R Exercise

R Questions & Solutions

Vector

1.Create a numeric variable “num_var” with the value “42.5”.

num_var <- 42.5 
num_var

## [1] 42.5

2.Print the data type “char_var”.

char_var <- "R is fun!" 
char_var

## [1] "R is fun!"

3.Print the data type “char_var”.

class(char_var)

## [1] "character"

4.Create a list “student_info” with the following elements:

“name”: “Mohammad Nasir Abdullah”

“age”: 18

“grades”: a numeric vector with values “99, 100, 89”.

student_info <- list( Name="Mohamad Nasir Abdullah",                        Age= 18, Grades= c(99,100,89))  
student_info

## $Name
## [1] "Mohamad Nasir Abdullah"
## 
## $Age
## [1] 18
## 
## $Grades
## [1]  99 100  89

5.Create a data frame “df_students” with the following columns

1.”Name”: “John”, “Pablo”

2.”Age”: “22”, “30”

3.”Grade”: “A”, “C”

df_students <- list( Name= c("John","Pablo"), Age= c(22,30), Grade= c("A","C")) 
df_students

## $Name
## [1] "John"  "Pablo"
## 
## $Age
## [1] 22 30
## 
## $Grade
## [1] "A" "C"

6.Create a numeric vector “vec_num” with the values “5,10,15,20”.

vec_num <- c(5,10,15,20) 
vec_num

## [1]  5 10 15 20

7.Extract the second and third elements from “vec_num”.

vec_num[c(2,3)]

## [1] 10 15

8.Create a vector “vec_seq” that contains a sequence of number from 1 to 10.

vec_seq <- seq(1,10, by=1) 
vec_seq

##  [1]  1  2  3  4  5  6  7  8  9 10

9.Create a vector “vec_rand” with 5 random numbers between 1 and 100.

set.seed(123456) 
vec_rand <- sample(1:100, size = 5) 
vec_rand

## [1] 60 42 71 54 74

10.Create a character vector “vec_char” with the values “apple”, “banana”, “cherry”.

vec_char <- c("apple","banana","cherry") 
vec_char

## [1] "apple"  "banana" "cherry"

11.Use the substr() function to extract the first 3 characters from each elements of “vec_char”.

substr(vec_char, start = 1, stop = 3)

## [1] "app" "ban" "che"

12.Create a character vector “vec_colors” with the values “red”, “blue”, “green”, “red”, “blue”.

vec_colors <- c("red","blue","green","red","blue")

13.Convert “vec_colors” into a factor vector “factor_colors”.

factor_colors <- as.factor(vec_colors)

14.Print the levels of “factor_colors”.

factor_colors

## [1] red   blue  green red   blue 
## Levels: blue green red

15.Given a numeric vector “vec_bonus = c(10,20,30,40,50)”, write a code snippet to :

vec_bonus <- c(10,20,30,40,50)

1.Extract all elements greater than 25.

vec_bonus[vec_bonus>25]

## [1] 30 40 50

2.Calculate the mean of the extracted elements.

mean(vec_bonus[vec_bonus>25])

## [1] 40

16.Given the following vectors:

weights <- c(55, 68, 72, 61, 58, 75, 64, 70) names <- c(“Alice”, “Bob”, “Charlie”, “Daisy”, “Eve”, “Frank”, “Grace”, “Henry”)

weights <- c(55, 68, 72, 61, 58, 75, 64, 70) 
names <- c("Alice", "Bob","Charlie","Daisy","Eve","Frank","Grace","Henry")

Identify the names of individuals whose weight is greater than 65kg.

names(weights) <- names 
weights

##   Alice     Bob Charlie   Daisy     Eve   Frank   Grace   Henry 
##      55      68      72      61      58      75      64      70

weights[weights > 65]

##     Bob Charlie   Frank   Henry 
##      68      72      75      70

Find the average weight of individuals weighting less than or equal to 60kg.

mean(weights[weights <= 60])

## [1] 56.5

Extract the weights of individuals whose names start with the letter ‘A’ or ‘D’.

weights[startsWith(names,"A")| startsWith(names,"D")]

## Alice Daisy 
##    55    61

Determine the number of individuals whose weight is between 60kg and 70kg (inclusive).

length(weights[weights>= 60 & weights <= 70])

## [1] 4

17.Given the following vector of daily temperatures (in Celsius) for a week:

temperatures <- c(22, 25, 19, 21, 18, 24, 23) days <- c(“Monday”, “Tuesday”, “Wednesday”, “Thursday”, “Friday”, “Saturday”, “Sunday”)

temperatures <- c(22,25,19,21,18,24,23) 
days <- c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday")

Identify the days when the temperature was below 20°C.

names(temperatures) <- days 
temperatures

##    Monday   Tuesday Wednesday  Thursday    Friday  Saturday    Sunday 
##        22        25        19        21        18        24        23

temperatures[temperatures<20]

## Wednesday    Friday 
##        19        18

Calculate the difference between the highest and lowest temperatures of the week.

max(temperatures) - min(temperatures)

## [1] 7

Determine the number of days when the temperature was between 20°C and 24°C (inclusive).

length(temperatures[temperatures >= 20 & temperatures <= 24])

## [1] 4

18.Given the following vector of exam scores:

scores <- c(85, 78, 92, 65, 88, 70, 95, 80, 60, 90) students <- c(“Anna”, “Ben”, “Cara”, “Dan”, “Ella”, “Finn”, “Grace”, “Hank”, “Ivy”, “Jack”)

scores <- c(85,78,92,65,88,70,95,80,60,90) 
students <- c("Anna", "Ben", "Cara", "Dan", "Ella", "Finn", "Grace", "Hank", "Ivy", "Jack")

Identify the students who scored above 90.

names(scores) <- students  
scores[scores > 90]

##  Cara Grace 
##    92    95

Find the average score of students who scored below 80.

mean(scores[scores <80])

## [1] 68.25

Extract the scores of students whose names start with a vowel (A, E, I, O, U).

scores[startsWith(students,"A")|startsWith(students,"E")|startsWith(students,"I")| startsWith(students,"O")|startsWith(students,"U")]

## Anna Ella  Ivy 
##   85   88   60

Matrix

Exercise 1: Basic Matrix Creation

1.Create a matrix with numbers from 1 to 12, having 4 rows. Print the matrix.

matrix(1:12, nrow=4 )

##      [,1] [,2] [,3]
## [1,]    1    5    9
## [2,]    2    6   10
## [3,]    3    7   11
## [4,]    4    8   12

2.Now, reshape the same data into a matrix with 3 columns. Print the result.

matrix(1:12, ncol=3)

##      [,1] [,2] [,3]
## [1,]    1    5    9
## [2,]    2    6   10
## [3,]    3    7   11
## [4,]    4    8   12

Exercise 2: Filling Order

1.Create a matrix with numbers from 1 to 6, having 2 rows, filled by columns. Print the matrix.

matrix(1:6, nrow=2, byrow= FALSE)

##      [,1] [,2] [,3]
## [1,]    1    3    5
## [2,]    2    4    6

2.Create another matrix with the same data, but this time filled by rows. Print the matrix and compare with the previous one.

matrix(1:6, nrow=2 , byrow= TRUE)

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6

Exercise 3: Naming Matrix Dimensions

1.Create a matrix with numbers from 1 to 9, having 3 rows.

matrix(1:9, nrow=3)

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

2.Assign row names as “R1”, “R2”, and “R3”. Assign column names as “C1”, “C2”, and “C3”.

rownames <- c("R1", "R2", "R3")
columnnames <- c("C1", "C2", "C3")

3.Print the matrix with row and column names.

matrix(1:9, nrow=3, ncol=3, byrow= FALSE,
       dimnames = list(rownames, columnnames))

##    C1 C2 C3
## R1  1  4  7
## R2  2  5  8
## R3  3  6  9

Exercise 4: Accessing Matrix Elements

1.Consider the matrix:

mat <- matrix(1:9, nrow=3, dimnames=list(c("R1", "R2", "R3"), c("C1", "C2", "C3")))
mat

##    C1 C2 C3
## R1  1  4  7
## R2  2  5  8
## R3  3  6  9

Print the element in the second row and third column.

mat[2,3]

## [1] 8

Print the entire second row.

mat[2, ]

## C1 C2 C3 
##  2  5  8

c.Print the entire first column.

mat[ ,1]

## R1 R2 R3 
##  1  2  3

Exercise 5: Matrix Operations

1.Create two matrices:

A <- matrix(1:4, nrow=2)  
B <- matrix(5:8, nrow=2)
A;B

##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4

##      [,1] [,2]
## [1,]    5    7
## [2,]    6    8

Perform and print the matrix addition of A and B.

A+B

##      [,1] [,2]
## [1,]    6   10
## [2,]    8   12

Perform and print the matrix multiplication of A and B.

A*B

##      [,1] [,2]
## [1,]    5   21
## [2,]   12   32

Exercise 6: Advanced Matrix Creation

1.Create a diagonal matrix with the numbers 4, 5, and 6 on its diagonal. Print the matrix.

x<- diag(x=c(4,5,6), nrow=3, ncol=3, names= T)
x

##      [,1] [,2] [,3]
## [1,]    4    0    0
## [2,]    0    5    0
## [3,]    0    0    6

2.Check if the matrix from the previous step is symmetric. Print the result.

isSymmetric(x)

## [1] TRUE

Exercise 7: Basic Matrix Operations

1.Create two 2x2 matrices: A=[2435] B=[1324]

A <- matrix(2:5, nrow=2, ncol=2, byrow= TRUE)
B <- matrix(1:4, nrow=2, ncol=2, byrow= TRUE)
A;B

##      [,1] [,2]
## [1,]    2    3
## [2,]    4    5

##      [,1] [,2]
## [1,]    1    2
## [2,]    3    4

Perform and print matrix addition for A and B.

A+B

##      [,1] [,2]
## [1,]    3    5
## [2,]    7    9

Perform and print matrix subtraction for A and B.

A-B

##      [,1] [,2]
## [1,]    1    1
## [2,]    1    1

Exercise 8: Matrix Multiplication

1.Using the matrices A and B from Exercise 7:

Perform and print element-wise multiplication.

A*B

##      [,1] [,2]
## [1,]    2    6
## [2,]   12   20

Perform and print matrix multiplication.

A%%B

##      [,1] [,2]
## [1,]    0    1
## [2,]    1    1

Exercise 9: Scalar Operations

1.For the matrix A from Exercise 7:

Multiply the matrix by the scalar value 3 and print the result.

A*3

##      [,1] [,2]
## [1,]    6    9
## [2,]   12   15

Divide the matrix by the scalar value 2 and print the result.

A/2

##      [,1] [,2]
## [1,]    1  1.5
## [2,]    2  2.5

Exercise 10: Transposition and Inversion

1.Using the matrix A from Exercise 7:

Find and print the transpose of matrix A.

t(A)

##      [,1] [,2]
## [1,]    2    4
## [2,]    3    5

Find and print the inverse of matrix A. (Ensure the matrix is invertible first)

solve(A)

##      [,1] [,2]
## [1,] -2.5  1.5
## [2,]  2.0 -1.0

Exercise 11: Diagonal and Power Operations

1.For the matrix A from Exercise 7:

Extract and print the diagonal of the matrix.

diag(A)

## [1] 2 5

Raise the matrix to the power of 3 and print the result.

A^3

##      [,1] [,2]
## [1,]    8   27
## [2,]   64  125

Exercise 12: Advanced Operations

1.Create a matrix C

C <- matrix(1:9, nrow=3, ncol=3, byrow= TRUE)
C

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9

Perform matrix multiplication of A and C. Is it possible? If not, explain why.

#A*C
#It cannot be multiplied since the number of columns in A is not equal to the number of rows in C

Extract the second row and third column of matrix C and print them.

C[2,3]

## [1] 6

Exercise 13: Basic Matrix Indexing

1.Create the following matrix:

Matrix M:

M <- matrix(1:9, nrow=3,ncol=3)
M

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

Access and print the element in the 3rd row, 1st column.

M[3,1]

## [1] 3

Retrieve the entire 2nd row. What values do you get?

M[2, ]

## [1] 2 5 8

Extract the entire 3rd column. What values are present?

M[ ,3]

## [1] 7 8 9

Exercise 14: Advanced Matrix Indexing

1.Using the matrix M from Exercise 13:

Access and print the elements in the 1st and 3rd rows of the 2nd column.

M[c(1,3), 2]

## [1] 4 6

Extract a submatrix containing the 1st and 2nd rows of the 1st and 3rd columns.

M[c(1,2), c(1,3)]

##      [,1] [,2]
## [1,]    1    7
## [2,]    2    8

Can you retrieve all elements of M that are greater than 4? If so, which elements satisfy this condition?

M[M>4]

## [1] 5 6 7 8 9

Exercise 15: Conditional Indexing

1.Still with matrix M:

Identify and print elements that are even numbers.

M[M%%2==0]

## [1] 2 4 6 8

Retrieve all elements that are less than 7 and are odd numbers.

M[M<7 & M%%2!=0]

## [1] 1 3 5

Exercise 16: Negative Indexing

1.Once again, with matrix M:

Access and print the matrix excluding the 2nd row.

M[-2, ]

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    3    6    9

Extract the matrix without the 1st and 3rd columns.

M[,c(-1,-3)]

## [1] 4 5 6

Exercise 17: Practical Application

1.Imagine matrix M represents scores of 3 students in 3 subjects. Rows represent students and columns represent subjects.

If the 3rd subject is “Math”, extract scores of all students in Math.

M[,3]

## [1] 7 8 9

The second student retook the exam for the 1st subject and scored a 5. Update the matrix to reflect this score.

M[2,1] <- 5
M

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    5    5    8
## [3,]    3    6    9

Calculate the average score for the 1st student across all subjects.

mean(M[1, ])

## [1] 4

Array

Exercise 1: Basic Array Operations

1.Create a 3D array named my_array with dimensions 3x4x2 using numbers from 1 to 24. Print the array.

my_array <- array(1:24, c(3,4,2))
my_array

## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]   13   16   19   22
## [2,]   14   17   20   23
## [3,]   15   18   21   24

2.Access and print the element located in the 2nd row, 3rd column, and 1st layer of my_array.

my_array[2,3,1]

## [1] 8

3.Retrieve the entire 1st layer of my_array. What values are present?

my_array[ , ,1]

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

Exercise 2: Array Arithmetic

1.Create another 3D array named another_array with dimensions 3x4x2 using numbers from 25 to 48.

another_array <- array(25:48, c(3,4,2))
another_array

## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]   25   28   31   34
## [2,]   26   29   32   35
## [3,]   27   30   33   36
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]   37   40   43   46
## [2,]   38   41   44   47
## [3,]   39   42   45   48

2.Perform and print the result of the element-wise addition of my_array and another_array.

my_array + another_array

## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]   26   32   38   44
## [2,]   28   34   40   46
## [3,]   30   36   42   48
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]   50   56   62   68
## [2,]   52   58   64   70
## [3,]   54   60   66   72

3.Multiply my_array by a scalar value of 2. Print the result.

my_array*2

## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]    2    8   14   20
## [2,]    4   10   16   22
## [3,]    6   12   18   24
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]   26   32   38   44
## [2,]   28   34   40   46
## [3,]   30   36   42   48

4.Execute element-wise multiplication between my_array and another_array. Print the outcome.

my_array*another_array

## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]   25  112  217  340
## [2,]   52  145  256  385
## [3,]   81  180  297  432
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]  481  640  817 1012
## [2,]  532  697  880 1081
## [3,]  585  756  945 1152

Exercise 3: Using apply() with Arrays

1.Using my_array from Exercise 1:

Calculate and print the sum of elements along the 1st dimension (rows).

apply(my_array, MARGIN= 1, sum)

## [1]  92 100 108

Compute and print the mean value for each layer (3rd dimension).

apply(my_array, MARGIN= 3, mean)

## [1]  6.5 18.5

c.Determine the maximum value for each column across all layers. Print the results.

apply(my_array, MARGIN=c(2,3), max)

##      [,1] [,2]
## [1,]    3   15
## [2,]    6   18
## [3,]    9   21
## [4,]   12   24

2.Define a function that calculates the range (difference between maximum and minimum) of a numeric vector.

range_function <- function(x) {return(max(x)-min(x))}

Use the apply() function to calculate the range for each column in my_array across all layers. Print the results.

apply(my_array, MARGIN= 3, range_function )

## [1] 11 11

Modify the function to also return the mean of the vector. Use apply() to retrieve both the range and mean for each row in my_array across all layers. Print the outcomes.

mean_range <- function(x) {return (c(max(x)-min(x), mean(x)))}
apply(my_array, MARGIN= c(1,3), FUN=mean_range)

## , , 1
## 
##      [,1] [,2] [,3]
## [1,]  9.0  9.0  9.0
## [2,]  5.5  6.5  7.5
## 
## , , 2
## 
##      [,1] [,2] [,3]
## [1,]  9.0  9.0  9.0
## [2,] 17.5 18.5 19.5

Exercise 4: Advanced apply() Usage

1.Using my_array:

Define a custom function that multiplies a vector by a given scalar and then adds another scalar (both scalars are arguments to the function).

scalar_function <- function(x,k,y) {return (x*k+y)}

Use the apply() function to apply this custom function on my_array, choosing a multiplication scalar of 0.5 and an addition scalar of 10. Print the result.

apply(my_array,MARGIN=3, scalar_function,k=0.5, y=10)

##       [,1] [,2]
##  [1,] 10.5 16.5
##  [2,] 11.0 17.0
##  [3,] 11.5 17.5
##  [4,] 12.0 18.0
##  [5,] 12.5 18.5
##  [6,] 13.0 19.0
##  [7,] 13.5 19.5
##  [8,] 14.0 20.0
##  [9,] 14.5 20.5
## [10,] 15.0 21.0
## [11,] 15.5 21.5
## [12,] 16.0 22.0

2.Calculate the standard deviation for each row in my_array across all columns and layers.

apply(my_array, MARGIN= c(2,3), sd)

##      [,1] [,2]
## [1,]    1    1
## [2,]    1    1
## [3,]    1    1
## [4,]    1    1

R Exercise

Nur Alya Farahin binti Khairul Anuar

2023-10-17

R Questions & Solutions

Vector

Matrix

Array