Module 4: Programming Structue

 



We can see in the first image is a boxplot that compares the patient's blood pressures based on the assessment made by the first doctor, which was categorized as good or bad. The plot shows that patients who were rated as having a higher assessment generally have more variable blood pressure compared to those who were rated as good. 

The histogram in the second image displays the overall distribution of the blood pressure values across all the assessed patients. Most of the patients fall within a safe range of blood pressure, while some have very high readings. These higher assessments may need closer medical care. 

Together, the boxplot and the histogram provide a good summary of both the different assessments by the first doctor as well as the overall pattern in blood pressure measurement. The R code used for this assignment and to generate these visualizations has been posted on the GitHub readme, and the link will be attached.

Comments

Post a Comment

Popular posts from this blog

Evaluating the function myMean in RStudio

Image
 After running the given code in the assignment, an error is prompted that reads object assignment not found. The function does not work because it references assignment, and someData that were not previously defined inside the function. We passed the function assignment2, but the calculations were not done since R could not find the variables, since they were labeled as assignment2 and someData. After fixing those two and replacing assignment and someData with assignemnet2 we get the proper mean. 
Read more

Module #8 Assignment

For this week's assignment, I worked with the given dataset containing four variables for a set of students. The dataset included both males and females, and it also included ages, grades, and names. The assignment tasked me with importing the file into R, calculating the mean grade by sex, filtering the dataset for names containing the letter "i", and export the results into a csv file.  The first step I did was importing the dataset into R using the read.table(). After importing the data, I used the ddply() function from the pylr packaged that we were tasked with installing for this assignment to group the dataset by sex and calculate the average of the grade column. This summarized the comparison between male and female students instead of having to look through them individually. After generating the mean, we get the following output:  Sex Grade_Average 1 Female 86.9375 2 Male 80.2500 After that I converted teh dataset into a datarame and filtered u...
Read more

Module 5: Doing Math

For this assingment we are tasked with solving the inverse of a matrix using the given values. As the hint stated, we can start with the following for step 1: Step 1: In this step, we are creating the matrices. A <- matrix(1:100, nrow=10)   B <- matrix(1:1000, nrow=10) Step 2: After creating the matrices, we can use the dim function to check their shape. > dim(A) [1] 10 10 > dim(B) [1] 10 100 After running this function, we can see that matrix A is a square 10x10 and matrix B is not becasue its 10x100. Step 3: Next, we can use the det() to find the determinant of matrix A  > det(A) [1] 0 After running the det() we can see that the result is 0 which means the matrix is singular which has no inverse. Step 4: I attempted to double check and try finding the inverse using the solve() function > solve(A) Error in solve.default(A) : Lapack routine dgesv: system is exactly singular: U[6,6] = 0 Step 5: I also attempted to double check matrix B even though is not ...
Read more