Module 7 Assignment

 Hi! For this visualization I decided to use the AirQuality dataset that I have used before and wanted to explore with new visualization insights. As I was reading the provided article by Nathan Yau I was immediately drawn in by the beanplot. His claim that he has never and will never use this visualization made it an incredibly attractive option to use for this assignment. I am an avid defender of the beanplot now that I know about it and have produced this creation:

I enjoy the shape of the data and how it is expressed by the tick marks. It looks nearly artistic and I think it is one of the most unique formats to reflect data. I also enjoyed the Violin and Density plots however I could not use those for my data unfortunately despite very much wanting to do so. 

Module #6 Assignment

 The goal of this assignment was to illustrate data graphically within R. I decided two routes for this; one as demonstrated within the module and an alternative that uses the ggplot2 package. My first visualization was a histogram containing a comparative normal distribution curve. 

These lines of code produced the following output:

Secondly, I wanted to use ggplot since it's relevant to my other coursework and wanted to apply it outside of a single class. 

I used these parameters and instructions to create a plot that compared the vehicle displacement (x) against that of its weight (y). I chose the shape and colors based off of nothing besides which of the options and sizes I felt were appropriate and appealing to me. The result is as such:

Based on this graphic, it is evident that a strong, positive correlation is evident. I consider these two designs to be in-line with the ideas posited by Few and Yau. The principles of purpose, execution, and effect are intact and can be verified by recognizing the attributes of the graphics themselves. 

Module #5 Assignment

 This assignment was an exploration of a different visual/graphics software. Plotly is an online application that is available to students for free and provides an opportunity to import data to visually articulate. Similar to a Adobe and Tableau, Plotly is designed to make professional-looking graphical presentations of data. Plotly offers a few unique versions; including 3D, which I was excited to try out but was stumped since I am new to the software and wasn't sure how to input the data in such a complex way yet. The representation I chose was a simple line chart with a personalized style. Point shape, size, color, and graph color were two of the primary methods in which the software can manipulate the graphical manifestation of the data. I went on a spree of throwing any color and combination together for fun but ended up o the one provided below for its professional look. 

There is also an option to insert error bars, In this case they do not provide very much useful information or significant reflection of the data, but it is a very important feature for larger data sets and I can imagine that many people have found them to be beneficial to their work. 

Module #4 Assignment

 This assignment had a primary focus on the exploration of Tableau and its functions. Using the provided data from Data.gov, I went about throwing the variables together in different combinations to see the results and how the application might express the data visually. One of the visualizations I liked was the relationship between reported city and characteristics of the incident. The characteristics I chose to visualize were the "collisions with motor vehicles", "collision with person",  "vehicle revenue hours", "vehicle revenue miles", and "ridership". I wanted to understand if location and city had any meaningful impact on these variables. The area chart below is reflective of the reported number of accidents and their respective characteristics. The chart is much larger than this image shows, and continues when scrolling up/down and to the right. 

In addition, I wanted to see if the bar graph would be better equipped to represent the relationships in the data. 

This bar graph illustrates the data in a more organized, however inefficient manner in my opinion. There are a number of Null values and instances where the number of accidents make scaling the relationship to others difficult. A benefit, however, is that there is a clear and distinct differences in the number of reported accidents between the cities, which are best reflected in bars and the use of color, width, and height of the bar graph.

I explored a lot more within Tableau to get a better understanding of the software before and after producing these designs; it is very user-friendly in a lot of ways and I think some additional practice with more data will be incredibly useful in the future.