Calculating Quartiles and Plotting PDFs

Computing Quartiles of a sample in R

rr <- read.csv("C://Users//MAHE//Desktop//Quartile.csv",header=TRUE)
quantile(rr$Final.Grade)

##   0%  25%  50%  75% 100% 
##   13   72   88   92  100

To know which quartile each point belongs to

q <- quantile(rr$Final.Grade)
rr$q <- cut(rr$Final.Grade, q)
levels(rr$q) <- c("q1","q2","q3","q4")
rr

##    Final.Grade    q
## 1           91   q3
## 2           89   q3
## 3           67   q1
## 4           73   q2
## 5           92   q3
## 6           84   q2
## 7           94   q4
## 8          100   q4
## 9           74   q2
## 10          52   q1
## 11          59   q1
## 12          40   q1
## 13          91   q3
## 14          13 <NA>
## 15          64   q1
## 16          85   q2
## 17          74   q2
## 18          39   q1
## 19          70   q1
## 20          70   q1
## 21          72   q1
## 22          71   q1
## 23          88   q2
## 24          95   q4
## 25          84   q2
## 26          97   q4
## 27          94   q4
## 28          93   q4
## 29          47   q1
## 30          88   q2
## 31          93   q4
## 32          89   q3
## 33          98   q4
## 34          82   q2
## 35          84   q2
## 36          88   q2
## 37          94   q4
## 38          70   q1
## 39          82   q2
## 40          98   q4
## 41          86   q2
## 42          93   q4
## 43          82   q2
## 44          72   q1
## 45          88   q2
## 46          88   q2
## 47          77   q2
## 48          88   q2
## 49          92   q3
## 50          95   q4
## 51          91   q3
## 52          96   q4
## 53          88   q2
## 54          72   q1
## 55          90   q3
## 56          88   q2
## 57          88   q2

Plotting PDF of a sample

library(ggplot2)
ggplot(rr, 
       aes(x=rr$Final.Grade, fill = "green")) + 
  geom_density(alpha=.3)

x <- rr$Final.Grade
h<-hist(rr$Final.Grade, breaks=10, col="red", xlab="Miles Per Gallon",
        main="Histogram for marks")
xfit<-seq(min(x),max(x),length=40)
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x))
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2)

d <- density(rr$Final.Grade)
plot(d, main="Density of marks")
polygon(d, col="red", border="blue")