Data description

This data set includes information about the postitions and attributes of 198 tropical storms. The observations were measured every six hours for the duration of the storm. In total there are 10,010 observations from the 198 storms. This data is a sub set of the National Oceanic and Atmospheric Administration (NOAA) Atlantic hurricane database best track data.

Variable Variable Descrption
name Storm name
year Year of the report
month Month of the report
day Day of the report
hour Hour of report in (UTC)
lat Latitude of the storm center
long Longitude of the storm center
status Storm classification
category Saffir_Simpson storm category
wind Storm’s maximum sustained wind speed (in knots)
pressure Air pressure at the storm’s center (in millibars)
ts_diameter Diameter of the area experiencing tropical storm strength winds (34 knots or above)
hu_diameter Diameter of the area experiencing hurricane strength winds (64 knots or above)

Data visualizations

# Write R code here to create your first plot
storms %>%
  ggplot(aes(pressure, wind)) +
  geom_point(aes(color = status))  +
  theme_bw() +
  labs(
    y = 'Maximum Sustained Wind Speed (in Knots)',
    x = 'Air Pressure (in Millibars)',
    fill = 'Storm Classification'
  )

This plot shows the relationship between air pressure and wind speed, and the status of each storm. The plot demonstates a clear relationship between air pressure and wind speed. As the pressure increases, the wind speed and decreases and when the pressure decreases the wind speed increases, demonstrating an inverse relationship. Since storms are classified based on their wind speed it is not suprising that there are clear lines between the classifications in terms of wind speed. However, the graph demonstrates that while pressue and wind speed are related there there is not as clear distinctions between storm classifications when it comes to pressure.

# Write R code here to create your second plot

storms %>% 
  group_by(month) %>%
  ggplot(aes(as.factor(month))) +
  geom_bar() +
  theme_bw() +
  labs(
    x = "Month",
    y = "Number of Storms"
  )

This plot displays the month that have the most reports filed for hurricanes. It shows that September has the highest number of reports. There are also zero reports February and March, causing them not to be featured on this graph. The graph also shows that the months surrouding september also have a higher number of reports while the months furthest from september have a much lower number of reports.