Achim Rumberger 27 Januar 2018
I would like to express my thanks to the contributor of the HURDAT package ("https://rdrr.io/cran/HURDAT/"). Compared to the original data from https://www.nhc.noaa.gov/data/hurdat/ it got so much easier to work with the data.
#libraries
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(ggthemes)
library(ggmap)
library(htmlwidgets)
library(gridExtra)##
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## combine
library(HURDAT)
library(lubridate)##
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## date
library(splines)
library(plotly)##
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## wind
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source("classifiy_hurricianes.R")The data span the time from 1851 to 2016. We will have almost 80k observations with 24 variables. It will take a while to read the inital data with get_hurdat function. Therefore I will save the data for more easy access in the future. If you run this code the first time please uncomment the lines with "get_hurdat" and "saveRDS".
#data <- get_hurdat(basin = c("AL", "EP"))
#saveRDS(data, "data/hurricanesALEP")
data <- readRDS("data/hurricanesALEP")data$YEAR <- year(data$DateTime)
data$MONTH <- month(data$DateTime)
data$DAY <- day(data$DateTime)
summary(data %>% select(YEAR, MONTH, DAY, Wind, Pressure))## YEAR MONTH DAY Wind
## Min. :1851 Min. : 1.000 Min. : 1.00 Min. : 10.00
## 1st Qu.:1939 1st Qu.: 8.000 1st Qu.: 8.00 1st Qu.: 30.00
## Median :1975 Median : 9.000 Median :16.00 Median : 45.00
## Mean :1964 Mean : 8.517 Mean :15.86 Mean : 51.57
## 3rd Qu.:1997 3rd Qu.: 9.000 3rd Qu.:23.00 3rd Qu.: 65.00
## Max. :2016 Max. :12.000 Max. :31.00 Max. :185.00
## NA's :338
## Pressure
## Min. : 872.0
## 1st Qu.: 986.0
## Median :1000.0
## Mean : 993.5
## 3rd Qu.:1007.0
## Max. :1024.0
## NA's :43473
# first plot
df = data %>%
group_by(YEAR) %>%
summarise(Distinct_Storms = n_distinct(Key))
p = ggplot(df, aes(x = YEAR, y = Distinct_Storms)) + theme_stata()
p + geom_line(size = 1.1) +
ggtitle("Number of Storms Per Year") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method='loess', se = FALSE, formula = y ~ x) +
ylab("Storms")
Create a new variable which classifies the hurricanes according to windspeed.
data$CATEGORY <- sapply(data$Wind, classifyHurricane)#by category
df = data %>%
filter(grepl("H", CATEGORY)) %>%
group_by(YEAR,CATEGORY) %>%
summarise(Distinct_Storms = n_distinct(Key))
df$CATEGORY = factor(df$CATEGORY)
p = ggplot(df, aes(x = YEAR, y = Distinct_Storms, col = CATEGORY)) + theme_stata()
p + geom_line(size = 1.1) +
scale_color_brewer(direction = -1, palette = "Dark2") +
ggtitle("Number of Storms Per Year By Category (H)") +
theme(plot.title = element_text(hjust = 0.5)) +
facet_wrap(~ CATEGORY, scales = "free_x", ncol = 1) +
geom_smooth(method = 'lm', se = FALSE, col = 'black') +
theme(axis.text.x = element_text(hjust = 1, angle=45), legend.position = 'none') +
ylab('Number of Storms') +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))
## Make a distinct dataset I also will filter the data. A hurricane in its lifecycle will go through several stages, from tropical storm to a class H1 hurricane to H2 to H1, tropical storm. I want to count each storm only once with its highest ratings. On my laptop this takes a while, so again, I will save the data for further analysis. This will show us, that in the time frame from 1851 to 2016 a total of 2900 storms have been recorded. If you run this code the first time please uncomment the lines with "highestClassification4Storm" and "saveRDS".
#distinctStormData <- highestClassification4Storm(data)
#saveRDS(distinctStormData, "data/hurricanesALEPAAA")
distinctStormData <- readRDS("data/hurricanesALEPAAA")
summary(distinctStormData)## Key YEAR CATEGORY Wind
## Length:2900 Min. :1851 Length:2900 Min. : 0.00
## Class :character 1st Qu.:1942 Class :character 1st Qu.: 45.00
## Mode :character Median :1974 Mode :character Median : 60.00
## Mean :1963 Mean : 67.45
## 3rd Qu.:1996 3rd Qu.: 90.00
## Max. :2016 Max. :185.00
df = distinctStormData %>%
group_by(YEAR) %>%
summarise(Distinct_Storms = n_distinct(Key))
p = ggplot(df, aes(x = YEAR, y = Distinct_Storms)) + theme_stata()
p + geom_line(size = 1.1) +
ggtitle("Number of distinct Storms Per Year") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_smooth(method='loess', se = FALSE, formula = y ~ x) +
ylab('Number of Storms') +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))
## Plot disitinct hurricanes by category
df = distinctStormData %>%
filter(grepl("H", CATEGORY)) %>%
group_by(YEAR,CATEGORY) %>%
summarise(Distinct_Storms = n_distinct(Key))
df$CATEGORY = factor(df$CATEGORY)
p = ggplot(df, aes(x = YEAR, y = Distinct_Storms, col = CATEGORY)) + theme_stata()
p + geom_line(size = 1.1) +
scale_color_brewer(direction = -1, palette = "Dark2") +
ggtitle("Number ofd disitinct Storms Per Year By Category (H)") +
theme(plot.title = element_text(hjust = 0.5)) +
facet_wrap(~CATEGORY, scales = "free_x", ncol = 1) +
geom_smooth(method = 'lm', se = FALSE, col = 'black') +
theme(axis.text.x = element_text(angle=45), legend.position = 'none') +
ylab('Number of Storms') +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))
- Noteworthy to me seem to be two observations:
- the marked increase in overall storm activity from the 1950s
- the increase in category 5 hurricanes. What was once a very event, from the 1990s onward the occurence of a major hurricane became a regular feature of the hurricane season.