LIS 4317 Final Project: Fuel Economy Data from the U.S Dept. of Energy

 

LIS 4317 Final Project: Fuel Economy Data from the U.S Dept. of Energy

Investigating the association between vehicle features and CO2 emissions in a dataset comprising fuel efficiency data is the issue statement for my final project. The objective is to ascertain the extent to which CO2 emissions differ among various vehicle classes. In order to evaluate the effects of automobiles on the environment and pinpoint possible areas where fuel efficiency regulations could be strengthened, further investigation is essential. The idea suggests that specific vehicle classes, like larger cars or ones with bigger engines, might have more CO2 emissions than other car classes.

This issue is set within the larger framework of transportation research and environmental sustainability. The relationship between vehicle characteristics and emissions, particularly CO2 emissions, has been well studied in the past. Numerous techniques, such as statistical evaluations and graphics, have been used to investigate these connections. For example, to see how emissions vary amongst various car models, researchers have employed bar charts, box plots, and scatter plots. Additionally, research in this field has looked into how regulations and technology developments can lower the emissions from vehicles.

A bar plot showing the distribution of CO2 emissions by vehicle class is made using a visual analytics technique in order to solve the issue. To get the average CO2 emissions for each vehicle type, the dataset is first analyzed. Subsequently, the box plot is created using the R ggplot2 library, where the y-axis represents CO2 emissions and the x-axis represents vehicle class. This method facilitates insights into possible trends or patterns by providing an easy-to-understand graphic representation of the variations in CO2 emissions across various vehicle classes.

Overall, this solution offers a structured and informative approach to examining the relationship between vehicle characteristics and CO2 emissions, contributing to the broader understanding of environmental sustainability in the automotive industry. The visual representation accurately portrays the data without distortion, resulting in a low lie factor. There are no unnecessary or excessive decorative elements in the visualization, ensuring clarity and focus on the data. The visualizations adheres to best practices outlined by Stephen Few, such as clear labeling, appropriate use of color, and minimal distractions. It fits into the best practices outlined in our textbook for effective visual analysis.