library(car) 
library(ggplot2) 
library(pastecs) 
 
install.packages("car") 
 
datscan <- read.csv("C:/Users/mehdi/Desktop/Projects/PPMI dataset/DaTScan_Analysis.csv") 
 
datscan <- subset(datscan, EVENT_ID == "SC") 
 
datscan <- datscan[ , c(2,6)] 
 
norm <- rnorm(100) 
 
norm <- as.data.frame(norm) 
 
plot <- ggplot(datscan, aes(datscan$DATSCAN_CAUDATE_R)) + 
  geom_histogram(aes(y = ..density..), color = "red", fill = "white") + 
  stat_function(fun = dnorm , args = list(mean = mean(datscan$DATSCAN_CAUDATE_R, na.rm = TRUE), 
                                          sd = sd(datscan$DATSCAN_CAUDATE_R, na.rm = TRUE))) 
 
plot2 <- qplot(sample = datscan$DATSCAN_CAUDATE_R) 
 
plot2 
 
plot 
 
stat <- stat.desc(datscan$DATSCAN_CAUDATE_R, basic =FALSE, norm = TRUE) 
stat 
 
test <- shapiro.test(datscan$DATSCAN_CAUDATE_R) 
test 
 
 
demo <- read.csv("C:/Users/mehdi/Desktop/Projects/Reyhaneh/status.csv") 
demo <- demo[ , c(1,2)] 
 
demo <- subset(demo, COHORT != "3") 
 
final <- merge(datscan, demo , by = "PATNO") 
 
class(final$COHORT) 
 
final$COHORT <- as.factor(final$COHORT) 
 
lt <- leveneTest(final$DATSCAN_CAUDATE_R, final$COHORT, center = median) 
lt 
summary(lt)