LIS 4370 R Programming - Module 5 Assignment

For this assignment, I will be doing math with the following matrices:

For the most part, I used the explanation that was provided through the course announcements as a guide to doing this assignment. To begin, it is important to note that these two matrices above will not produce any inverses. 

We can check for inverses by using the det() function which gives us the determinant of a matrix. Entering into R det(A) and det(B), we can see in the console that det(A) will come out with zero while det(B) will give an error because det(B) is not a square matrix.

Now, what is a square matrix exactly? Well, it's a matrix with the same number of rows and columns which allows for one to add and multiply with it. Looking at the structure of the matrix when we transpose the matrices, we can immediately see that the rows greatly outnumber the columns whereas the A matrix does contain the same number of rows and columns (10 rows by 10 columns).

Moving on in the code, we are asked to create two vectors called a and b and since we intend to multiply them by the above matrices, we must make sure that the vector length matches the number of matrix rows. We then create the following vectors:

Moving onto the multiplication of the vectors and matrices, we can execute the following piece of code to perform the calculation:

The calculation is then saved to a table under the following variables:

result_1:

result_2:

The next step is to reassign the vectors a and b to equal the number of rows of the column for the corresponding matrix:

By executing the code, both a and b become of length 10.

Lastly, we are asked to multiply the matrices which can done through the following piece of code:

A taste of the output can be seen in the following image:

Here's a link to the code on GitHub:

Module 5 Code

~ Katie

LIS 4317 Visual Analytics - Module 5 Assignment

For this assignment, I will be working with a dataset containing data regarding average position and time. Following the part to whole design framework, I decided to focus on the part of  the data where time spans from half a second (0.5) to the maximum time recorded (3.8).

With a filtered dataset, I created a simple scatter plot with Plot.ly to graph the data.

Here's the plot:

Observing the plot, we can quickly see that as time increases, average position increases too. However, it appears that average position levels off around 1. More data is definitely needed to further understand why the data points seems to stop at this point.

Thinking about the Part to Whole Design Framework and how it applies to this plot, the first thing I appreciate is its ability to zoom in on an aspect of the data like time from 0.5 to 3.8. Sometimes, looking at the full picture of the data can be a bit overwhelming and you may miss key information. By taking a part of the data, we can clearly see that steady climb in position and time. 

Here's a link to the plot:

Module 5 Scatter Plot

~ Katie