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Final

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       The dataset I chose was found here https://catalog.data.gov/dataset/electric-vehicle-population-data and was chosen due to its popularity, and its similarity to several assignments this year.      I was attempting to solve a few questions I had about electric cars. I wanted to know what automaker was making the highest number of electric vehicles, if the range the vehicles can operate in has increased, and where electric vehicles were most being used. The related works to this that inspired my choice happened to be all the mtcars plots I did this year. We focused on MPG several times and I wanted to visualize how electric cars have improved in that department as well. My technological approach was to chart out the different ranges of electric vehicles and how that number has changed over time. I like the simplicity and visual appeal of Tableau, so I used that to construct the project. I discovered that tesla has a massive domination on the elect...
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week 12

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  Oh boy this was a bit of a pain, I wasn't sure at all how certain lines were setup and had to use chat gpt to learn how to run -pip files and get all the extensions added. Once that was done that code was trying to save things in appdata so I had to research how to change the directory where things were stored in order to obtain this image. I think having some form of knowledge about this now and the purpose social networking visualizations can have, I could see myself using this in the future. I would have to look up exactly how to reformat it however as I don't think this process will remain in my mind. 
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Module 11

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  Marginal histogram scatterplot
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Module 10

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 This week was several different methods of using GGplot to get visuals. Personally I like the colored bar chart but each has its purpose. The patterns each show allow someone at a glance to be able to clearly see what the data represents. Colors when properly used can highlight key information and the heights of lines and bars can indicate volume and facilitate comparisons between data points.
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Module 9

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  I used mtcars just for simplicities sake. The images show mile per gallon vs weight, and also colorizes the number of cylinders in the engine as well as its horsepower. I think this works well as you can clearly visualize most the major differences between cars and the impact it has on MPG.  The colors contrast each other nicely, allowing the visual to be easily readable with little confusion. There is balance in the display as the opaqueness of the circles overlapping still allows you to clearly identify each individual data point. The alignment of the graph is also scaled consistently, not skewing the data in a way that could be misrepresentative of the data.
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Module 8

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  What patterns or relationships did you observe? weight and horsepower have a negative effect on MPG. The lower the horsepower and weight the more mpg the car gets How did your use of grid layout or facets enhance interpretation? Grid layout lets you look at different information at the same time, providing information that can be compared instantly rather then by scrolling. In your opinion, how do Few’s recommendations help or hinder your design choices? Using grid layouts aligns with Few’s recommendations, because they present multiple comparisons in an organized and clean manner without overloading any of the plots. 
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Module 7. Assignment: Visualizing Distributions in R

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  This distribution shows the distribution of horsepower in different vehicles in the mtcars dataset. This test was mostly done using 100 horsepower cars. There were very few high horsepower cars in the dataset which could lead to some biases in the data.  I did a separate visual to see how many cylinders were in each vehicle tested. This was a bit more even then I expected seeing how low the horsepower count was.  Each of these charts is simple to read and follows the principals by being simple, uncluttered, neutral colored, and they are not intentionally misleading.
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