102/replication_package/step6/step6.2/Step6.2_Analyses.Rhistory

dat <- read.table("Step6.2_Total.dat",header=TRUE)

##Participants

length(levels(as.factor(dat$SUBJECT)))
table(dat$GENDER)/48
mean(dat$AGE)
sd(dat$AGE)

	##Exclusion of mistranslated scenario

dat <- dat[dat$SCENARIO!="P_08",]

	##Exclusion

COND <- as.factor(dat$CONDITION1_PRESSURE)
levels(COND) <- c("SLOW","FAST")

tab <- table(dat$SUBJECT[COND=="FAST"],dat$BINARY[COND=="FAST"],dat$CATEGORY[COND=="FAST"])
(tab[,1,1]+tab[,2,1])<3
(tab[,1,2]+tab[,2,2])<3
(tab[,1,3]+tab[,2,3])<3
(tab[,1,4]+tab[,2,4])<3
(tab[,1,5]+tab[,2,5])<3
(tab[,1,6]+tab[,2,6])<3

dat <- dat[dat$SUBJECT!=64,]

##Average time constraint in the Fast condition

COND <- as.factor(dat$CONDITION1_PRESSURE)
levels(COND) <- c("SLOW","FAST")

mean(dat$BINARY_LIMIT[COND=="FAST"],na.rm=TRUE)
sd(dat$BINARY_LIMIT[COND=="FAST"],na.rm=TRUE)

##Computation of utilitarian scores

raw_score <- as.numeric(as.factor(dat$BINARY))-1
dat2 <- aggregate(raw_score,list(SUBJECT=dat$SUBJECT,CATEGORY=dat$CATEGORY,COND=COND),mean,na.rm=TRUE)

score <- dat2$x
score[dat2$CATEGORY=="UD"] <- 1-score[dat2$CATEGORY=="UD"]
score[dat2$CATEGORY=="PD"] <- 1-score[dat2$CATEGORY=="PD"]
score[dat2$CATEGORY=="HC"] <- 1-score[dat2$CATEGORY=="HC"]
score[dat2$CATEGORY=="AO"] <- 1-score[dat2$CATEGORY=="AO"]
score[dat2$CATEGORY=="P"] <- 1-score[dat2$CATEGORY=="P"]

##Mean and SD for Utilitarian Scores

mean(score[dat2$CATEGORY=="UD" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="UD" & dat2$COND=="FAST"],na.rm=TRUE)
mean(score[dat2$CATEGORY=="UD" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="UD" & dat2$COND=="SLOW"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="PD" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="PD" & dat2$COND=="FAST"],na.rm=TRUE)
mean(score[dat2$CATEGORY=="PD" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="PD" & dat2$COND=="SLOW"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)
mean(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)
mean(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)
mean(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)
mean(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)

##Correlations between scores

cor.test(score[dat2$CATEGORY=="UD" & dat2$COND=="FAST"],score[dat2$CATEGORY=="UD" & dat2$COND=="SLOW"])
cor.test(score[dat2$CATEGORY=="PD" & dat2$COND=="FAST"],score[dat2$CATEGORY=="PD" & dat2$COND=="SLOW"])
cor.test(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"])
cor.test(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"])
cor.test(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"])
cor.test(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"])

##Comparison between conditions

CATEGORY <- dat2$CATEGORY
CONDITION <- dat2$COND
dat3 <- data.frame(score,CATEGORY,CONDITION)

library(lsr)

t.test(score[dat3$CATEGORY=="UD" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="UD" & dat3$CONDITION=="SLOW"],paired=TRUE)
datUD <- dat3[dat3$CATEGORY=="UD",]
cohensD(score ~ CONDITION, data = datUD, method = "paired")

t.test(score[dat3$CATEGORY=="PD" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="PD" & dat3$CONDITION=="SLOW"],paired=TRUE)
datPD <- dat3[dat3$CATEGORY=="PD",]
cohensD(score ~ CONDITION, data = datPD, method = "paired")

t.test(score[dat3$CATEGORY=="HC" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="HC" & dat3$CONDITION=="SLOW"],paired=TRUE)
datHC <- dat3[dat3$CATEGORY=="HC",]
cohensD(score ~ CONDITION, data = datHC, method = "paired")

t.test(score[dat3$CATEGORY=="AO" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="AO" & dat3$CONDITION=="SLOW"],paired=TRUE)
datAO <- dat3[dat3$CATEGORY=="AO",]
cohensD(score ~ CONDITION, data = datAO, method = "paired")

t.test(score[dat3$CATEGORY=="DE" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="DE" & dat3$CONDITION=="SLOW"],paired=TRUE)
datDE <- dat3[dat3$CATEGORY=="DE",]
cohensD(score ~ CONDITION, data = datDE, method = "paired")

t.test(score[dat3$CATEGORY=="P" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="P" & dat3$CONDITION=="SLOW"],paired=TRUE)
datP <- dat3[dat3$CATEGORY=="P",]
cohensD(score ~ CONDITION, data = datP, method = "paired")

##Figures

cond_class <- ordered(dat3$CONDITION, levels = c("FAST", "SLOW"))
type_class <- ordered(dat3$CATEGORY, levels = c("UD", "PD", "HC","AO","DE","P"))
dat4 <- data.frame(score,type_class,cond_class)

library(ggplot2)
ggplot(dat3, aes(type_class, score, fill=factor(cond_class))) +
  geom_boxplot()+
  scale_y_continuous(breaks = seq(0, 1, by = 0.1))+
  ggtitle("Study 2 (Time constraint) - Utilitarian scores per type of scenarios and condition")+
  xlab("Type of scenarios")+
  ylab("Utilitarian scores")+
  scale_fill_discrete(name = "Condition", labels = c("Fast", "Slow"))+
  theme(axis.title=element_text(size=16,face="bold"), axis.text=element_text(size=14),legend.title=element_text(size=16,face="bold"),
  legend.text=element_text(size=14), plot.title=element_text(size=20,face="bold",hjust=0.5))+
  annotate(geom="text", x=1, y=1.1, label="d=-0.09, p=.33", color="black",size=5)+
  annotate(geom="text", x=2, y=1.1, label="d=-0.16, p=.08", color="black",size=5)+
  annotate(geom="text", x=3, y=1.1, label="d=0.60, p<.001***", color="black",size=5)+
  annotate(geom="text", x=4, y=1.1, label="d=-0.15, p=.10", color="black",size=5)+
  annotate(geom="text", x=5, y=1.1, label="d=0.26, p=.005**", color="black",size=5)+
  annotate(geom="text", x=6, y=1.1, label="d=0.03, p=.75", color="black",size=5)

ggsave('Step6.2.tiff', width = 15, height = 10,dpi=600, compression = "lzw")

##Preparation of data for mini-meta

library(effsize)

	#D

mean(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="FAST"],na.rm=TRUE)

mean(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="SLOW"],na.rm=TRUE)

cohen.d((datUD$score+datPD$score) ~ datUD$CONDITION)

	#HC

mean(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)

cohen.d(datHC$score ~ datHC$CONDITION)

	#AO

mean(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)

cohen.d(datAO$score ~ datAO$CONDITION)

	#DE

mean(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)

cohen.d(datDE$score ~ datDE$CONDITION)

	#P

mean(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)

mean(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)
sd(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)

cohen.d(datP$score ~ datP$CONDITION)