Exercise 1:
Create a data frame named students with the following columns: Name, Age, Grade, and Subject. Populate it with at least 5 rows of sample data.
student<- data.frame(
name = c('Ali', "Bob", "Lukman", "Aishah", "Kamal"),
age = c(10, 20, 19, 28, 31),
grade = c("A", "A", "B", "B", "C"),
subject = c("Math", "History", "Math", "English", "History")
)
student name age grade subject
1 Ali 10 A Math
2 Bob 20 A History
3 Lukman 19 B Math
4 Aishah 28 B English
5 Kamal 31 C HistoryDisplay the structure of the students data frame using the str() function.
str(student)'data.frame': 5 obs. of 4 variables:
$ name : chr "Ali" "Bob" "Lukman" "Aishah" ...
$ age : num 10 20 19 28 31
$ grade : chr "A" "A" "B" "B" ...
$ subject: chr "Math" "History" "Math" "English" ...Add a new column to the students data frame named Attendance and populate it with sample data
student$Attendance<- as.factor(c("Present", "Present", "Absent", "Absent", "Present"))
student$Attendance[1] Present Present Absent Absent Present
Levels: Absent PresentExercise 2:
From the mtcars dataset, extract the mpg (miles per gallon) and hp (horsepower) columns and save them as a new data frame named car_specs.
car_specs<- mtcars[ , c("mpg", "hp")]
car_specs mpg hp
Mazda RX4 21.0 110
Mazda RX4 Wag 21.0 110
Datsun 710 22.8 93
Hornet 4 Drive 21.4 110
Hornet Sportabout 18.7 175
Valiant 18.1 105
Duster 360 14.3 245
Merc 240D 24.4 62
Merc 230 22.8 95
Merc 280 19.2 123
Merc 280C 17.8 123
Merc 450SE 16.4 180
Merc 450SL 17.3 180
Merc 450SLC 15.2 180
Cadillac Fleetwood 10.4 205
Lincoln Continental 10.4 215
Chrysler Imperial 14.7 230
Fiat 128 32.4 66
Honda Civic 30.4 52
Toyota Corolla 33.9 65
Toyota Corona 21.5 97
Dodge Challenger 15.5 150
AMC Javelin 15.2 150
Camaro Z28 13.3 245
Pontiac Firebird 19.2 175
Fiat X1-9 27.3 66
Porsche 914-2 26.0 91
Lotus Europa 30.4 113
Ford Pantera L 15.8 264
Ferrari Dino 19.7 175
Maserati Bora 15.0 335
Volvo 142E 21.4 109Retrieve the first 6 rows of the car_specs data frame.
head(car_specs) mpg hp
Mazda RX4 21.0 110
Mazda RX4 Wag 21.0 110
Datsun 710 22.8 93
Hornet 4 Drive 21.4 110
Hornet Sportabout 18.7 175
Valiant 18.1 105Create a subset of mtcars containing only cars with 6 cylinders (cyl).
mtcars[mtcars$cyl==6, ] mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6Exercise 3:
Calculate the median horsepower (hp) of all cars in the mtcars dataset.
median(mtcars$hp)[1] 123How many cars in the dataset have an automatic transmission (am column: 0 represents automatic, 1 represents manual)?
length(mtcars$am[mtcars$am == 0])[1] 19Which car model in the mtcars dataset has the highest miles per gallon (mpg)?
rownames(mtcars)[which.max(mtcars$mpg)][1] "Toyota Corolla"Exercise 4:
Extract and display all cars from mtcars with 4 cylinders (cyl).
rownames(mtcars[mtcars$cyl == 4, ]) [1] "Datsun 710" "Merc 240D" "Merc 230" "Fiat 128"
[5] "Honda Civic" "Toyota Corolla" "Toyota Corona" "Fiat X1-9"
[9] "Porsche 914-2" "Lotus Europa" "Volvo 142E" How many cars in the mtcars dataset have more than 100 horsepower (hp) and weigh (column wt) less than 3,000 lbs?
nrow(mtcars[mtcars$hp > 100 & mtcars$wt < 3, ])[1] 5Retrieve all car models from mtcars that have an automatic transmission and can cover more than 20 miles per gallon.
rownames(mtcars[mtcars$am == 0 & mtcars$mpg > 20, ])[1] "Hornet 4 Drive" "Merc 240D" "Merc 230" "Toyota Corona" Exercise 5:
How many rows and columns are present in the mtcars dataset?
dim(mtcars)[1] 32 11What are the names of all the columns in the dataset?
colnames(mtcars) [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
[11] "carb"Display the last 8 rows of the dataset.
tail(mtcars, 8) mpg cyl disp hp drat wt qsec vs am gear carb
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Exercise 6:
Calculate the median horsepower (hp) of all cars in the dataset.
median(mtcars$hp)[1] 123How many cars in the dataset have an automatic transmission (am column: 0 represents automatic, 1 represents manual)?
sum(mtcars$am == 0)[1] 19Which car model has the highest miles per gallon (mpg)?
rownames(mtcars)[which.max(mtcars$mpg)][1] "Toyota Corolla"Exercise 7:
Extract and display all cars with 4 cylinders (cyl).
rownames(mtcars[mtcars$cyl == 4, ]) [1] "Datsun 710" "Merc 240D" "Merc 230" "Fiat 128"
[5] "Honda Civic" "Toyota Corolla" "Toyota Corona" "Fiat X1-9"
[9] "Porsche 914-2" "Lotus Europa" "Volvo 142E" How many cars have more than 100 horsepower (hp) and weigh (column wt) less than 3,000 lbs?
nrow(mtcars[mtcars$hp > 100 & mtcars$wt < 3, ])[1] 5Retrieve all car models that have an automatic transmission and can cover more than 20 miles per gallon.
rownames(mtcars[mtcars$am == 0 & mtcars$mpg > 20, ])[1] "Hornet 4 Drive" "Merc 240D" "Merc 230" "Toyota Corona" GRADE ASSIGNMENT
score <- 87
if (score >= 90) {
print(“A”)
} else if (score >= 80) {
print(“B”)
} else if (score >= 70) {
print(“C”)
} else if (score >= 60) {
print(“D”)
} else {
print(“FAIL”)
}
GIVEN SCORE == 87 CLASSIFY SCORE, >=90 A, >=80 - 90 AS B, MPORE THAN 70-80 AS C, 60 - 70 AS D AND ELSE IF FAIL
score <- 87
if (score >= 90) {
print("A")
} else if (score >= 80) {
print("B")
} else if (score >= 70) {
print("C")
} else if (score >= 60) {
print("D")
} else {
print("FAIL")
}[1] "B"