Sample Programs Using Data Frames in R

The following programs illustrate different applications and operations on data frames suitable for undergraduate laboratories.

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1. Student Result Analysis

Aim

Create a data frame containing details of students and perform basic analysis.

Program

students <- data.frame(
  Name = c("John", "Mary", "Alex", "David", "Sara"),
  Marks = c(85, 92, 45, 78, 95)
)

print(students)

# Passed students
passed <- students[students$Marks >= 50, ]

# Average marks
avg <- mean(students$Marks)

# Topper
topper <- students[students$Marks == max(students$Marks), ]

cat("Average Marks =", avg, "\n")

cat("Topper:\n")
print(topper)

cat("Passed Students:\n")
print(passed)

Concepts Demonstrated

• Data frame creation
• Logical indexing
• Statistical functions

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2. Employee Salary Management

Aim

Calculate annual salaries and identify the highest-paid employee.

Program

employees <- data.frame(
  Name = c("John", "Mary", "Alex", "David"),
  MonthlySalary = c(40000, 55000, 45000, 60000)
)

employees$AnnualSalary <- employees$MonthlySalary * 12

print(employees)

highest <- employees[employees$AnnualSalary ==
                       max(employees$AnnualSalary), ]

cat("Highest Paid Employee:\n")
print(highest)

Concepts Demonstrated

• Adding columns
• Arithmetic operations
• Filtering rows

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3. Cricket Team Statistics

Aim

Analyze runs scored by players.

Program

team <- data.frame(
  Player = c("Rohit", "Gill", "Virat", "Rahul", "Hardik"),
  Runs = c(75, 42, 110, 65, 35)
)

print(team)

# Average score
avg_runs <- mean(team$Runs)

# Players scoring more than 50
good_players <- team[team$Runs > 50, ]

# Highest scorer
highest <- team[team$Runs == max(team$Runs), ]

cat("Average Runs =", avg_runs, "\n")

print(good_players)

print(highest)

Concepts Demonstrated

• Selection using conditions
• Statistical analysis
• Maximum values

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4. Library Management System

Aim

Maintain details of books and display available books.

Program

books <- data.frame(
  Title = c("DBMS", "Python", "Machine Learning", "OS"),
  Price = c(450, 500, 700, 550),
  Available = c(TRUE, FALSE, TRUE, TRUE)
)

print(books)

# Available books
available_books <- books[books$Available == TRUE, ]

# Most expensive book
costly_book <- books[books$Price == max(books$Price), ]

cat("Available Books:\n")
print(available_books)

cat("Most Expensive Book:\n")
print(costly_book)

Concepts Demonstrated

• Boolean columns
• Logical indexing
• Maximum value

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5. Sales Data Analysis

Aim

Analyze monthly sales of products.

Program

sales <- data.frame(
  Product = c("Laptop", "Mobile", "TV", "Printer", "Tablet"),
  Sales = c(120000, 85000, 150000, 45000, 90000)
)

print(sales)

# Total sales
total_sales <- sum(sales$Sales)

# Average sales
avg_sales <- mean(sales$Sales)

# Products with sales above average
high_sales <- sales[sales$Sales > avg_sales, ]

# Sort in descending order
sorted_sales <- sales[order(sales$Sales,
                            decreasing = TRUE), ]

cat("Total Sales =", total_sales, "\n")
cat("Average Sales =", avg_sales, "\n")

cat("Products Above Average Sales:\n")
print(high_sales)

cat("Products Sorted by Sales:\n")
print(sorted_sales)

Concepts Demonstrated

• sum(), mean()
• Conditional filtering
• Sorting using order()

Sample Program: Using Factors in a Data Frame

Aim

Create a data frame containing student details and use factors to represent categorical variables such as Gender and Grade. Perform some basic analysis on the data.

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Program

# Create a data frame
students <- data.frame(
  RollNo = c(101, 102, 103, 104, 105),
  Name = c("John", "Mary", "Alex", "David", "Sara"),
  Gender = factor(c("Male", "Female", "Male", "Male", "Female")),
  Marks = c(85, 92, 78, 65, 95),
  Grade = factor(c("A", "A", "B", "C", "A"),
                 levels = c("C", "B", "A"),
                 ordered = TRUE)
)

# Display the data frame
print(students)

# Structure of the data frame
str(students)

# Frequency of Gender
cat("Gender Distribution:\n")
print(table(students$Gender))

# Frequency of Grades
cat("Grade Distribution:\n")
print(table(students$Grade))

# Students having Grade A
cat("Students with Grade A:\n")
print(students[students$Grade == "A", ])

# Average marks of male students
male_avg <- mean(students$Marks[students$Gender == "Male"])

cat("Average Marks of Male Students =", male_avg, "\n")

# Students with grade greater than B
cat("Students with Grade > B:\n")
print(students[students$Grade > "B", ])

---

Output

  RollNo  Name Gender Marks Grade
1    101  John   Male    85     A
2    102  Mary Female    92     A
3    103  Alex   Male    78     B
4    104 David   Male    65     C
5    105  Sara Female    95     A

Gender Distribution

Female Male
     2    3

Grade Distribution

C B A
1 1 3

Students with Grade A

  RollNo Name Gender Marks Grade
1    101 John   Male    85     A
2    102 Mary Female    92     A
5    105 Sara Female    95     A

Average Marks of Male Students

Average Marks of Male Students = 76

Students with Grade > B

  RollNo Name Gender Marks Grade
1    101 John   Male    85     A
2    102 Mary Female    92     A
5    105 Sara Female    95     A