LIS 4317

  Fuel Efficiency and Emissions Ratings in 2025 SmartWay Vehicles Research Question: How do SmartWay-certified vehicles compare to non-certified vehicles in terms of fuel efficiency and greenhouse gas emissions in model year 2025, and what role does fuel type play in these performance metrics? Short Summary: This project analyzes fuel efficiency and emissions scores among SmartWay-certified vehicles for Model Year 2025 using data from the U.S. Environmental Protection Agency (EPA). By comparing combined MPG performance, SmartWay labels, and greenhouse gas scores, the goal is to evaluate how well alternative fuel vehicles perform relative to traditional gasoline-powered counterparts. Problem Description: Amid growing environmental concerns and stricter emissions regulations, automotive manufacturers face increasing pressure to produce cleaner, more efficient vehicles. Consumers are also becoming more environmentally conscious, often turning to SmartWay certification labels to guid...

  This week, I explored R animations using Yihui Xie's (animation) package, as highlighted on Yihui's blog and Bryan's R-bloggers post. The goal was to create a basic animation and understand how visualization can evolve dynamically with data. I created a simple animated scatter plot where random points appear and change across 10 frames. The animation simulates data fluctuation over time, using the savegif() function. Each frame displays a new set of random points with vibrant colors for visual clarity. This kind of animation can be incredibly useful when demonstrating changes over time, such as stock trends, simulation outputs, or even the convergence of algorithms. What I found compelling was how easy it was to implement — a few lines of code produced an engaging visualization. The animation output was saved as a GIF, making it easy to embed in websites or presentations. This simple tool opens up creative possibilities for storytelling with data something static chart...

  This week, I explored the basics of Social Network Analysis by creating my first network visualization using RStudio and the ggnet2 package. Here’s what I learned during the process. What Worked: Installing the necessary libraries (GGally, network, sna, and ggplot2) was smooth. I was able to generate a basic network and customize it by adjusting node size, color, and edge width. What Didn’t Work: At first, I forgot to convert the matrix created by rgraph() into a network object using the network() function, which caused an error in ggnet2. Another issue I faced was with labeling nodes—I initially tried to use numbers instead of letters, which didn’t render appropriately until I used network.vertex.names(). Key Takeaways: "ggnet2" is a powerful yet beginner-friendly tool for network visualization in R. This would be useful for presenting real-world social networks like Twitter conversations or organizational structures. R Studio Code: # Install necessary packages (run on...

Dot Dash Plot in ggplot2 This graph titled "Dot Dash Plot in ggplot2" illustrates the trend of budget expenditures over a series of years ranging approximately from 1967 to 1977 . The x-axis represents the Year , while the y-axis indicates Budget Expenditures . The data points are connected using a dot-dash line , with each point clearly marked to emphasize individual yearly values. The overall trend shows a sharp increase in expenditures from 1967 to around 1970, followed by fluctuations with minor increases and decreases through the mid-1970s. The plot peaks just before 1976 and then declines slightly toward 1977. The use of a grey background and dashed lines provides a clear, visual contrast that helps highlight the progression and variability of the data over time.