Severe weather events consequences in US
Synopsis
The goal of this document is to give answers to questions about which types of severe weather events are most harmful with respect to population health and which types of events have the greatest economic consequences. Analysis is based on the data from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. First we must prepare the data. Then we need to aggregate data by type of severe weather events and finally we sort the data in descending order. Conclusions of our analysis shows that across the United States the tornados are most harmful with respect to population health and the floods have the greatest economic consequences.
Data Processing
Before we can do our analysis we must download the data from the Coursera “Reproducible Research” course.
if(!file.exists("./StormData.csv.bz2"))
{
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, "./StormData.csv.bz2", method = "curl")
}Then we read the downloaded data, which are in the form of a comma-separated-value file compressed via the bzip2.
storm_data <- read.table(bzfile("./StormData.csv.bz2"),header = TRUE, sep=",")After reading in the data we check the first few rows in this dataset.
dim(storm_data)## [1] 902297 37 head(storm_data)## 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 6Then we select only columns that are relevant for our analysis and calculate property damages:
consequences <- storm_data[,c("EVTYPE","FATALITIES","INJURIES","PROPDMG","PROPDMGEXP")]
magnitude <- function(x){
m <- 1
if (x=="K" | x=="k" ) m <-1000
if (x=="M" | x=="m" ) m <-1000000
if (x=="B" | x=="b" ) m <-1000000000
return(m)
}
consequences$PROPDMGEXP <- sapply(consequences$PROPDMGEXP,magnitude)
consequences$PROPDMG <- consequences$PROPDMG*consequences$PROPDMGEXPTo answer the questions we need to aggregate data by type of severe weather events and eliminate the types of events with zero values.
fatalities_by_evtype <- aggregate(FATALITIES ~ EVTYPE,data=consequences,FUN=sum, na.rm=TRUE)
fatalities_by_evtype <- fatalities_by_evtype[fatalities_by_evtype$FATALITIES!=0,]
injuries_by_evtype <- aggregate(INJURIES ~ EVTYPE,data=consequences,FUN=sum, na.rm=TRUE)
injuries_by_evtype <- injuries_by_evtype[injuries_by_evtype$INJURIES!=0,]
propdmg_by_evtype <- aggregate(PROPDMG ~ EVTYPE,data=consequences,FUN=sum, na.rm=TRUE)
propdmg_by_evtype <- propdmg_by_evtype[propdmg_by_evtype$PROPDMG!=0,]Results
Now we are ready to do analysis to answer the questions. First we sort our data descending and then show the first five rows.
fatalities_by_evtype <- fatalities_by_evtype[order(-fatalities_by_evtype$FATALITIES),]
head(fatalities_by_evtype,5)## EVTYPE FATALITIES
## 830 TORNADO 5633
## 123 EXCESSIVE HEAT 1903
## 147 FLASH FLOOD 978
## 269 HEAT 937
## 452 LIGHTNING 816 injuries_by_evtype <- injuries_by_evtype[order(-injuries_by_evtype$INJURIES),]
head(injuries_by_evtype,5)## EVTYPE INJURIES
## 830 TORNADO 91346
## 854 TSTM WIND 6957
## 164 FLOOD 6789
## 123 EXCESSIVE HEAT 6525
## 452 LIGHTNING 5230 propdmg_by_evtype <- propdmg_by_evtype[order(-propdmg_by_evtype$PROPDMG),]
head(propdmg_by_evtype,5)## EVTYPE PROPDMG
## 164 FLOOD 1.447e+11
## 406 HURRICANE/TYPHOON 6.931e+10
## 830 TORNADO 5.694e+10
## 666 STORM SURGE 4.332e+10
## 147 FLASH FLOOD 1.614e+10So now we can say that across the United States the TORNADOs are most harmful with respect to population health and the FLOODs have the greatest economic consequences. We can also represent this by adequate plots.
library(ggplot2)
qplot(EVTYPE,FATALITIES,data=fatalities_by_evtype[fatalities_by_evtype$FATALITIES>100,],
geom="bar", stat="identity", xlab="Weather event type",ylab="Number of fatalities")+
theme(axis.text.x = element_text(angle = 90)) +
labs(title="Number of fatalities by\n severe weather event type")
qplot(EVTYPE,INJURIES,data=injuries_by_evtype[injuries_by_evtype$INJURIES>1000,],
geom="bar", stat="identity", xlab="Weather event type",ylab="Number of injuries")+
theme(axis.text.x = element_text(angle = 90))+
labs(title="Number of injuries by\n severe weather event type")
qplot(EVTYPE,PROPDMG,data=propdmg_by_evtype[propdmg_by_evtype$PROPDMG>5000000000,],
geom="bar", stat="identity", xlab="Weather event type",ylab="Properity damages in $")+
theme(axis.text.x = element_text(angle = 90))+
labs(title="Properity damages by\n severe weather event type")