Reading and Viewing the CSV File
setwd("C:/Users/RRD/Downloads")
Ddillema.df<-read.csv("Data - Deans Dilemma.csv")
View(Ddillema.df)
Summarisy of Data Set
summary(Ddillema.df)
## SlNo Gender Gender.B Percent_SSC Board_SSC
## Min. : 1.0 F:127 Min. :0.0000 Min. :37.00 CBSE :113
## 1st Qu.: 98.5 M:264 1st Qu.:0.0000 1st Qu.:56.00 ICSE : 77
## Median :196.0 Median :0.0000 Median :64.50 Others:201
## Mean :196.0 Mean :0.3248 Mean :64.65
## 3rd Qu.:293.5 3rd Qu.:1.0000 3rd Qu.:74.00
## Max. :391.0 Max. :1.0000 Max. :87.20
##
## Board_CBSE Board_ICSE Percent_HSC Board_HSC
## Min. :0.000 Min. :0.0000 Min. :40.0 CBSE : 96
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:54.0 ISC : 48
## Median :0.000 Median :0.0000 Median :63.0 Others:247
## Mean :0.289 Mean :0.1969 Mean :63.8
## 3rd Qu.:1.000 3rd Qu.:0.0000 3rd Qu.:72.0
## Max. :1.000 Max. :1.0000 Max. :94.7
##
## Stream_HSC Percent_Degree Course_Degree
## Arts : 18 Min. :35.00 Arts : 13
## Commerce:222 1st Qu.:57.52 Commerce :117
## Science :151 Median :63.00 Computer Applications: 32
## Mean :62.98 Engineering : 37
## 3rd Qu.:69.00 Management :163
## Max. :89.00 Others : 5
## Science : 24
## Degree_Engg Experience_Yrs Entrance_Test S.TEST
## Min. :0.00000 Min. :0.0000 MAT :265 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 None : 67 1st Qu.:1.0000
## Median :0.00000 Median :0.0000 K-MAT : 24 Median :1.0000
## Mean :0.09463 Mean :0.4783 CAT : 22 Mean :0.8286
## 3rd Qu.:0.00000 3rd Qu.:1.0000 PGCET : 8 3rd Qu.:1.0000
## Max. :1.00000 Max. :3.0000 GCET : 2 Max. :1.0000
## (Other): 3
## Percentile_ET S.TEST.SCORE Percent_MBA
## Min. : 0.00 Min. : 0.00 Min. :50.83
## 1st Qu.:41.19 1st Qu.:41.19 1st Qu.:57.20
## Median :62.00 Median :62.00 Median :61.01
## Mean :54.93 Mean :54.93 Mean :61.67
## 3rd Qu.:78.00 3rd Qu.:78.00 3rd Qu.:66.02
## Max. :98.69 Max. :98.69 Max. :77.89
##
## Specialization_MBA Marks_Communication Marks_Projectwork
## Marketing & Finance:222 Min. :50.00 Min. :50.00
## Marketing & HR :156 1st Qu.:53.00 1st Qu.:64.00
## Marketing & IB : 13 Median :58.00 Median :69.00
## Mean :60.54 Mean :68.36
## 3rd Qu.:67.00 3rd Qu.:74.00
## Max. :88.00 Max. :87.00
##
## Marks_BOCA Placement Placement_B Salary
## Min. :50.00 Not Placed: 79 Min. :0.000 Min. : 0
## 1st Qu.:57.00 Placed :312 1st Qu.:1.000 1st Qu.:172800
## Median :63.00 Median :1.000 Median :240000
## Mean :64.38 Mean :0.798 Mean :219078
## 3rd Qu.:72.50 3rd Qu.:1.000 3rd Qu.:300000
## Max. :96.00 Max. :1.000 Max. :940000
##
Find the percentage of students who are placed
prop.table(table(Ddillema.df$Placement))*100
##
## Not Placed Placed
## 20.2046 79.7954
Creating dataframe of placed students
placed.df<-Ddillema.df[which(Ddillema.df$Placement_B==1),]
Viewing dataframe of placed students
View(placed.df)
Finding mean salary of male and female students who were placed
by(placed.df$Salary, placed.df$Gender, mean)
## placed.df$Gender: F
## [1] 253068
## --------------------------------------------------------
## placed.df$Gender: M
## [1] 284241.9
Displaying placed and not placed together.
layout(matrix(c(1, 1, 2, 2), 1, 4, byrow = TRUE), widths = c(1,1))
hist(placed.df$Percent_MBA, right=FALSE, xlim = c(50, 80), breaks = 3, main="MBA Performance of placed students", xlab="MBA percentage", ylab="count",col="red")
hist(notplaced.df$Percent_MBA, right=FALSE, xlim = c(50, 80), breaks = 3, main="MBA Performance of not placed students", xlab="MBA percentage", ylab="count",col="red")
Creating boxplots to compare the distribution of salaries for male and female students who were placed
boxplot(placed.df$Salary ~ placed.df$Gender, horizontal=TRUE, ylab = "Gender", xlab = "Salary", las=1, main="Comparison of Salaries of Males and Females")
Generating the scatterplot
placedET.df<-placed.df[which(placed.df$S.TEST==1),]
library("car")
## Warning: package 'car' was built under R version 3.3.3
scatterplotMatrix(formula = ~ Salary+Percent_MBA+Percentile_ET, cex=0.6, data=placedET.df, diagonal="density")