Extreme weather effects can take a devastating toll on the public's health and property damaging. I conducted a review of the health and economic impacts of US extereme weather events to evaluate a most important event based on United States Storm Data from National Weather Service for years 1950 - 2011. It was established that in United States tornado has the greatest economic consequences and is most harmful for public health.
The U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database was obtained from the the Reproducible Research course's web site. The data were loaded from the “StormData.csv.bz2” file using the “read.csv” function.
data <- read.csv(bzfile("StormData.csv.bz2"), header = T, sep = ",", na.strings = "",
stringsAsFactors = F)
I explored 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. The database includes 902297 rows in 37 variables.
dim(data)
## [1] 902297 37
names(data)
## [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(data, 3)
## 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
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 <NA> <NA> <NA> <NA> 0
## 2 TORNADO 0 <NA> <NA> <NA> <NA> 0
## 3 TORNADO 0 <NA> <NA> <NA> <NA> 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 <NA> <NA> 14.0 100 3 0 0
## 2 NA 0 <NA> <NA> 2.0 150 2 0 0
## 3 NA 0 <NA> <NA> 0.1 123 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0 <NA> <NA> <NA> <NA>
## 2 0 2.5 K 0 <NA> <NA> <NA> <NA>
## 3 2 25.0 K 0 <NA> <NA> <NA> <NA>
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 <NA> 1
## 2 3042 8755 0 0 <NA> 2
## 3 3340 8742 0 0 <NA> 3
I got subsets of data where FATALITIES, INJURES or PROPDMG are not equal to 0. I aggregated and sorted them by events' type to show their impact on the health of the community.
health.influence.df <- subset(data, FATALITIES != 0 | INJURIES != 0)
property.damage.df <- subset(data, PROPDMG != 0)
fatalities.df <- aggregate(FATALITIES ~ EVTYPE, data = health.influence.df,
sum)
fatalities.df <- fatalities.df[order(fatalities.df$FATALITIES, decreasing = T),
]
injuries.df <- aggregate(INJURIES ~ EVTYPE, data = health.influence.df, sum)
injuries.df <- injuries.df[order(injuries.df$INJURIES, decreasing = T), ]
I illustrated impact of extreme weather events on the health of the community producing plots. Most human fatalities were caused by tornado.
par(mfrow = c(2, 1))
barplot(fatalities.df$FATALITIES[1:5], names.arg = fatalities.df$EVTYPE[1:5],
cex.names = 0.5, main = "Top 5 extreme weather events caused fatalities in the US 1950 - 2011",
xlab = "Weather event", ylab = "Number of fatalities", col = rainbow(5))
barplot(injuries.df$INJURIES[1:5], names.arg = injuries.df$EVTYPE[1:5], cex.names = 0.5,
main = "Top 5 extreme weather events caused injuries in the US 1950 - 2011",
xlab = "Weather event", ylab = "Number of injuries", col = rainbow(5))
I aggregated and sorted the 'property.damage.df' subset by events' type to show their impact on property damage. The most damaging weather events was tornado.
property.damage.df <- aggregate(PROPDMG ~ EVTYPE, data = property.damage.df,
sum)
property.damage.df <- property.damage.df[order(property.damage.df$PROPDMG, decreasing = T),
]
par(mfrow = c(1, 1))
barplot(property.damage.df$PROPDMG[1:5], names.arg = property.damage.df$EVTYPE[1:5],
cex.names = 0.5, main = "Top 5 weather events caused property damage in the US 1950 - 2011",
xlab = "Weather event", ylab = "Number of property damage", col = rainbow(5))
It was established that in United States tornado has the greatest economic consequences and is most harmful for public health.