# getting and setting working directory (the folder path where R is looking our for absolute loading and saving of files to)
getwd()## [1] "C:/Users/MUSAAB-TECH/OneDrive/icammada/PROJECT"setwd("C:/Users/MUSAAB-TECH/OneDrive/Documents")
getwd() # to confirm ## [1] "C:/Users/MUSAAB-TECH/OneDrive/Documents"simple arithmetic operations in R:
# Basic arithmetic operations
5 + 3## [1] 810 - 4## [1] 66 * 7## [1] 4220 / 4## [1] 5Exercise 1.1: Calculate the following expressions:
# Calculate 15 + 25
15 + 25## [1] 40# Calculate 100 - 37
100 - 37## [1] 63# Calculate 8 * 9
8 * 9## [1] 72In R, we can store values in objects using the assignment operator
<-:
# Assigning values to objects
x <- 10
y <- 5
result <- x + y
print(result)## [1] 15Exercise 1.2: Create my own variables:
# Create a variable 'age' with your age
age <- 25
# Create a variable 'name' with your name
name <- "Student"
# Print both variables
print(age)## [1] 25print(name)## [1] "Student"Vectors are the most basic data structure in R. They contain elements of the same type of 1 dimension array.
# Creating numeric vectors
numbers <- c(1, 2, 3, 4, 5)
print(numbers)## [1] 1 2 3 4 5# Creating character vectors
fruits <- c("apple", "banana", "orange")
print(fruits)## [1] "apple" "banana" "orange"# Creating logical vectors
logical_vals <- c(TRUE, FALSE, TRUE)
print(logical_vals)## [1] TRUE FALSE TRUEExercise 2.1 : Create your own vectors:
# Create a vector of your favorite numbers
fav_numbers <- c(7, 13, 21, 42)
print(fav_numbers)## [1] 7 13 21 42# Create a vector of colors
colors <- c("red", "blue", "green", "yellow")
print(colors)## [1] "red" "blue" "green" "yellow"# Vector arithmetic
vec1 <- c(1, 2, 3, 4)
vec2 <- c(5, 6, 7, 8)
# Element-wise operations
sum_vec <- vec1 + vec2
print(sum_vec)## [1] 6 8 10 12# Vector functions
length(vec1)## [1] 4sum(vec1)## [1] 10mean(vec1)## [1] 2.5Exercise 2.2 : Practice vector operations:
# Create two vectors
scores1 <- c(85, 90, 78, 92)
scores2 <- c(88, 85, 82, 95)
# Calculate the average of both vectors
avg_scores <- (scores1 + scores2) / 2
print(avg_scores)## [1] 86.5 87.5 80.0 93.5# Find the maximum score in scores1
max(scores1)## [1] 92# Creating sequences
seq1 <- 1:10
print(seq1)## [1] 1 2 3 4 5 6 7 8 9 10# Using seq() function
seq2 <- seq(0, 20, by = 2)
print(seq2)## [1] 0 2 4 6 8 10 12 14 16 18 20# Repetition with rep()
rep_vec <- rep(c(1, 2, 3), times = 3)
print(rep_vec)## [1] 1 2 3 1 2 3 1 2 3Exercise 2.3 : Create complex sequences:
# Create a sequence from 5 to 50 by steps of 5
seq_5_50 <- seq(5, 50, by = 5)
print(seq_5_50)## [1] 5 10 15 20 25 30 35 40 45 50# Repeat the pattern c("A", "B") 4 times
pattern <- rep(c("A", "B"), times = 4)
print(pattern)## [1] "A" "B" "A" "B" "A" "B" "A" "B"# Create a decreasing sequence from 100 to 1 by steps of -10
decreasing <- seq(100, 1, by = -10)
print(decreasing)## [1] 100 90 80 70 60 50 40 30 20 10Subsetting is crucial for data manipulation:
# Create a sample vector
sample_vec <- c(10, 20, 30, 40, 50)
# Access single elements
first_element <- sample_vec[1]
print(first_element)## [1] 10# Access multiple elements
subset1 <- sample_vec[c(1, 3, 5)]
print(subset1)## [1] 10 30 50# Access ranges
subset2 <- sample_vec[2:4]
print(subset2)## [1] 20 30 40Exercise 2.4 : Practice subsetting:
# Create a vector of months
months <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun")
# Extract the first three months
first_quarter <- months[1:3]
print(first_quarter)## [1] "Jan" "Feb" "Mar"# Extract specific months (1st, 4th, and 6th)
specific_months <- months[c(1, 4, 6)]
print(specific_months)## [1] "Jan" "Apr" "Jun"Exercise 2.5 : Advanced subsetting with conditions:
# Create a vector of test scores
test_scores <- c(78, 85, 92, 67, 88, 95, 73, 89)
# Find scores greater than 80
high_scores <- test_scores[test_scores > 80]
print(high_scores)## [1] 85 92 88 95 89# Find positions of scores greater than 85
high_positions <- which(test_scores > 85)
print(high_positions)## [1] 3 5 6 8# Replace scores below 75 with 75
adjusted_scores <- test_scores
adjusted_scores[adjusted_scores < 75] <- 75
print(adjusted_scores)## [1] 78 85 92 75 88 95 75 89# a. Verify that 6a + 42/3^4.2 - 3.62 = 29.50556 when a = 2.3.
a <- 2.3
result_a <- (6*a + 42) / 3^(4.2 - 3.62)
result_a## [1] 29.50556print(paste("Result:", result_a))## [1] "Result: 29.5055606431273"print(paste("Expected: 29.50556"))## [1] "Expected: 29.50556"print(paste("Match:", round(result_a, 5) == 29.50556))## [1] "Match: TRUE"# a. Create object with 3^2 × 4^1/8
my_object <- 3^2 * 4^1/8
print(paste("Original value:", my_object))## [1] "Original value: 4.5"# b. Overwrite by dividing by 2.33
my_object <- my_object / 2.33
print(paste("After division by 2.33:", my_object))## [1] "After division by 2.33: 1.931330472103"Matrices are two-dimensional arrays of the same data type.
# Create a matrix using matrix() function
mat1 <- matrix(1:12, nrow = 3, ncol = 4)
print(mat1)## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12# Create matrix by binding vectors
vec_a <- c(1, 2, 3)
vec_b <- c(4, 5, 6)
mat2 <- rbind(vec_a, vec_b)
print(mat2)## [,1] [,2] [,3]
## vec_a 1 2 3
## vec_b 4 5 6Exercise 3.1 : Create your own matrices:
# Create a 2x3 matrix with numbers 1-6
my_matrix <- matrix(1:6, nrow = 2, ncol = 3)
print(my_matrix)## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6# Create a matrix by column binding two vectors
col1 <- c(10, 20, 30)
col2 <- c(40, 50, 60)
combined_matrix <- cbind(col1, col2)
print(combined_matrix)## col1 col2
## [1,] 10 40
## [2,] 20 50
## [3,] 30 60# Matrix dimensions
mat <- matrix(1:6, nrow = 2)
dim(mat)## [1] 2 3nrow(mat)## [1] 2ncol(mat)## [1] 3mat## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6# Matrix transpose
t(mat)## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6# Matrix multiplication
mat_a <- matrix(c(1, 2, 3, 4), nrow = 2)
mat_b <- matrix(c(5, 6, 7, 8), nrow = 2)
mat_product <- mat_a %*% mat_b
print(mat_product)## [,1] [,2]
## [1,] 23 31
## [2,] 34 46Exercise 3.2 : Matrix calculations:
# Create two 3x3 matrices
matrix_x <- matrix(1:9, nrow = 3)
matrix_y <- matrix(9:1, nrow = 3)
print("Matrix X:")## [1] "Matrix X:"print(matrix_x)## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9print("Matrix Y:")## [1] "Matrix Y:"print(matrix_y)## [,1] [,2] [,3]
## [1,] 9 6 3
## [2,] 8 5 2
## [3,] 7 4 1# Element-wise addition
addition <- matrix_x + matrix_y
print("Addition:")## [1] "Addition:"print(addition)## [,1] [,2] [,3]
## [1,] 10 10 10
## [2,] 10 10 10
## [3,] 10 10 10# Matrix multiplication
multiplication <- matrix_x %*% matrix_y
print("Matrix multiplication:")## [1] "Matrix multiplication:"print(multiplication)## [,1] [,2] [,3]
## [1,] 90 54 18
## [2,] 114 69 24
## [3,] 138 84 30# Create a sample matrix
sample_matrix <- matrix(1:12, nrow = 3, ncol = 4)
print(sample_matrix)## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12# Access specific elements
element <- sample_matrix[2, 3]
print(element)## [1] 8# Access entire rows or columns
row2 <- sample_matrix[2, ]
col3 <- sample_matrix[, 3]
print(row2)## [1] 2 5 8 11print(col3)## [1] 7 8 9Exercise 3.3 : Practice matrix subsetting:
# Create a 4x4 matrix
big_matrix <- matrix(1:16, nrow = 4)
print(big_matrix)## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16# Extract the diagonal elements
diagonal <- diag(big_matrix)
print(diagonal)## [1] 1 6 11 16# Extract a 2x2 submatrix from the top-left corner
submatrix <- big_matrix[1:2, 1:2]
print(submatrix)## [,1] [,2]
## [1,] 1 5
## [2,] 2 6# Construct and store a 4 × 2 matrix that's filled row-wise with the values 4.3, 3.1, 8.2, 8.2, 3.2, 0.9, 1.6, and 6.5, in that order.
# Confirm the dimensions of the matrix from (a) are 3 × 2 if you remove the first row.
# a. Create 4x2 matrix filled row-wise
values <- c(4.3, 3.1, 8.2, 8.2, 3.2, 0.9, 1.6, 6.5)
my_matrix <- matrix(values, nrow = 4, ncol = 2, byrow = TRUE)
print("4x2 matrix:")## [1] "4x2 matrix:"print(my_matrix)## [,1] [,2]
## [1,] 4.3 3.1
## [2,] 8.2 8.2
## [3,] 3.2 0.9
## [4,] 1.6 6.5# b. Remove first row and check dimensions
matrix_no_first_row <- my_matrix[-1, ]
print("Matrix after removing first row:")## [1] "Matrix after removing first row:"print(matrix_no_first_row)## [,1] [,2]
## [1,] 8.2 8.2
## [2,] 3.2 0.9
## [3,] 1.6 6.5dim(matrix_no_first_row)## [1] 3 2