A")) + 
  #B011Point <- B011Point + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width=0.50), width = 0.2, size =1,color = c("#003070","#007030","#003070","#007030"))
  stat_summary(fun.y = mean, geom = "point",
               shape = 21,
               size = 15,stroke = 0,alpha = 0.5,
               position = position_dodge(width=0.50)) + 
  scale_fill_manual(name = "", values=c("#6A6A6A","#8b0000")) +
  labs(x="Response hand", y = "Peak velocity [mm/s]") +
  #B011Point <- B011Point + theme(panel.background = element_rect(fill = "white"),text = element_text(size=20))
  #theme_classic(base_size = 25)
  theme(panel.background = element_rect(fill = "white", colour = "black", size =1, linetype = "blank"),
        axis.line = element_line(color="black", size = 1),
        text = element_text(size=35,color = "black"),
        axis.ticks = element_line(color = "black", size = 1),
        axis.text = element_text(color = "black", size =30),
        axis.ticks.length = unit(0.3,"cm"),
        legend.position = "top")+
  coord_cartesian(ylim=c(900,1800)) +
  scale_x_continuous(labels=c("left","right"), 
                     breaks=c(1,2), limits=c(0.5,2.5), expand=c(0,0))
B011Point
ggsave("B011_peakvel.pdf", width = 28, height = 20, units = "cm")
####################


########## abs tpeak vel ######
agg_data_abstpeakvel <- with(data, aggregate(abs.tpeak.vel, list(Subj, Compatibility, hand), mean))
names(agg_data_abstpeakvel) <- c("Subj", "Compatibility", "hand", "Meanabstpeakvel")
summary(agg_data_abstpeakvel)

#rearrange and export for JAPS
library(reshape2)
agg_data_wide_abstpeakvel <- dcast(agg_data_abstpeakvel, Subj ~Compatibility+hand, value.var = "Meanabstpeakvel")
write.csv2(agg_data_wide_abstpeakvel, "agg_data_B011_abstpeakvel.csv")

#calculate condition means + credible intervals
agg_data_abstpeakvel_MCI <- with(agg_data_abstpeakvel, aggregate(Meanabstpeakvel, list(Compatibility, hand), mean)) #conditions means + credible intervals inserted from JASP output
names(agg_data_abstpeakvel_MCI) <- c("Compatibility", "hand", "Meanabstpeakvel")
agg_data_abstpeakvel_MCI$upperCI <- c(233,240,234,236) #these values come from JASP
agg_data_abstpeakvel_MCI$lowerCI <- c(211,215,213,214) #these values come from JASP
summary(agg_data_abstpeakvel_MCI) 

#abs tPeak vel Point plots of aggregated data:
agg_data_abstpeakvel$hand <- as.numeric(agg_data_abstpeakvel$hand)
agg_data_abstpeakvel_MCI$hand <- as.numeric(agg_data_abstpeakvel_MCI$hand)

B011Point <- ggplot(agg_data_abstpeakvel,aes(hand,Meanabstpeakvel, fill = Compatibility)) +
  geom_point(position = position_jitterdodge(jitter.width=0.05, dodge.width = 0.5),
             shape = 21,
             size =5,stroke = 0) + #plot individual data points
  geom_errorbar(data = agg_data_abstpeakvel_MCI,aes(ymin=lowerCI, ymax=upperCI),
                position = position_dodge(width = 0.50),
                width = 0.2, size=2,alpha = 0.5,
                color = c("#8b0000","#6A6A6A","#8b0000","#6A6A6A")) + 
  #B011Point <- B011Point + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width=0.50), width = 0.2, size =1,color = c("#003070","#007030","#003070","#007030"))
  stat_summary(fun.y = mean, geom = "point",
               shape = 21,
               size = 15,stroke = 0,alpha = 0.5,
               position = position_dodge(width=0.50)) + 
  scale_fill_manual(name = "", values=c("#6A6A6A","#8b0000")) +
  labs(x="Response hand", y = "Time to peak velocity [ms]") +
  #B011Point <- B011Point + theme(panel.background = element_rect(fill = "white"),text = element_text(size=20))
  #theme_classic(base_size = 25)
  theme(panel.background = element_rect(fill = "white", colour = "black", size =1, linetype = "blank"),
        axis.line = element_line(color="black", size = 1),
        text = element_text(size=35,color = "black"),
        axis.ticks = element_line(color = "black", size = 1),
        axis.text = element_text(color = "black", size =30),
        axis.ticks.length = unit(0.3,"cm"),
        legend.position = "top")+
  coord_cartesian(ylim=c(150,300)) +
  scale_x_continuous(labels=c("left","right"), 
                     breaks=c(1,2), limits=c(0.5,2.5), expand=c(0,0))
B011Point
ggsave("B011_abstpeakvel.pdf", width = 28, height = 20, units = "cm")
#######

########## max Grip Aperture ######
agg_data_maxGA <- with(data, aggregate(maxGA, list(Subj, Compatibility, hand), mean))
names(agg_data_maxGA) <- c("Subj", "Compatibility", "hand", "MeanmaxGA")
summary(agg_data_maxGA)

#rearrange and export for JAPS
library(reshape2)
agg_data_wide_maxGA <- dcast(agg_data_maxGA, Subj ~Compatibility+hand, value.var = "MeanmaxGA")
write.csv2(agg_data_wide_maxGA, "agg_data_B011_maxGA.csv")

#calculate condition means + credible intervals
agg_data_maxGA_MCI <- with(agg_data_maxGA, aggregate(MeanmaxGA, list(Compatibility, hand), mean)) #conditions means + credible intervals inserted from JASP output
names(agg_data_maxGA_MCI) <- c("Compatibility", "hand", "MeanmaxGA")
agg_data_maxGA_MCI$upperCI <- c(72,105,106,74) #these values come from JASP
agg_data_maxGA_MCI$lowerCI <- c(69,97,99,68) #these values come from JASP
summary(agg_data_maxGA_MCI)

#max GA vel Point plots of aggregated data:
agg_data_maxGA$hand <- as.numeric(agg_data_maxGA$hand)
agg_data_maxGA_MCI$hand <- as.numeric(agg_data_maxGA_MCI$hand)

B011Point <- ggplot(agg_data_maxGA,aes(hand,MeanmaxGA, fill = Compatibility)) +
  geom_point(position = position_jitterdodge(jitter.width=0.05, dodge.width = 0.5),
             shape = 21,
             size =5,stroke = 0) + #plot individual data points
  geom_errorbar(data = agg_data_maxGA_MCI,aes(ymin=lowerCI, ymax=upperCI),
                position = position_dodge(width = 0.50),
                width = 0.2, size=2,alpha = 0.5,
                color = c("#8b0000","#6A6A6A","#8b0000","#6A6A6A")) + 
  #B011Point <- B011Point + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width=0.50), width = 0.2, size =1,color = c("#003070","#007030","#003070","#007030"))
  stat_summary(fun.y = mean, geom = "point",
               shape = 21,
               size = 15,stroke = 0,alpha = 0.5,
               position = position_dodge(width=0.50)) + 
  scale_fill_manual(name = "", values=c("#6A6A6A","#8b0000")) +
  labs(x="Response hand", y = "Maximal grip aperture (mm)") +
  #B011Point <- B011Point + theme(panel.background = element_rect(fill = "white"),text = element_text(size=20))
  #theme_classic(base_size = 25)
  theme(panel.background = element_rect(fill = "white", colour = "black", size =1, linetype = "blank"),
        axis.line = element_line(color="black", size = 1),
        text = element_text(size=35,color = "black"),
        axis.ticks = element_line(color = "black", size = 1),
        axis.text = element_text(color = "black", size =30),
        axis.ticks.length = unit(0.3,"cm"),
        legend.position = "top")+
  coord_cartesian(ylim=c(50,130)) +
  scale_x_continuous(labels=c("left","right"), 
                     breaks=c(1,2), limits=c(0.5,2.5), expand=c(0,0))
B011Point
ggsave("B011_maxGA.pdf", width = 28, height = 20, units = "cm")
########


########## time to max Grip Aperture ######
agg_data_abstmaxGA <- with(data, aggregate(abs.tmaxGA, list(Subj, Compatibility, hand), mean))
names(agg_data_abstmaxGA) <- c("Subj", "Compatibility", "hand", "MeanabstmaxGA")
summary(agg_data_abstmaxGA)

#rearrange and export for JAPS
library(reshape2)
agg_data_wide_abstmaxGA <- dcast(agg_data_abstmaxGA, Subj ~Compatibility+hand, value.var = "MeanabstmaxGA")
write.csv2(agg_data_wide_abstmaxGA, "agg_data_B011_abstmaxGA.csv")

#calculate condition means + credible intervals
agg_data_abstmaxGA_MCI <- with(agg_data_abstmaxGA, aggregate(MeanabstmaxGA, list(Compatibility, hand), mean)) #conditions means + credible intervals inserted from JASP output
names(agg_data_abstmaxGA_MCI) <- c("Compatibility", "hand", "MeanabstmaxGA")
agg_data_abstmaxGA_MCI$upperCI <- c(397,406,385,421) #these values come from JASP
agg_data_abstmaxGA_MCI$lowerCI <- c(347,355,338,368) #these values come from JASP
summary(agg_data_abstmaxGA_MCI)
 
#time to max GA vel Point plots of aggregated data:
agg_data_abstmaxGA$hand <- as.numeric(agg_data_abstmaxGA$hand)
agg_data_abstmaxGA_MCI$hand <- as.numeric(agg_data_abstmaxGA_MCI$hand)

B011Point <- ggplot(agg_data_abstmaxGA,aes(hand,MeanabstmaxGA, fill = Compatibility)) +
  geom_point(position = position_jitterdodge(jitter.width=0.05, dodge.width = 0.5),
             shape = 21,
             size =5,stroke = 0) + #plot individual data points
  geom_errorbar(data = agg_data_abstmaxGA_MCI,aes(ymin=lowerCI, ymax=upperCI),
                position = position_dodge(width = 0.50),
                width = 0.2, size=2,alpha = 0.5,
                color = c("#8b0000","#6A6A6A","#8b0000","#6A6A6A")) + 
  #B011Point <- B011Point + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(width=0.50), width = 0.2, size =1,color = c("#003070","#007030","#003070","#007030"))
  stat_summary(fun.y = mean, geom = "point",
               shape = 21,
               size = 15,stroke = 0,alpha = 0.5,
               position = position_dodge(width=0.50)) + 
  scale_fill_manual(name = "", values=c("#6A6A6A","#8b0000")) +
  labs(x="Response hand", y = "Time to maximal grip aperture (ms)") +
  #B011Point <- B011Point + theme(panel.background = element_rect(fill = "white"),text = element_text(size=20))
  #theme_classic(base_size = 25)
  theme(panel.background = element_rect(fill = "white", colour = "black", size =1, linetype = "blank"),
        axis.line = element_line(color="black", size = 1),
        text = element_text(size=35,color = "black"),
        axis.ticks = element_line(color = "black", size = 1),
        axis.text = element_text(color = "black", size =30),
        axis.ticks.length = unit(0.3,"cm"),
        legend.position = "top")+
  coord_cartesian(ylim=c(200,600)) +
  scale_x_continuous(labels=c("left","right"), 
                     breaks=c(1,2), limits=c(0.5,2.5), expand=c(0,0))
B011Point
ggsave("B011_abstmaxGA.pdf", width = 28, height = 20, units = "cm")
########