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Final Project: Visual Analytics

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For this project, I was not sure which dataset to use, so I went through the R built-in datasets and chose the ToothGrowth dataset. It was easy to work with, and it has enough observations for me to make different visuals and compare patterns. The main question I wanted to go over in this project was how different supplement types and dose amounts affect tooth length. I also wanted to see if there was a big difference when increasing the doses. My goal was to understand the patterns in the ToothGrowth dataset. Related works I looked back on was the mtcars dataset we worked on for some of the assignments. We used boxplots, scatterplots, and other visuals to compare variables, which helped me decide which graphs to use for this project and gave me ideas for the visuals I made for the ToothGrowth dataset. For my solution, I used RStudio to make all the visuals and used a dataset that was already built into R. I created three graphs that helped me explore the dataset further. The first gra...
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Assignment: Using ChatGPT to Generate and Share Visualizations

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 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.
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Assignment # 12 using Python with netwokxx and plotnine

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  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.
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Module # 11 assignment

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  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.
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Module # 10 assignment

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 baser ggplot2 This is a bar chart: each bar shows how many hot dogs were eaten that year, and the color indicates whether a new record was set. The chart is simple and easy to read, as you can tell from the colored bars that, as years have gone by, a new record is being set. The headline is accurate, as it tells viewers what the chart is about. No, the visualization does not lie; it is consistent and shows how many hot dogs were eaten that year. There are no distractions on the graph, it's clean and uses a single bold color to indicate when records are being set. I like this graph because it's simple and easy to read. This style is a time-series line chart that shows the unemployment rate over time in the US. The graph looks clean, and it is easier to understand and read. There is no headline, but the chart is about unemployment over time. The line shows patterns and cycles in the unemployment rate. The visualization is accurate. The data uses a color scale to show the unempl...
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Module #9: Visualizing Multivariate Data with ggplot2 & Corrgram

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  I used the Iris, which has many flowers and includes details on sepal length and width, as well as petal length and width. I used this dataset because it measures different flower types, such as setosa, versicolor, and virginica.  My visualization measures sepal length and compares it across the three species of iris flowers. The boxplot shows that setosa has the smallest sepal length, virginica has the largest, and versicolor is in between. This visualization helps tell the differences and similarities.  Yes, the boxplot is a multivariate visualization because it compares sepal length across three species. Making it easy to compare them side by side.  In my boxplot, I used alignment by keeping everything easy to read and compare. I also used contrast by using a gray background with red boxes so the boxplots would stand out, and I used balance by spacing the boxplots evenly so the chart looks neat and easier to read and understand. 
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Module #7: Distribution Analysis in R

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  I used the mtcars set to make a bar chart to showcase how many cars have either 4, 6, or 8 cylinders. A bar plot helps compare groups clearly, so I decided to use it to compare how many cylinders cars have. The chart shows that most cars have 8 cylinders, while fewer have 4. Matching Few's recommendations, I took in note to make the visuals colorful and easy to understand and not too crowded. I also added the title and labels to let readers know what they are looking at, so they do not have to assume which cylinder is which. I agree with Few's recommendations because if I were to read a visual distribution, I would like to know what I am looking at, rather than having little to nothing that explains what it is about, and so much information that I cannot comprehend what is going on.
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