Data Visualization Analysis - Alexis Nguyen

 ChatGPT: ChatGPT + Edits I created the visual using ChatGPT by first going on the website and inputting in the textbox “Write an R Code using ggplot2 to make a scatterplot of Sepal.Length vs Sepal.Width from the iris dataset.” After sending it, it took a minute, then ChatGPT formed the code. What worked well was that the code ChatGPT gave me worked, and I got to see the graph. The challenges I encountered were that my R was not up to date, so I had to install ggplot2, and I did not know how to change the color of the scale names. In the end, I do think ChatGPT feels like a collaborator because it wrote the code, and all I did was go through it and make some adjustments.

  The code ran successfully. It was difficult at first because I don’t usually use Python, so I had to install it and figure out how it works. At first, I was typing the commands into the Python shell and the code didn’t run. After doing some research, I realized I needed to use Command Prompt instead to install everything correctly. Once I ran the code there, it worked and generated the ggplot output confirming the graph was created. I personally would not use this method because it was confusing to set up and the graph did not show up in the command prompt.

  I created a range frame plot in ggplot2 using the iris dataset. This Tufte-style visualization shows the relationship between sepal length and petal length. Looking at the plot, there is a positive trend where flowers with  longer sepals also have longer petals. I chose this style because it was one that I could clearly look at and understand what was happening. The plot is simple, not crowded with extra lines or colors, and it makes the relationship between the two variables easy to see.