PART A: Create a data frame with 6 students and the marks scored by them in 5 different courses. Implement the following:
# Create the data frame with student names and marks
students=data.frame(
name = c("Jai", "Hema", "Anu", "Harini", "Shara", "Kanss"),
course1 = c(85, 90, 78, 92, 0, 88),
course2 = c(0, 85, 80, 87, 90, 95),
course3 = c(90, 92, 0, 85, 88, 90),
course4 = c(85, 0, 88, 92, 86, 84),
course5 = c(88, 85, 90, 0, 92, 87)
)
print(students)## name course1 course2 course3 course4 course5
## 1 Jai 85 0 90 85 88
## 2 Hema 90 85 92 0 85
## 3 Anu 78 80 0 88 90
## 4 Harini 92 87 85 92 0
## 5 Shara 0 90 88 86 92
## 6 Kanss 88 95 90 84 87# Calculate the total score for each student and append it as a new column
students$total_score=rowSums(students[, 2:6])
print(students)## name course1 course2 course3 course4 course5 total_score
## 1 Jai 85 0 90 85 88 348
## 2 Hema 90 85 92 0 85 352
## 3 Anu 78 80 0 88 90 336
## 4 Harini 92 87 85 92 0 356
## 5 Shara 0 90 88 86 92 356
## 6 Kanss 88 95 90 84 87 444# Write the data frame to a CSV file without row numbers
write.csv(students, file = "StudMarks.csv", row.names = FALSE)# Read the content of "StudMarks.csv" in a new data frame and view it
students_df=read.csv("StudMarks.csv")
View(students_df)# Access the scores of students in course3 using the column name
course3_scores=students_df$course3
print(course3_scores)## [1] 90 92 0 85 88 90# Extract the score of the third student in course4
third_student_course4=students_df[3, "course4"]
print(third_student_course4)## [1] 88# Extract the scores of the first and second student in all the courses
first_second_students_scores=students_df[1:2, 2:6]
print(first_second_students_scores)## course1 course2 course3 course4 course5
## 1 85 0 90 85 88
## 2 90 85 92 0 85# Display the names and total scores of all students
names_total_scores=students_df[, c("name", "total_score")]
print(names_total_scores)## name total_score
## 1 Jai 348
## 2 Hema 352
## 3 Anu 336
## 4 Harini 356
## 5 Shara 356
## 6 Kanss 444# Make the column “name” as the row index of the data frame
rownames(students_df)=students_df$name
students_df=students_df[, -1]# Display the names of the students those who were present for Course4
present_course4= rownames(students_df[students_df$course4 > 0, ])
print(present_course4)## [1] "Jai" "Anu" "Harini" "Shara" "Kanss"# Obtain the names whose total score is above its average
average_total_score=mean(students_df$total_score)
above_average_students= rownames(students_df[students_df$total_score > average_total_score, ])
print(above_average_students)## [1] "Kanss"PART B:
# Load the mtcars dataset
data(mtcars)
# Make a copy of mtcars in a new variable
mtcars_copy <- mtcars# Display the structure of mtcars
str(mtcars_copy)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...# Display the dimension of mtcars
dim(mtcars_copy)## [1] 32 11# Find the car model with the highest horse power
max_hp=max(mtcars_copy$hp)
car_max_hp=rownames(mtcars_copy[mtcars_copy$hp == max_hp, ])
print(paste("Car with the highest horsepower:", car_max_hp))## [1] "Car with the highest horsepower: Maserati Bora"# Find the car model with the lowest horse power
min_hp=min(mtcars_copy$hp)
car_min_hp=rownames(mtcars_copy[mtcars_copy$hp == min_hp, ])
print(paste("Car with the lowest horsepower:", car_min_hp))## [1] "Car with the lowest horsepower: Honda Civic"# Display the names of all the automobile models listed in mtcars
car_models=rownames(mtcars_copy)
print(car_models)## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"# Calculate the average horse power
average_hp=mean(mtcars_copy$hp)
# Identify the car models whose horse power is greater than the average horse power
cars_above_avg_hp=rownames(mtcars_copy[mtcars_copy$hp > average_hp, ])
print("Car models with horse power greater than the average:")## [1] "Car models with horse power greater than the average:"print(cars_above_avg_hp)## [1] "Hornet Sportabout" "Duster 360" "Merc 450SE"
## [4] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [7] "Lincoln Continental" "Chrysler Imperial" "Dodge Challenger"
## [10] "AMC Javelin" "Camaro Z28" "Pontiac Firebird"
## [13] "Ford Pantera L" "Ferrari Dino" "Maserati Bora"# Identify the number of cars with automatic transmission
num_automatic_cars=sum(mtcars_copy$am == 0)
# Display the names of cars with automatic transmission
automatic_cars=rownames(mtcars_copy[mtcars_copy$am == 0, ])
print(paste("Number of cars with automatic transmission:", num_automatic_cars))## [1] "Number of cars with automatic transmission: 19"print("Names of cars with automatic transmission:")## [1] "Names of cars with automatic transmission:"print(automatic_cars)## [1] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [4] "Duster 360" "Merc 240D" "Merc 230"
## [7] "Merc 280" "Merc 280C" "Merc 450SE"
## [10] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [13] "Lincoln Continental" "Chrysler Imperial" "Toyota Corona"
## [16] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [19] "Pontiac Firebird"PART C:
# Store your date of birth in a variable dt
dt = "2003-07-01"# Convert the type of dt to Date() and store it in a variable
dt_date=as.Date(dt)
# Print its type
print(paste("Type of dt_date:", class(dt_date)))## [1] "Type of dt_date: Date"# Convert the type of dt to POSIXct() and store it in a variable
dt_posixct=as.POSIXct(dt)
# Print its type
print(paste("Type of dt_posixct:", class(dt_posixct)))## [1] "Type of dt_posixct: POSIXct" "Type of dt_posixct: POSIXt"# Print the weekday, month and quarter of your DoB
weekday = weekdays(dt_date)
month = months(dt_date)
quarter = quarters(dt_date)
print(paste("Weekday:", weekday))## [1] "Weekday: Tuesday"print(paste("Month:", month))## [1] "Month: July"print(paste("Quarter:", quarter))## [1] "Quarter: Q3"# Generate and display 5 consecutive days from your DoB
consecutive_days =dt_date + 0:4
print("5 consecutive days from DoB:")## [1] "5 consecutive days from DoB:"print(consecutive_days)## [1] "2003-07-01" "2003-07-02" "2003-07-03" "2003-07-04" "2003-07-05"# Print today’s date and time
today= Sys.time()
print(paste("Today's date and time:", today))## [1] "Today's date and time: 2024-08-04 18:30:19.381691"# Generate and display 5 new dates from your DoB with a distance of 3 months
new_dates=seq(dt_date, by = "3 months", length.out = 5)
print("5 new dates with a distance of 3 months from DoB:")## [1] "5 new dates with a distance of 3 months from DoB:"print(new_dates)## [1] "2003-07-01" "2003-10-01" "2004-01-01" "2004-04-01" "2004-07-01"# Calculate age in years and months
current_date=Sys.Date()
age_years=as.numeric(difftime(current_date, dt_date, units = "weeks")) %/% 52
age_months= (as.numeric(difftime(current_date, dt_date, units = "weeks")) %% 52) %/% 4
print(paste("Age:", age_years, "years and", age_months, "months"))## [1] "Age: 21 years and 2 months"