The basic goal of this analysis is to explore the NOAA Storm Database and answer some basic questions about severe weather events.
The data analysis will address the following two questions:
“Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health”. In order to answer this question couple of variables in the dataset will be used to measure the impact of a severe weather event on population health, namely, INJURIES and FATALITIES. Also, a new derived variable Health_Hazards will be created by adding these variables to measure the total impact.
“Across the United States, which types of events have the greatest economic consequences”. In order to answer this question, again another couple of variables in the dataset will be used to measure the impact of a severe weather event on economy, namely, PROPDMG and CROPDMG. Also, a new derived variable Eco_Hazards will be created by adding these variables to measure the total impact.
The exploratory data analysis will be done using R and barplots will be used to compare the impact of a severe event, both on population health and economy. Two sepearate analysis will be done to answer to two different questions.
As will be seen, TORNADO has the highest impact in terms of harmfulness in both the cases.
storm <- read.csv(bzfile("repdata-data-StormData.csv.bz2"))
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)
## 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
dim(storm)
## [1] 902297 37
loc_vars <- c("STATE", "BGN_DATE", "EVTYPE")
health_vars <- c("FATALITIES", "INJURIES")
prop_vars <- c("PROPDMG", "CROPDMG")
storm1 <- storm[c(loc_vars, health_vars)]
storm1$Health_Hazards <- storm1$FATALITIES + storm1$INJURIES
fatalities <- sort(tapply(storm1$FATALITIES, storm1$EVTYPE, sum), decreasing=TRUE)
injuries <- sort(tapply(storm1$INJURIES, storm1$EVTYPE, sum), decreasing=TRUE)
hazards <- sort(tapply(storm1$Health_Hazards, storm1$EVTYPE, sum), decreasing=TRUE)
n <- 20 # top 20 harmful events
fatalities <- head(fatalities, n) #f[f > 0]
injuries <- head(injuries, n) #i[i > 0]
hazards <- head(hazards, n) #h[h > 0]
fatalities <- as.data.frame(cbind(event=names(fatalities), count=fatalities, type="Fatalities"))
injuries <- as.data.frame(cbind(event=names(injuries), count=injuries, type="Injuries"))
hazards <- as.data.frame(cbind(event=names(hazards), count=hazards, type="Health Hazard"))
d <- rbind(fatalities, injuries, hazards)
d$count <- as.integer(as.character(d$count))
d <- transform(d, event = reorder(event, -count))
hazards$count <- as.integer(as.character(hazards$count))
tblHealth <- cbind(fatalities[1:2], injuries[1:2])
names(tblHealth) <- c("Events", "Fatalities", "Events", "Injuries")
print(tblHealth, row.names=FALSE)
## Events Fatalities Events Injuries
## TORNADO 5633 TORNADO 91346
## EXCESSIVE HEAT 1903 TSTM WIND 6957
## FLASH FLOOD 978 FLOOD 6789
## HEAT 937 EXCESSIVE HEAT 6525
## LIGHTNING 816 LIGHTNING 5230
## TSTM WIND 504 HEAT 2100
## FLOOD 470 ICE STORM 1975
## RIP CURRENT 368 FLASH FLOOD 1777
## HIGH WIND 248 THUNDERSTORM WIND 1488
## AVALANCHE 224 HAIL 1361
## WINTER STORM 206 WINTER STORM 1321
## RIP CURRENTS 204 HURRICANE/TYPHOON 1275
## HEAT WAVE 172 HIGH WIND 1137
## EXTREME COLD 160 HEAVY SNOW 1021
## THUNDERSTORM WIND 133 WILDFIRE 911
## HEAVY SNOW 127 THUNDERSTORM WINDS 908
## EXTREME COLD/WIND CHILL 125 BLIZZARD 805
## STRONG WIND 103 FOG 734
## BLIZZARD 101 WILD/FOREST FIRE 545
## HIGH SURF 101 DUST STORM 440
tblHealth <- cbind(hazards[1:2], round(100*hazards[2] / sum(hazards[2]),2))
names(tblHealth) <- c("Events", "Total.Health.Hazards", "Percent.Total.Health.Hazards")
print(tblHealth, row.names=FALSE)
## Events Total.Health.Hazards Percent.Total.Health.Hazards
## TORNADO 96979 65.86
## EXCESSIVE HEAT 8428 5.72
## TSTM WIND 7461 5.07
## FLOOD 7259 4.93
## LIGHTNING 6046 4.11
## HEAT 3037 2.06
## FLASH FLOOD 2755 1.87
## ICE STORM 2064 1.40
## THUNDERSTORM WIND 1621 1.10
## WINTER STORM 1527 1.04
## HIGH WIND 1385 0.94
## HAIL 1376 0.93
## HURRICANE/TYPHOON 1339 0.91
## HEAVY SNOW 1148 0.78
## WILDFIRE 986 0.67
## THUNDERSTORM WINDS 972 0.66
## BLIZZARD 906 0.62
## FOG 796 0.54
## RIP CURRENT 600 0.41
## WILD/FOREST FIRE 557 0.38
library(ggplot2)
ggplot(d, aes(x=event, y=count, fill=type)) +
geom_bar(stat="identity") +
facet_wrap(~type, scales = "free") +
ggtitle("Barplot showing the top events most harmful to population health") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5),
plot.title = element_text(lineheight=.8, face="bold"))
tblHealth <- transform(tblHealth, Events = reorder(Events, -Percent.Total.Health.Hazards))
p <- ggplot(data=tblHealth,
aes(x=factor(1),
y=Percent.Total.Health.Hazards,
fill = factor(Events))
)
p <- p + geom_bar(stat = "identity") + xlab("")
p + coord_polar(theta="y") +
ggtitle("Piechart showing the top events most harmful to population health") +
theme(plot.title = element_text(lineheight=.8, face="bold"))
consequences?
In order to answer this question, again another couple of variables in the dataset are used to measure the impact of a severe weather event on economy, namely, PROPDMG and CROPDMG.
Also, a new derived variable Eco_Hazards is created by adding these variables (Eco_Hazards = PROPDMG + CROPDMG) to measure the total impact.
storm2 <- storm[c(loc_vars, prop_vars)]
storm2$PROPDMG <- as.numeric(as.character(storm2$PROPDMG))
storm2$CROPDMG <- as.numeric(as.character(storm2$CROPDMG))
storm2$Eco_Hazards <- storm2$PROPDMG + storm2$CROPDMG
hazards <- sort(tapply(storm2$Eco_Hazards, storm2$EVTYPE, sum), decreasing=TRUE)
prop <- sort(tapply(storm2$PROPDMG, storm2$EVTYPE, sum), decreasing=TRUE)
crop <- sort(tapply(storm2$CROPDMG, storm2$EVTYPE, sum), decreasing=TRUE)
n <- 20 # top 20 harmful
prop <- head(prop, n)
crop <- head(crop, n)
hazards <- head(hazards, n)
hazards <- as.data.frame(cbind(event=names(hazards), count=hazards, type="Eco Hazard"))
prop <- as.data.frame(cbind(event=names(prop), count=prop, type="Prop Dmg"))
crop <- as.data.frame(cbind(event=names(crop), count=crop, type="Crop Dmg"))
d <- rbind(hazards, prop, crop)
d$count <- as.numeric(as.character(d$count))
d <- transform(d, event = reorder(event, -count))
hazards$count <- as.numeric(as.character(hazards$count))
tblHealth <- cbind(prop[1:2], crop[1:2])
names(tblHealth) <- c("Events", "PropDmg", "Events", "CropDmg")
print(tblHealth, row.names=FALSE)
## Events PropDmg Events CropDmg
## TORNADO 3212258.16 HAIL 579596.28
## FLASH FLOOD 1420124.59 FLASH FLOOD 179200.46
## TSTM WIND 1335965.61 FLOOD 168037.88
## FLOOD 899938.48 TSTM WIND 109202.6
## THUNDERSTORM WIND 876844.17 TORNADO 100018.52
## HAIL 688693.38 THUNDERSTORM WIND 66791.45
## LIGHTNING 603351.78 DROUGHT 33898.62
## THUNDERSTORM WINDS 446293.18 THUNDERSTORM WINDS 18684.93
## HIGH WIND 324731.56 HIGH WIND 17283.21
## WINTER STORM 132720.59 HEAVY RAIN 11122.8
## HEAVY SNOW 122251.99 FROST/FREEZE 7034.14
## WILDFIRE 84459.34 EXTREME COLD 6121.14
## ICE STORM 66000.67 TROPICAL STORM 5899.12
## STRONG WIND 62993.81 HURRICANE 5339.31
## HIGH WINDS 55625 FLASH FLOODING 5126.05
## HEAVY RAIN 50842.14 HURRICANE/TYPHOON 4798.48
## TROPICAL STORM 48423.68 WILDFIRE 4364.2
## WILD/FOREST FIRE 39344.95 TSTM WIND/HAIL 4356.65
## FLASH FLOODING 28497.15 WILD/FOREST FIRE 4189.54
## URBAN/SML STREAM FLD 26051.94 LIGHTNING 3580.61
tblHealth <- cbind(hazards[1:2], round(100*hazards[2] / sum(hazards[2]),2))
names(tblHealth) <- c("Events", "Total.Eco.Hazards", "Percent.Total.Eco.Hazards")
print(tblHealth, row.names=FALSE)
## Events Total.Eco.Hazards Percent.Total.Eco.Hazards
## TORNADO 3312277 28.02
## FLASH FLOOD 1599325 13.53
## TSTM WIND 1445168 12.23
## HAIL 1268290 10.73
## FLOOD 1067976 9.04
## THUNDERSTORM WIND 943636 7.98
## LIGHTNING 606932 5.13
## THUNDERSTORM WINDS 464978 3.93
## HIGH WIND 342015 2.89
## WINTER STORM 134700 1.14
## HEAVY SNOW 124418 1.05
## WILDFIRE 88824 0.75
## ICE STORM 67690 0.57
## STRONG WIND 64611 0.55
## HEAVY RAIN 61965 0.52
## HIGH WINDS 57385 0.49
## TROPICAL STORM 54323 0.46
## WILD/FOREST FIRE 43534 0.37
## DROUGHT 37998 0.32
## FLASH FLOODING 33623 0.28
ggplot(d, aes(x=event, y=count, fill=type)) +
geom_bar(stat="identity") + facet_wrap(~type, scales = "free") +
ggtitle("Barplot showing the top events with greatest economic conseqeunces") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5),
plot.title = element_text(lineheight=.8, face="bold"))
