Explore NOAA Storm DB
Jennifer Tsou
3/10/2018
Synopsis
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.
This project explores 2 questions: 1. The types of events that are most harmful with respect to population health. 2. The types of events that have the greatest economics consequences.
Data Processing
Download data and read in as csv. Next we subset the data to grab the key elements that has economics consequences and health implications.
#download.file('https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2', destfile = 'Storm.csv.bz2', method = 'curl')
Storm = read.csv('Storm.csv.bz2',header = TRUE, sep = ",")
#review storm data
names(Storm)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"head(Storm, nrow=2)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6#subset data
SubStorm = Storm[, c('EVTYPE','FATALITIES','INJURIES','PROPDMG','PROPDMGEXP','CROPDMG','CROPDMGEXP')]
str(SubStorm)## 'data.frame': 488189 obs. of 7 variables:
## $ EVTYPE : Factor w/ 974 levels " HIGH SURF ADVISORY",..: 825 825 825 825 825 825 825 825 825 825 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...Next step is to convert the values under variables by mapping the values
library(plyr)
table(SubStorm$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5
## 352019 1 8 5 215 25 13 4 4 28
## 6 7 8 B h H K m M
## 4 5 1 15 1 6 129085 7 6743tmpPROP=mapvalues(SubStorm$PROPDMGEXP, c("-","?","+", "0","1","2","3","4","5","6","7","8","B","h","H","K","m","M"," "),
c(1,1, 1, 1,10, 10^2, 10^3,10^4,10^5,10^6,10^7,10^8,10^9, 10^2,10^2,10^3,10^6, 10^6,1))## The following `from` values were not present in `x`:table(SubStorm$CROPDMGEXP)##
## ? 0 2 B k K m M
## 469800 7 19 1 5 21 17275 1 1060tmpCROP=mapvalues(SubStorm$CROPDMGEXP, c("?", "0","2","B","k","K","m","M"," "),
c(1,1, 10^2, 10^9,10^3,10^3,10^6,10^6,1))## The following `from` values were not present in `x`:SubStorm$Tot_PropDMG = as.numeric(tmpPROP)*SubStorm$PROPDMG
SubStorm$Tot_CropDMG = as.numeric(tmpCROP)*SubStorm$CROPDMGResults
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health ?
library(ggplot2)
event = aggregate(FATALITIES ~ EVTYPE, SubStorm, sum)
str(event)## 'data.frame': 974 obs. of 2 variables:
## $ EVTYPE : Factor w/ 974 levels " HIGH SURF ADVISORY",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 0 0 ...#get top 10
event = event[order(event$FATALITIES, decreasing = T),][1:10,]
#factor the events to get descending order for graph
event$EVTYPE <- factor(event$EVTYPE, levels = event$EVTYPE)
ggplot(event, aes(x=EVTYPE, y= FATALITIES)) +
geom_bar(stat = 'identity', fill = 'blue')+
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab('Events')+
ylab('Fatalities')+
ggtitle('Events That Are Most Harmful with Respect to Population Health Across the US')
We can see that Tornado is the number one cause of most harmful event
Number of Injuries BY TOP 10 Wrather Events
Injuries = aggregate(INJURIES ~ EVTYPE, SubStorm, sum)
str(Injuries)## 'data.frame': 974 obs. of 2 variables:
## $ EVTYPE : Factor w/ 974 levels " HIGH SURF ADVISORY",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ INJURIES: num 0 0 0 0 0 0 0 0 0 0 ...#get top 10
Injuries = Injuries[order(Injuries$INJURIES, decreasing = T),][1:10,]
#factor the events to get descending order for graph
Injuries$EVTYPE <- factor(Injuries$EVTYPE, levels = Injuries$EVTYPE)
ggplot(Injuries, aes(x=EVTYPE, y= INJURIES)) +
geom_bar(stat = 'identity', fill = 'red')+
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab('Events')+
ylab('Injuries')+
ggtitle('Injuries By Top 10 Weather Events Across the US')
We can see that Tornado causes the most injuries as as the event that causes the most fatalities above
Across the United States, which types of events have the greatest economic consequences ?
SubStorm$TotalDamage = SubStorm$Tot_CropDMG +SubStorm$Tot_PropDMG
DMG = aggregate(TotalDamage ~ EVTYPE, data=SubStorm, sum)
str(DMG)## 'data.frame': 974 obs. of 2 variables:
## $ EVTYPE : Factor w/ 974 levels " HIGH SURF ADVISORY",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ TotalDamage: num 1000 0 250 0 564 40 0 0 25 0 ...DMG = DMG[order(DMG$TotalDamage, decreasing = T), ][1:10, ]
DMG$EVTYPE = factor(DMG$EVTYPE, levels = DMG$EVTYPE)
ggplot(DMG, aes(x = EVTYPE, y = TotalDamage/10^3)) +
geom_bar(stat = "identity", fill = "green") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab("Event Type") + ylab("Damages (1k$)") + ggtitle("Property & Crop Damages(Total Damage) by top 10 Weather Events")
Conclusion: It appears that Tornado causes not only the most damage to human health but the economics damages as well.