National Oceanic and Atmospheric Administration’s Storm Database Analysis
Sysnopsis
The U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database was used to compare outcomes from storm and severe weather event categories. Analyzed data are from 1993–2011. Outcomes analyzed are the number of fatalities and injuries and the dollar amount of property damage. Event categories are convective, extreme temperature, flood, winter, and other.
Estimates of fatalities, injuries, property and crop damage were used to provide assistance with which types of event are most harmful to the population health and what the economic impact might be.
Read and Processing Data
- Read data
data1 <- read.csv(bzfile("StormData.csv.bz2"))
str(data1)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ 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 ...
## $ WFO : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : Factor w/ 436781 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...The data you have 37 columns and 902297 rows.
- Creating subsets of data for better management. 5% of the most recent data was used for reasons of system
storm<-data1[857183:902297,]
rm(data1)Analysis
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
- Create news variables for our analysis
fatalities <- aggregate(storm$FATALITIES, list(storm$EVTYPE), sum)
injuries <- aggregate(storm$INJURIES, list(storm$EVTYPE), sum)
health <- merge(fatalities, injuries, by="Group.1")
health[,4] <- health[,2] + health[,3]
colnames(health) <- c("type", "fatalities", "injuries", "total")
healthtop <- health[order(health$total, decreasing=TRUE),][1:10,]- Plotted the number of cases for each event
library(reshape2)
healthtop_1 <- melt(healthtop, id.vars="type")
library(ggplot2)
ggplot(healthtop_1, aes(x = reorder(type, -value), y = value)) +
geom_bar(stat = "identity", aes(fill = variable), position = "dodge") +
scale_fill_brewer("Number of Cases") + xlab("Event Type") + ylab("Number of Cases") +
theme_bw() + theme(axis.text.x = element_text(angle = 30, hjust=1)) +
ggtitle("Health Threat Events")
Across the United States, which types of events have the greatest economic consequences?
- Examining the data and According to the National Weather Service Storm Data Documentation: “K”/ “k” stand for thousand “M”/ “m” stand for million “B”/ “b” stand for billion
unique(storm$CROPDMGEXP)## [1] K M B
## Levels: ? 0 2 B k K m Munique(storm$PROPDMGEXP)## [1] K M B 0
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m M- Creates a new data frame called economy
economy <- storm[,c("EVTYPE", "PROPDMG", "PROPDMGEXP", "CROPDMG","CROPDMGEXP")]
index1 <- which(economy$PROPDMGEXP%in%c("K","k"))
index2 <- which(economy$PROPDMGEXP%in%c("M","m"))
index3 <- which(economy$PROPDMGEXP%in%c("B","b"))
economy[,2][index1] <- economy[,2][index1]*(10^3)
economy[,2][index2] <- economy[,2][index2]*(10^6)
economy[,2][index3] <- economy[,2][index3]*(10^9)
index4 <- which(economy$CROPDMGEXP%in%c("K","k"))
index5 <- which(economy$CROPDMGEXP%in%c("M","m"))
index6 <- which(economy$CROPDMGEXP%in%c("B","b"))
economy[,4][index4] <- economy[,4][index4]*(10^3)
economy[,4][index5] <- economy[,4][index5]*(10^6)
economy[,4][index6] <- economy[,4][index6]*(10^9)- Creates a new data frame called propertydmgs and corpdmgs
propertydmgs <- aggregate(economy$PROPDMG, list(economy$EVTYPE), sum)
corpdmgs <- aggregate(economy$CROPDMG, list(economy$EVTYPE), sum)
colnames(propertydmgs) <- c("type", "propertydmgs")
colnames(corpdmgs) <- c("type", "corpdmgs")
propertydmgs <- propertydmgs[order(propertydmgs$propertydmgs, decreasing=TRUE),][1:10,]
corpdmgs <- corpdmgs[order(corpdmgs$corpdmgs, decreasing=TRUE),][1:10,]- Plotted the data Property Damage (Dollar) for each event type.
ggplot(propertydmgs, aes(x=reorder(type, -propertydmgs), y=propertydmgs)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_brewer("Property Damage(US Dollar)") + xlab("Event Type") + theme_bw() +
ylab("Property Damage (Dollar)") + theme(axis.text.x = element_text(angle = 30, hjust=1)) +
ggtitle("Top 10 Property Threat Events")
- Plotted the data Property Damage (Dollar) for each event type.
ggplot(corpdmgs, aes(x=reorder(type, -corpdmgs), y=corpdmgs)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_brewer("Corp Damage(US Dollar)") + xlab("Event Type") +
ylab("Corp Damage (Dollar)") + theme_bw() +
theme(axis.text.x = element_text(angle = 30, hjust=1)) +
ggtitle("Top 10 Corp Threat Events")
- Part of US suffer most from the weather disaster
healthState <- storm[,c("STATE", "FATALITIES", "INJURIES")]
healthState[,4] <- healthState[,2] + healthState[,3]
colnames(healthState)[4] <- "TOTAL"
healthState_1 <- aggregate(healthState$TOTAL, list(healthState$STATE), sum)
colnames(healthState_1) <- c("STATE", "VALUE")
healthState<-healthState_1[order(healthState_1$VALUE,decreasing = TRUE),]
head(healthState)## STATE VALUE
## 2 AL 2139
## 36 MO 1474
## 57 TN 936
## 58 TX 674
## 31 MA 413
## 48 OK 399Results
According to the data (5% of the data of 2011), demonstrated among these types of weather disaster per number of case, TORNADO (5826), EXCESSIVE HEAT (674) and THUNDER STORM WIND (344) are most dangerous for public health.
the types of disasters that have caused more damage and property loss in dollars are TORNADO (7.613372510^{9}), FLOOD (7.590086510^{9}) and FLASH FLOOD (1.30468310^{9}). While FLOOD (1.513710^{8}), THUNDER STORM WIND (1.3921610^{8}) and ICESTORM (8.832310^{7}) bring most corp damage. In the aspect of geographic classification, ALABAMA (2139) is the most vulnerable targets for bad weather events.