Introduction

Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.

Data Download

The following code is used to download and read the data:

library(R.utils)
## Warning: package 'R.utils' was built under R version 4.0.2
## Loading required package: R.oo
## Warning: package 'R.oo' was built under R version 4.0.2
## Loading required package: R.methodsS3
## Warning: package 'R.methodsS3' was built under R version 4.0.2
## R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.
## R.oo v1.24.0 (2020-08-26 16:11:58 UTC) successfully loaded. See ?R.oo for help.
## 
## Attaching package: 'R.oo'
## The following object is masked from 'package:R.methodsS3':
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## R.utils v2.10.1 (2020-08-26 22:50:31 UTC) successfully loaded. See ?R.utils for help.
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## Attaching package: 'R.utils'
## The following object is masked from 'package:utils':
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#bunzip2("repdata_data_StormData (1).csv.bz2", "StormData.csv")
df <- read.csv("StormData.csv")

Data Processing

Health Impact To evaluate the health impact, the total fatalities and the total injuries for each event type (EVTYPE) are calculated. The codes for this calculation are shown as follows.

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## The following objects are masked from 'package:base':
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df.fatalities <- df %>% select(EVTYPE, FATALITIES) %>% group_by(EVTYPE) %>% summarise(total.fatalities = sum(FATALITIES)) %>% arrange(-total.fatalities)
## `summarise()` ungrouping output (override with `.groups` argument)
head(df.fatalities, 10)
## # A tibble: 10 x 2
##    EVTYPE         total.fatalities
##    <chr>                     <dbl>
##  1 TORNADO                    5633
##  2 EXCESSIVE HEAT             1903
##  3 FLASH FLOOD                 978
##  4 HEAT                        937
##  5 LIGHTNING                   816
##  6 TSTM WIND                   504
##  7 FLOOD                       470
##  8 RIP CURRENT                 368
##  9 HIGH WIND                   248
## 10 AVALANCHE                   224
df.injuries <- df %>% select(EVTYPE, INJURIES) %>% group_by(EVTYPE) %>% summarise(total.injuries = sum(INJURIES)) %>% arrange(-total.injuries)
## `summarise()` ungrouping output (override with `.groups` argument)
head(df.injuries, 10)
## # A tibble: 10 x 2
##    EVTYPE            total.injuries
##    <chr>                      <dbl>
##  1 TORNADO                    91346
##  2 TSTM WIND                   6957
##  3 FLOOD                       6789
##  4 EXCESSIVE HEAT              6525
##  5 LIGHTNING                   5230
##  6 HEAT                        2100
##  7 ICE STORM                   1975
##  8 FLASH FLOOD                 1777
##  9 THUNDERSTORM WIND           1488
## 10 HAIL                        1361

Economic Impact

The data provides two types of economic impact, namely property damage (PROPDMG) and crop damage (CROPDMG). The actual damage in $USD is indicated by PROPDMGEXP and CROPDMGEXP parameters. According to this link, the index in the PROPDMGEXP and CROPDMGEXP can be interpreted as the following:-

H, h -> hundreds = x100 K, K -> kilos = x1,000 M, m -> millions = x1,000,000 B,b -> billions = x1,000,000,000 (+) -> x1 (-) -> x0 (?) -> x0 blank -> x0

The total damage caused by each event type is calculated with the following code.

df.damage <- df %>% select(EVTYPE, PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)

Symbol <- sort(unique(as.character(df.damage$PROPDMGEXP)))
Multiplier <- c(0,0,0,1,10,10,10,10,10,10,10,10,10,10^9,10^2,10^2,10^3,10^6,10^6)
convert.Multiplier <- data.frame(Symbol, Multiplier)

df.damage$Prop.Multiplier <- convert.Multiplier$Multiplier[match(df.damage$PROPDMGEXP, convert.Multiplier$Symbol)]
df.damage$Crop.Multiplier <- convert.Multiplier$Multiplier[match(df.damage$CROPDMGEXP, convert.Multiplier$Symbol)]

df.damage <- df.damage %>% mutate(PROPDMG = PROPDMG*Prop.Multiplier) %>% mutate(CROPDMG = CROPDMG*Crop.Multiplier) %>% mutate(TOTAL.DMG = PROPDMG+CROPDMG)

df.damage.total <- df.damage %>% group_by(EVTYPE) %>% summarize(TOTAL.DMG.EVTYPE = sum(TOTAL.DMG))%>% arrange(-TOTAL.DMG.EVTYPE) 
## `summarise()` ungrouping output (override with `.groups` argument)
head(df.damage.total,10)
## # A tibble: 10 x 2
##    EVTYPE            TOTAL.DMG.EVTYPE
##    <chr>                        <dbl>
##  1 FLOOD                 150319678250
##  2 HURRICANE/TYPHOON      71913712800
##  3 TORNADO                57352117607
##  4 STORM SURGE            43323541000
##  5 FLASH FLOOD            17562132111
##  6 DROUGHT                15018672000
##  7 HURRICANE              14610229010
##  8 RIVER FLOOD            10148404500
##  9 ICE STORM               8967041810
## 10 TROPICAL STORM          8382236550

Results

Health Impact The top 10 events with the highest total fatalities and injuries are shown graphically.

library(ggplot2)
g <- ggplot(df.fatalities[1:10,], aes(x=reorder(EVTYPE, -total.fatalities), y=total.fatalities))+geom_bar(stat="identity") + theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))+ggtitle("Top 10 Events with Highest Total Fatalities") +labs(x="EVENT TYPE", y="Total Fatalities")
g

g <- ggplot(df.injuries[1:10,], aes(x=reorder(EVTYPE, -total.injuries), y=total.injuries))+geom_bar(stat="identity") + theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))+ggtitle("Top 10 Events with Highest Total Injuries") +labs(x="EVENT TYPE", y="Total Injuries")
g

As shown in the figures, tornado causes the hightest in both the total fatality and injury count.

Economic Impact

The top 10 events with the highest total economic damages (property and crop combined) are shown graphically.

g <- ggplot(df.damage.total[1:10,], aes(x=reorder(EVTYPE, -TOTAL.DMG.EVTYPE), y=TOTAL.DMG.EVTYPE))+geom_bar(stat="identity") + theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))+ggtitle("Top 10 Events with Highest Economic Impact") +labs(x="EVENT TYPE", y="Total Economic Impact ($USD)")

g

As shown in the figure, flood has the highest economic impact.