One of the unique parts of Hurricanes in comparison to other natural disasters is that they are named, and are often more remembered by the public.

Since Hurricanes are deadly and cause great deals of destruction, I believe thereill be a negative trend in the popularity of a name in the years after the Hurricane hits.

This ranks the names of the most damaging hurricanes: https://www.ncdc.noaa.gov/billions/dcmi.pdf.

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Part one of this project will look at the names of the 6 worst hurricanes according to cost< adjusted damage to see if their is a negative correlation with future babynames.

Let’s load the ‘state names’ dataset from Kaggle, which can be found here: https://www.kaggle.com/kaggle/us-baby-names

library(readr)
StateNames <- read_csv("~/Desktop/StateNames.csv", 
    col_types = cols(Gender = col_character()))
View(StateNames)

First, let’s load the necessary packages.

library(tidyverse)
library(babynames)

This graph analyzes the number of babies names Katrina. While the named peaked in the 1980s, and was seemingly already on the decline, there was a very steep dip right in the mid 2000s range that seemingly was correlated to the hurricane.

babynames %>% filter (name %in% c( "Katrina" ) & sex =="F") %>% 
  ggplot(aes(year, n , color = name)) + geom_line()

Hurricane Andrew occurred in 1992, and we see a similar trend as this name peaks right in the early 1990s and then goes on decline.

babynames %>% filter (name %in% c( "Andrew" ) & sex =="M") %>% 
  ggplot(aes(year, n , color = name)) + geom_line()