30 R Programming Basic Exercises for Advanced with Solutions

Master advanced R Programming skills with our comprehensive list of top 30 exercises. Dive into coding challenges that improve your understanding and proficiency in R Programming, setting a solid foundation for professional-level challenges. Start your journey to R Programming mastery today!

Learning Objectives:

Master advanced topics such as custom function creation, debugging, object-oriented programming, and writing reusable scripts. Apply R in real-world data science workflows, including modeling, automation, and package development.

Exercise Instructions:

Start with the first exercise and attempt to solve it before checking the hint or solution.
Ensure you understand the logic behind each solution, as this will help you in more complex problems.
Use these exercises to reinforce your learning and identify areas that may require further study.

1. Write an R program to compute the inverse of a non-singular matrix without using the solve function.

Required Input:

Matrix: 
 [,1] [,2]
[1,] 4 7
[2,] 2 6

Expected Output:

     [,1] [,2]
[1,]  0.6 -0.7
[2,] -0.2  0.4

Code In R

# Predefined structure compute_inverse <- function(matrix) { # Your logic here } # Call the function

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2. Create a custom function to calculate the R-squared value for a linear regression model.

Required Input:

Observed: c(3, -0.5, 2, 7)
Predicted: c(2.5, 0.0, 2, 8)

Expected Output:

R-squared: 0.9486

Code In R

# Predefined structure calculate_r_squared <- function(observed, predicted) { # Your logic here } # Call the function

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3. Implement a binary search algorithm in R to find an element in a sorted vector.

Required Input:

Vector: c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
Target: 7

Expected Output:

Element found at index: 7

Code In R

# Predefined structure binary_search <- function(vector, target) { # Your logic here } # Call the function

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4. Create a function to perform k-means clustering on a dataset and return the cluster centers.

Required Input:

Expected Output:

Cluster Centers:
  x y
1 9 8
2 2 3

Code In R

# Predefined structure perform_kmeans <- function(df, k) { # Your logic here } # Call the function

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5. Create a program to optimize a mathematical function using the optim function.

Required Input:

Function: f(x) = (x[1] - 3)^2 + (x[2] - 4)^2

Expected Output:

Optimized Result: x = 3 , y = 4

Code In R

# Predefined structure optimize_function <- function() { # Your logic here } # Call the function

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6. Write a function to calculate the determinant of a matrix recursively.

Required Input:

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

Expected Output:

Determinant: 0

Code In R

# Predefined structure recursive_determinant <- function(matrix) { # Your logic here } # Call the function

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7. Implement the program to find the intersection points of two functions using numerical methods.

Required Input:

Functions: f(x) = x^2 - 4, g(x) = x - 2

Expected Output:

Intersection Points: x = 2 , y = 0

Code In R

# Predefined structure find_intersection <- function(f, g, interval) { # Your logic here } # Call the function

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8. Write a program to calculate the gradient of a multivariable function numerically.

Required Input:

Function: f(x, y) = x^2 + y^2 
Point: (1, 2)

Expected Output:

Gradient: 2, 4

Code In R

# Predefined structure calculate_gradient <- function(f, point) { # Your logic here } # Call the function

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9. Create a function to perform LOOCV (Leave-One-Out Cross-Validation) on a dataset.

Required Input:

Data Frame: 
 x y
1 1 2
2 2 4
3 3 6
4 4 8

Expected Output:

Mean Squared Error: 0

Code In R

# Predefined structure perform_loocv <- function(df) { # Your logic here } # Call the function

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10. Write an R program to solve a system of linear equations using Gaussian elimination.

Required Input:

Equations: 
 2x + 3y = 8
 x + 2y = 5

Expected Output:

Solution: x = 1 , y = 2

Code In R

gaussian_elimination <- function(A, b) { # Your logic here } # Call the function

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