Programs to Try using DataFrames in R - Assignment 9

 

1.Problem Statement

Consider the academic details of 12 students in a class. The dataset contains:

• Roll Number
• Student Name
• Gender
• Department
• Attendance Percentage
• Internal Mark
• Grade

Create a data frame using the following data and perform the specified operations.

Student Data

Roll No.	Name	Gender	Department	Attendance	Internal Mark	Grade
101	Arun	Male	CSE	92	85	A
102	Anu	Female	ECE	88	78	B
103	Rahul	Male	CSE	75	65	C
104	Meera	Female	EEE	95	91	A
105	Vishnu	Male	ECE	82	72	B
106	Priya	Female	CSE	90	88	A
107	Akhil	Male	EEE	68	55	C
108	Neha	Female	ECE	85	76	B
109	Kiran	Male	CSE	78	69	C
110	Divya	Female	EEE	94	89	A
111	Sandeep	Male	ECE	72	61	C
112	Lakshmi	Female	CSE	87	81	A

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Tasks

Part A – Creating and Exploring the Data Frame

1. Create the data frame students.
2. Display the complete data frame.
3. Display the first five records.
4. Display the last five records.
5. Display the structure of the data frame using str().
6. Display summary information using summary().
7. Find the number of rows and columns.
8. Display the column names.
9. Access the Attendance column.
10. Access the Internal Mark column.

Part B – Understanding Factors

Convert the following variables into factors:

• Gender
• Department
• Grade

For example:

students$Gender <- factor(students$Gender)

Then:

11. Display the structure of the data frame again.
12. Display the levels of Gender.
13. Display the levels of Department.
14. Display the levels of Grade.
15. Find the number of students in each department.
16. Find the number of male and female students.
17. Find the number of students in each grade.

Useful functions:

levels()
table()

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Part C – Data Selection and Filtering

Perform the following operations:

18. Display students whose attendance is greater than 85%.
19. Display students whose internal mark is greater than 75.
20. Display students who obtained grade A.
21. Display students belonging to the CSE department.
22. Display female students from the ECE department.
23. Display students whose attendance is below 75%.

For example:

students[students$Attendance > 85, ]

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Part D – Sorting

24. Sort the students according to their internal marks in ascending order.
25. Sort the students according to their internal marks in descending order.
26. Identify the student who obtained the highest internal mark.
27. Identify the student with the lowest attendance.

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Part E – Statistical Analysis

Calculate:

28. Average attendance of all students.
29. Average internal mark of all students.
30. Maximum internal mark.
31. Minimum internal mark.
32. Average internal mark of male students.
33. Average internal mark of female students.
34. Average internal mark for each department.
35. Average attendance for each department.

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Part F – Group-Based Analysis

This part is particularly useful for understanding factors.

Find:

36. Number of students in each department.
37. Average internal mark for each department.
38. Highest internal mark in each department.
39. Number of students in each grade.
40. Average internal mark for each grade.

Students can use:

aggregate()

For example:

aggregate(InternalMark ~ Department,
          data = students,
          FUN = mean)

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Part G – Creating a New Variable

Create a new column called Performance.

Assign:

• "Excellent" if Internal Mark ≥ 80
• "Good" if Internal Mark is between 70 and 79
• "Average" if Internal Mark is below 70

Then:

41. Display the updated data frame.
42. Convert Performance into a factor.
43. Find the number of students in each performance category.

2.Create a data frame containing employee details. Use factors to represent categorical variables such as Department and Performance Rating, and perform various analyses.

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# Create the data frame

employees <- data.frame(
  EmpID = c(101, 102, 103, 104, 105),
  Name = c("John", "Mary", "Alex", "David", "Sara"),
  Department = factor(c("HR", "IT", "Finance", "IT", "HR")),
  Salary = c(50000, 70000, 60000, 80000, 55000),
  Rating = factor(
    c("Good", "Excellent", "Average", "Excellent", "Good"),
    levels = c("Average", "Good", "Excellent"),
    ordered = TRUE
  )
)
EmpID  Name Department Salary    Rating
1   101  John         HR  50000      Good
2   102  Mary         IT  70000 Excellent
3   103  Alex    Finance  60000   Average
4   104 David         IT  80000 Excellent
5   105  Sara         HR  55000      Good



Find
1 Department-wise Employee Count
2 Average Salary by Department
3 Employees with Excellent Rating
4 Employees with Rating > Good
5 Highest Paid Employee