Matrix dimensions in R Programming - Mini Project: Build & Apply

Matrix dimensions

📖 Scenario: You are working with a simple matrix in R, which is like a table of numbers arranged in rows and columns. Understanding the size of this matrix helps you know how many rows and columns it has.

🎯 Goal: You will create a matrix with specific numbers, then find out how many rows and columns it has using R functions.

📋 What You'll Learn

Create a matrix with exact values

Use a variable to store the matrix

Use R functions to find the number of rows and columns

Print the number of rows and columns

💡 Why This Matters

🌍 Real World

Matrices are used in data analysis, statistics, and scientific computing to organize and manipulate data in rows and columns.

💼 Career

Knowing how to handle matrix dimensions is important for data scientists, statisticians, and anyone working with numerical data in R.

1

Create the matrix

Create a matrix called my_matrix with the numbers 1 to 6 arranged in 2 rows and 3 columns using the matrix() function.

R Programming

# Create the matrix my_matrix with numbers 1 to 6 in 2 rows and 3 columns
# Your code here

Hint

Use matrix(1:6, nrow = 2, ncol = 3) to create the matrix.

2

Store the number of rows

Create a variable called num_rows and set it to the number of rows in my_matrix using the nrow() function.

R Programming

my_matrix <- matrix(1:6, nrow = 2, ncol = 3)
# Create num_rows to store the number of rows in my_matrix
# Your code here

Hint

Use nrow(my_matrix) to get the number of rows.

3

Store the number of columns

Create a variable called num_cols and set it to the number of columns in my_matrix using the ncol() function.

R Programming

my_matrix <- matrix(1:6, nrow = 2, ncol = 3)
num_rows <- nrow(my_matrix)
# Create num_cols to store the number of columns in my_matrix
# Your code here

Hint

Use ncol(my_matrix) to get the number of columns.

4

Print the dimensions

Print the number of rows and columns stored in num_rows and num_cols using two separate print() statements.

R Programming

my_matrix <- matrix(1:6, nrow = 2, ncol = 3)
num_rows <- nrow(my_matrix)
num_cols <- ncol(my_matrix)
# Print num_rows and num_cols
# Your code here

Hint

Use print(num_rows) and print(num_cols) to show the results.

Practice

1. What does the dim() function return when used on a matrix in R?

easy

A. The first element of the matrix

B. The sum of all elements in the matrix

C. A vector with the number of rows and columns

D. The matrix converted to a vector

Solution

1. Step 1: Understand the purpose of dim()

   The dim() function in R returns the dimensions of an object, typically a matrix or array.

2. Step 2: What does it return for a matrix?

   For a matrix, dim() returns a vector of length two: the number of rows and the number of columns.

3. Final Answer:

   A vector with the number of rows and columns -> Option C

4. Quick Check:

   dim(matrix) = c(rows, columns) [OK]

Hint: Remember dim() gives rows and columns together [OK]

Common Mistakes:

• Thinking dim() returns sum of elements
• Confusing dim() with length()
• Expecting dim() to return matrix elements

2. Which of the following is the correct way to create a 3x2 matrix in R?

easy

A. matrix(1:6, nrow=3, ncol=2)

B. matrix(1:6, nrow=2, ncol=3)

C. matrix(1:6, rows=3, cols=2)

D. matrix(1:6, 3, 2, byrow=TRUE)

Solution

1. Step 1: Check the matrix function parameters

   The matrix() function uses nrow and ncol to specify rows and columns.

2. Step 2: Identify correct parameter names and values

   matrix(1:6, nrow=3, ncol=2) uses nrow=3 and ncol=2, which correctly creates a 3-row, 2-column matrix.

3. Final Answer:

   matrix(1:6, nrow=3, ncol=2) -> Option A

4. Quick Check:

   Use nrow and ncol to set dimensions [OK]

Hint: Use nrow and ncol to set matrix size [OK]

Common Mistakes:

• Using wrong parameter names like rows or cols
• Swapping nrow and ncol values
• Omitting parameter names causing confusion

3. What is the output of the following R code?

m <- matrix(1:12, nrow=3, ncol=4)
dim(m)

medium

A. [1] 12

B. [1] 4 3

C. [1] 3

D. [1] 3 4

Solution

1. Step 1: Understand matrix creation

   The matrix m is created with 3 rows and 4 columns using nrow=3 and ncol=4.

2. Step 2: Check the output of dim(m)

   The dim() function returns a vector with the number of rows and columns, so it returns 3 4.

3. Final Answer:

   [1] 3 4 -> Option D

4. Quick Check:

   dim(m) = c(3, 4) [OK]

Hint: dim() returns rows then columns vector [OK]

Common Mistakes:

• Confusing order of rows and columns
• Expecting dim() to return total elements
• Misreading output as a single number

4. Identify the error in this R code snippet:

m <- matrix(1:9, nrow=3, ncol=3)
dim(m) <- c(2, 5)

medium

A. The code is correct and runs without error

B. You cannot assign dimensions that don't match the number of elements

C. dim() cannot be used on matrices

D. The matrix function is missing the byrow argument

Solution

1. Step 1: Check the original matrix size

   The matrix m has 9 elements (3 rows x 3 columns).

2. Step 2: Analyze the dimension assignment

   Assigning dim(m) <- c(2, 5) tries to reshape m to 2 rows and 5 columns, which requires 10 elements, not 9.

3. Final Answer:

   You cannot assign dimensions that don't match the number of elements -> Option B

4. Quick Check:

   New dims must match total elements [OK]

Hint: New dimensions must multiply to total elements [OK]

Common Mistakes:

• Ignoring element count mismatch
• Thinking dim() cannot be assigned
• Assuming byrow affects dimension assignment

5. You have a matrix m with dimensions 4x5. You want to reshape it into a matrix with 5 rows. Which of the following commands correctly changes the dimensions without losing data?

hard

A. dim(m) <- c(5, 4)

B. dim(m) <- c(5, 5)

C. dim(m) <- c(5, 3)

D. dim(m) <- c(5, 2)

Solution

1. Step 1: Calculate total elements in original matrix

   The original matrix has 4 rows x 5 columns = 20 elements.

2. Step 2: Check which new dimension matches total elements

   dim(m) <- c(5, 4) sets dimensions to 5 rows and 4 columns, which is 20 elements, matching original size.

3. Step 3: Verify other options

   Options B, C, and D have total elements 25, 15, and 10 respectively, which do not match 20, causing errors or data loss.

4. Final Answer:

   dim(m) <- c(5, 4) -> Option A

5. Quick Check:

   New dims must multiply to 20 [OK]

Hint: New rows x new cols = total elements [OK]

Common Mistakes:

• Choosing dimensions that don't match total elements
• Swapping rows and columns incorrectly
• Forgetting to check total element count