LIS 4317

  This is the basic graphic that I created in R Studio using the dataset "mtcars" to show the relationship between "Miles Per Gallon" and "Horsepower." My graphic aligns with Few's perspective by: Emphasizing clarity and simplicity. Avoids unnecessary decorations that detract from the information. The scatter plot follows the principle of (Practical Data Visualization) by using minimal themes and labeling the axes correctly. My graphic aligns with Yau's perspective by: Focuses on engaging narratives through data visualization. Encourages creative but effective graphic design. Although the scatter plot is functional, my graphic lacks deeper storytelling elements, such as color coding by another variable.

  For this assignment, I used Plotly to develop a graphic that combined the Part-to-Whole and Ranking design aspects to show the relationship between Average Position and Time. My objective was to employ  plotly to better understand the interactions between the two main variables in the dataset, Average Position and Time, over a specific period. The average position (orange) and time (green) are distinguished by various colors in the bar chart. The y-axis measures the values connected to each category, whereas the x-axis depicts discrete points or categories. Part-to-Whole Approach: This technique makes it easier to see how various components fit together to form a broader dataset. Here, we can compare Time and Average Position across various x-axis values while preserving a feeling of proportion thanks to the stacked bars. This method has drawbacks when comparing absolute numbers directly because overlapping bars occasionally make the interpretation less clear. Ranking Interp...

Hello, Here is my Assignment #4 Times Series Visualization. I only used 6 of the 12 available variables in this dataset that include: Primary USA City Year Vehicle Revenue Miles Vehicle Revunue Hours Ridership Collision with Motor Vehicles The insights gained from my times series visualization are: Ridership trends over time for each city Comparison of service miles and hours across years. Collision data visualized as bubble size – larger bubbles mean more collisions. City-based filtering allows users to focus on specific locations. Some of the challenges I faced while working on Tableau was trying to figure out how to dual axis the data, and allow it for visually look correct. I played around a lot with what should be placed on what axis, and how I wanted to color coordinate the data by city. Overall, this was a fun experience and I enjoy the opportunity to try and create new types of data visualization products.

Hello, I used my original creation of the black dots that indicated the locations of MLB Stadiums by grid coordinates, and decided to input the team logo by location to help visualize what team plays in what state. BEFORE: AFTER: