Module # 5 Doing Math

Issaiah Jennings 
Module # 5 Doing Math

In this assignment, I worked with two matrices, A and B. Matrix A is a 10x10 square matrix, so I calculated its determinant. If the determinant was not zero, I found the inverse using the solve() function. If the determinant was zero, I returned a message saying the matrix is singular and doesn't have an inverse. Matrix B is a 10x100 matrix, which is not square, so it doesn't have a determinant or an inverse. I handled both cases by checking the conditions before trying to compute the inverse and printed appropriate messages.



> # Define matrix A
> A <- matrix(1:100, nrow=10)
> 
> # Define matrix B
> B <- matrix(1:1000, nrow=10)
> 
> # Compute determinant of A
> det_A <- det(A)
> 
> # Check if A is invertible (det(A) ≠ 0)
> if (det_A != 0) {
+   inv_A <- solve(A)  # Compute inverse of A
+ } else {
+   inv_A <- "Matrix A is singular and does not have an inverse."
+ }
> 
> # Print results
> print("Determinant of A:")
[1] "Determinant of A:"
> print(det_A)
[1] 0
> 
> print("Inverse of A (if applicable):")
[1] "Inverse of A (if applicable):"
> print(inv_A)
[1] "Matrix A is singular and does not have an inverse."
> 
> # Explanation of B:
> print("Matrix B is not square (10x100), so it does not have an inverse.")
[1] "Matrix B is not square (10x100), so it does not have an inverse."
> 


https://github.com/Ijennin/R-Functions/blob/8bc9f578cc6855243df1f4255c4fb8da86713b15/Module%20%23%205%20Doing%20Math

Comments

Post a Comment

Popular posts from this blog

Final project - create your own R Package

Final project - create your own R Package Issaiah Jennings  In this blog post, I’ll walk you through how I created an R package, combinePredictR, to analyze the 2019 NFL Combine data. This package includes functions for cleaning, visualizing, and summarizing key player statistics, such as the 40-yard dash time. Along the way, I’ll explain the steps I took, the challenges I encountered, and how I addressed them.  Step 1: Setting Up the Package I started by setting up my R package using usethis and devtools, which are great tools for package development in R. Here's a quick rundown of what I did: I used the usethis::create_package() function to initialize the basic structure of the package. I created a new R script for each function: clean_combine_data.R: For data cleaning. plot_40yd_by_position.R: To plot the 40-yard dash times by player position. summarize_40yd_by_position.R: To create a summary table showing average, minimum, and maximum times by position. Step 2: Cleani...
Read more

Module # 1 assignment(R)

Hey there!  Welcome to my blog about R Programming! I'm really excited to share my journey with you. Don’t forget to check out my GitHub for some cool projects I've been working on. Enjoy exploring! github.com/Ijennin
Read more

Module # 4 Programming structure in R

Image
The following data was collected by the local hospital. This data set contains 5 variables based on observation of 8 patients. In addition to the measurements of the patients checking in to the hospital that night, this data provides the patients' histories regarding the frequency of their visits to the hospital in the last 12 months.This data displays the measurement of blood pressure, first assessment by general doctor (bad=1, good =0) titled "first," the second assessment by external doctor (called "second"), and the last row provides the head of the emergency unit's decision regarding immediate care for the patient based on the values 0 or 1 (low = 0, high =1). The names of your variables are as follows: "Freq","bloodp","first”, " second”, ”finaldecision” > # Create vectors for each variable > Frequency <- c(0.6, 0.3, 0.4, 0.4, 0.2, 0.6, 0.3, 0.4, 0.9, 0.2) > BP <- c(103, 87, 32, 42, 59, 109, 78, 205, 135, 176...
Read more