this is file describing how we can do this

This project explores patterns in vehicle insurance claims using a real-world dataset. Through data cleaning, visualization, and statistical exploration in R, I analyzed claim frequency and severity across variables such as vehicle usage, type, manufacturer, insured values, and premiums. Key insights include identifying high-risk vehicle categories, understanding the distribution of claim amounts (on a log scale), and comparing average premiums and claims by usage. The analysis demonstrates how exploratory data work can support risk assessment and underwriting decisions, aligning with core principles of property and casualty insurance. Tools used: R, dplyr, ggplot2, plotly, tidyr

This interactive plot shows how execution methods in the United States vary based on the number of victims involved in a case. The height of each bar represents the total number of executions within a given victim count group: one victim, two to four victims, or five or more. Overlaid on these bars are two lines that display the proportion of executions carried out by lethal injection and by electrocution within each group. As the number of victims increases, the proportion of electrocutions also rises. While lethal injection remains the most common method overall, offenders with a higher victim count are more likely to be electrocuted. This pattern suggests that the method of execution may be influenced by the perceived severity of the crime, as indicated by the number of victims. Hovering over each section of the plot reveals specific values, offering a closer look at how the proportions and counts change across victim groups.

Final project for FISH 546