Storms

Packages and Data

setwd("~/Desktop/Great Graphs/Great Graphs")
library(tidyverse)

df <- read.csv("storms19.csv") %>%
  select(STATE,YEAR,EVENT_TYPE,DAMAGE_PROPERTY,MONTH_NAME)




## Isolating 3 States
dfst <- filter(df,STATE %in% c("WISCONSIN","ILLINOIS","ARKANSAS")) %>%
  droplevels()


## Filtering events
evf <- filter(dfst,EVENT_TYPE %in% c("Flash Flood","Hail","Thunderstorm Wind","Winter Storm","Tornado")) %>%
  droplevels()

dd <- read.csv("storms19.csv") %>%
  select(STATE,YEAR,EVENT_TYPE,MONTH_NAME,CZ_NAME)

wid <- filter(dd,STATE %in% c("WISCONSIN")) %>%
  droplevels()


wif <- filter(wid,EVENT_TYPE %in% c("Hail","Heavy Snow","Thunderstorm Wind","Extreme Cold/Wind Chill","Lakeshore Flood")) %>%
  droplevels()

wic <- filter(wif,CZ_NAME %in% c("BAYFIELD")) %>%
  droplevels()

theb <- theme_bw() +
  theme(plot.title=element_text(face="bold",size=rel(1.5)),
        axis.title=element_text(face="bold",size=rel(1.25))) +
  theme(panel.grid.major.y=element_blank(),
        panel.grid.minor.y=element_blank()) +
  theme(plot.title=element_text(color="white"),
        plot.caption=element_text(color="white")) +
  theme(panel.grid.major=element_line(color="gray30")) +
  theme(plot.background=element_rect(fill="gray5")) +
  theme(panel.grid.minor=element_line(color="gray30",linetype="dotted")) +
  theme(axis.text=element_text(color="white"),
        axis.title=element_text(color="white")) +
  theme(panel.background=element_rect(fill="gray5"))

Number of Storm Types from North to South

This graph shows the amount of big storms as well as the different types of storms seen as you go from North to South. I used three states that were pretty much in a straight line stretching North to South. I wanted to try to illustrate how storms change as you get closer and further away from equator. This data comes from https://www.ncdc.noaa.gov which is a database for weather records dating back into the 1950’s. I chose to facet the graphs to clearly show each state and allow for easy comparisons across them all. I decided not to add axis titles to not overwhelm the viewer with easily understood information. I went for a dark theme so it was easier on the eyes.

clrs <- c("tomato3","cornflowerblue","forestgreen")

ggplot(data=evf,mapping=aes(x=EVENT_TYPE,fill=STATE)) +
  geom_bar() +
  facet_grid(rows=vars(STATE)) +
  theb +
  scale_x_discrete(name="") +
  scale_y_continuous(name="",breaks=seq(0,600,100),expand=expansion(mult=c(0,0.05))) +
  scale_fill_manual(values=clrs) +
  theme(legend.position="none") +
  coord_flip() +
  labs(title="Number of Large Storm Events in 2019
       ",
       caption="Source: https://www.ncdc.noaa.gov")

Bayfield County Storms

This figure represents storm data strictly from Bayfield County. I wanted to show how storm types change throughout the stretch of a single year and highlight the major storm types of each month. Unsurprisingly, the thunderstorms dominated the summer months and heavy snow and cold very heavily present throughout winter. It is interesting to note that there was no significant storms recorded for both April and June. I chose to put the months along to y-axis to make it easier to follow and not look too cluttered. I tried to choose colors for each storm type that represent the storm type that it is. Again I chose to use a dark background for simplicity and ease on eyes.

ss <- c("skyblue","lemonchiffon1","gray45","royalblue4","darkviolet")

ggplot(data=wic,mapping=aes(x=MONTH_NAME,fill=EVENT_TYPE)) +
  geom_bar() +
  scale_x_discrete(name="",limits=c("December","November","October","September","August","July","June","May","April","March","February","January")) +
  scale_y_continuous(name="",breaks=seq(0,10,1),expand=expansion(mult=c(0,0.05))) +
  scale_fill_manual(values=ss,name="Storm Events") +
  labs(title="Most Occuring Storm Events Per Month",
       subtitle = "Data From Bayfield County (2019)",
       caption="Source: https://www.ncdc.noaa.gov") +
  coord_flip() +
  theb +
  theme(plot.subtitle=element_text(color="white")) +
  theme(panel.background=element_rect(fill="gray5")) +
  theme(plot.background=element_rect(fill="gray9")) +
  theme(legend.background=element_rect(fill="gray5"),
        legend.title=element_text(color="white"),
        legend.text=element_text(color="white")) +
  annotate(geom="label",x="May",y=2.6, size=3,
           label="No results recorded") +
  annotate(geom="segment",x="May",xend="April",y=1.9,yend=1,
           arrow=arrow(length=unit(2,"mm"),type="closed",angle=13),color="white") +
  annotate(geom="segment",x="May",xend="June",y=1.9,yend=1,
           arrow=arrow(length=unit(2,"mm"),type="closed",angle=13),color="white")