#import file nif<-read.csv(“E:/R/NIFTY_analysis/Prc_PrevOPCvsHighLow.csv”)

#display column names names(nif)

#adding column with Quintile values using mutate() nif<-nif %>% mutate(QuintOPC=ntile(Perc_OPC,5))

#jittered plot with x data clustered in quintiles against y plot<- nif %>% ggplot(aes(x=as.factor(QuintOPC), y= Prc_HL))+geom_point(alpha=0.5, position =“jitter”)

#categorizing through colors for 3rd dimension plot<- nif %>% ggplot(aes(x=as.factor(QuintOPC), y= Prc_HL, colour=DirOPC))+geom_point(alpha=0.5, position =“jitter”)

#adding x and y labels plot<- nif %>% ggplot(aes(x=as.factor(QuintOPC), y= Prc_HL, colour=DirOPC))+geom_point(alpha=0.5, position =“jitter”)+xlab(“% Difference bw today’s Open and yesterday’s close(in Quintiles)”)+ylab(“Percentage change Low to High Same day”)

#adding title and subtitle plot<- nif %>% ggplot(aes(x=as.factor(QuintOPC), y= Prc_HL, colour=DirOPC))+geom_point(alpha=0.5, position =“jitter”)+xlab(“% Difference bw today’s Open and yesterday’s close(in Quintiles)”)+ylab(“Percentage change Low to High Same day”)+ggtitle(“Intraday Variation in NIFTY based on Gap up and Gap down values”, subtitle=“Derived from NIFTY data from April 2010 to April 2025”)

#view plot print(plot)