Week 1, Day 6 of the Data Analytics Internship
Subarna N
February 3, 2018
Deans Dilemma
TASK 2b - Reading the dataset
## Parsed with column specification:
## cols(
## .default = col_integer(),
## Gender = col_character(),
## Percent_SSC = col_double(),
## Board_SSC = col_character(),
## Percent_HSC = col_double(),
## Board_HSC = col_character(),
## Stream_HSC = col_character(),
## Percent_Degree = col_double(),
## Course_Degree = col_character(),
## Entrance_Test = col_character(),
## Percentile_ET = col_double(),
## `S-TEST*SCORE` = col_double(),
## Percent_MBA = col_double(),
## Specialization_MBA = col_character(),
## Placement = col_character()
## )## See spec(...) for full column specifications.The dataset Deans dilemma contains 391 observations of 26 variables
TASK 2c - Summarizing the dataset
## SlNo Gender Gender-B Percent_SSC
## Min. : 1.0 Length:391 Min. :0.0000 Min. :37.00
## 1st Qu.: 98.5 Class :character 1st Qu.:0.0000 1st Qu.:56.00
## Median :196.0 Mode :character Median :0.0000 Median :64.50
## 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_SSC Board_CBSE Board_ICSE Percent_HSC
## Length:391 Min. :0.000 Min. :0.0000 Min. :40.0
## Class :character 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:54.0
## Mode :character Median :0.000 Median :0.0000 Median :63.0
## 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
## Board_HSC Stream_HSC Percent_Degree Course_Degree
## Length:391 Length:391 Min. :35.00 Length:391
## Class :character Class :character 1st Qu.:57.52 Class :character
## Mode :character Mode :character Median :63.00 Mode :character
## Mean :62.98
## 3rd Qu.:69.00
## Max. :89.00
## Degree_Engg Experience_Yrs Entrance_Test S-TEST
## Min. :0.00000 Min. :0.0000 Length:391 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character 1st Qu.:1.0000
## Median :0.00000 Median :0.0000 Mode :character Median :1.0000
## Mean :0.09463 Mean :0.4783 Mean :0.8286
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.00000 Max. :3.0000 Max. :1.0000
## Percentile_ET S-TEST*SCORE Percent_MBA Specialization_MBA
## Min. : 0.00 Min. : 0.00 Min. :50.83 Length:391
## 1st Qu.:41.19 1st Qu.:41.19 1st Qu.:57.20 Class :character
## Median :62.00 Median :62.00 Median :61.01 Mode :character
## 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
## Marks_Communication Marks_Projectwork Marks_BOCA Placement
## Min. :50.00 Min. :50.00 Min. :50.00 Length:391
## 1st Qu.:53.00 1st Qu.:64.00 1st Qu.:57.00 Class :character
## Median :58.00 Median :69.00 Median :63.00 Mode :character
## Mean :60.54 Mean :68.36 Mean :64.38
## 3rd Qu.:67.00 3rd Qu.:74.00 3rd Qu.:72.50
## Max. :88.00 Max. :87.00 Max. :96.00
## Placement_B Salary
## Min. :0.000 Min. : 0
## 1st Qu.:1.000 1st Qu.:172800
## Median :1.000 Median :240000
## Mean :0.798 Mean :219078
## 3rd Qu.:1.000 3rd Qu.:300000
## Max. :1.000 Max. :940000TASK 3a- The median salary of all the students in the data sample
## vars n mean sd median trimmed mad min max range skew
## X1 1 391 219078.3 138311.6 240000 217011.5 88956 0 940000 940000 0.24
## kurtosis se
## X1 1.74 6994.72The median salary of all the studentsin data sample = 240000
TASK 3b- The percentage of students who were placed.
## Placement
## Not Placed Placed
## 79 312## Placement
## Not Placed Placed
## 0.202046 0.79795479% of students were placed
TASK 3c- creating a dataframe called placed
New data for the students who was placed was created 312 observation of 26 variables #TASK 3d- The median salary of students who were placed.
## vars n mean sd median trimmed mad min max range skew
## X1 1 312 274550 93331.63 260000 263980 59304 120000 940000 820000 2.26
## kurtosis se
## X1 10.34 5283.86The median salary of the students who were placed=260000 #Use R to create a table showing the mean salary of males and females, who were placed.
## Gender x
## 1 F 193288.2
## 2 M 231484.8TASK 3f- histogram showing a breakup of the MBA performance of the students who were placed
TASK 3c- creating a dataframe called not placed
New data for the students who was not placed was created 79 observation of 26 variables
TASK 3h- comparing the MBA performance of Placed and Not Placed students:
TASK 3i-comparing the distribution of salaries of males and females who were placed,using boxer plot:
TASK 3j-Creating a dataframe called placedET, representing students who were placed after the MBA and who also gave some MBA entrance test before admission into the MBA program.
New data for the students who was placed and also cleared entrance was created 261 observation of 26 variables
TASK 3k- Scatter Plot Matrix for 3 variables – {Salary, Percent_MBA, Percentile_ET} using the dataframe placedET.
Warning in install.packages : package ‘scatterplotmatrix’ is not available (for R version 3.4.3)