R Markdown

ctv.df <- read.csv(paste("Data - Deans Dilemma.csv", sep=""))
View(ctv.df)
summary(ctv.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  
##

Including Plots

You can also embed plots, for example:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0  172800  240000  219078  300000  940000

It shows that 79.79% of the students were placed.

mytable<-with(ctv.df,table(Placement))
mytable
## Placement
## Not Placed     Placed 
##         79        312
prop.table(mytable)
## Placement
## Not Placed     Placed 
##   0.202046   0.797954

It shows that the median of placed students is 260000

newdata<-ctv.df[which(ctv.df$Placement=="Placed"),]
summary(newdata$Salary)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  120000  220000  260000  274550  300000  940000

The mean salary of women was 253068 and the mean salary of men was 284241.9

aggregate(newdata$Salary, by=list(gender=newdata$Gender), mean)
##   gender        x
## 1      F 253068.0
## 2      M 284241.9
hist(ctv.df$Percent_MBA, breaks=3, col="gray")

newdata2<-ctv.df[which(ctv.df$Placement!="Placed"),]
boxplot(newdata$Salary~newdata$Gender, horizontal=TRUE, xlab="Salaries", ylab="Gender")

library(car)
placedET<-ctv.df[which(ctv.df$Placement=="Placed"&ctv.df$Entrance_Test!="None"),]
scatterplot.matrix(formula = ~ Salary + Percent_MBA + Percentile_ET, data=placedET, diagonal="histogram")
## Warning: 'scatterplot.matrix' is deprecated.
## Use 'scatterplotMatrix' instead.
## See help("Deprecated") and help("car-deprecated").

```

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