Introduction

This web page features a plot created with Plotly. The goal of this assignment is to demonstrate the ability to host an interactive visualization on RPubs that includes a current date and reproducible code.

Interactive Weather Impact Analysis

In this section, we visualize the impact of severe weather events on public health. Unlike a static plot, you can hover over the bars below to see exact values, zoom into specific areas, or toggle categories on and off using the legend.

# Data Preparation
weather_data <- data.frame(
  Event = c("Tornado", "Excessive Heat", "Flood", "Flash Flood", "Lightning", "TSTM Wind"),
  Fatalities = c(5633, 1903, 470, 978, 816, 133),
  Injuries = c(91346, 6525, 6789, 1777, 5230, 1488)
)

# Creating the interactive Plotly Bar Chart
fig <- plot_ly(weather_data, 
               x = ~reorder(Event, -Injuries), 
               y = ~Injuries, 
               type = 'bar', 
               name = 'Injuries',
               marker = list(color = 'rgba(255, 153, 51, 0.7)',
                            line = list(color = 'rgba(255, 153, 51, 1.0)', width = 1.5)))

fig <- fig %>% add_trace(y = ~Fatalities, 
                         name = 'Fatalities',
                         marker = list(color = 'rgba(204, 0, 0, 0.7)',
                                      line = list(color = 'rgba(204, 0, 0, 1.0)', width = 1.5)))

fig <- fig %>% layout(
         title = "Health Impact of Severe Weather (Total Counts)",
         xaxis = list(title = "Weather Event Type"),
         yaxis = list(title = "Number of Persons Affected"),
         barmode = 'group',
         hovermode = "x unified")

fig

Conclusion

The interactive chart above highlights that while Tornadoes cause the highest number of injuries, Excessive Heat remains a significant threat to life. Using Plotly allows users to explore these data points in greater detail than standard static visualizations.

Date of Document Creation: r format(Sys.Date(), “%B %d, %Y”)