NOAA Storm Data Analysis
krrish16
24 July 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 (start in the year 1950 and end in November 2011). 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.
Data Processing
# Loading data
data <- read.csv("./StormData.csv.bz2")
dim(data)## [1] 902297 37head(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 6# Checking for Missing values
sapply(data , function(x) sum(is.na(x)))## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME
## 0 0 0 0 0 0
## STATE EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## 0 0 0 0 0 0
## END_TIME COUNTY_END COUNTYENDN END_RANGE END_AZI END_LOCATI
## 0 0 902297 0 0 0
## LENGTH WIDTH F MAG FATALITIES INJURIES
## 0 0 843563 0 0 0
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC
## 0 0 0 0 0 0
## ZONENAMES LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## 0 47 0 40 0 0
## REFNUM
## 0Alphabetical characters are used to signify magnitude of property damage and crop damage that include “K” for thousands, “M” for millions, and “B” for billions.So we have to convert them into numericals
# Converting property damage and crop damage
unique(data$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Mdata$PROPEXP[data$PROPDMGEXP == "K"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "M"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == ""] <- 1
data$PROPEXP[data$PROPDMGEXP == "B"] <- 1e+09
data$PROPEXP[data$PROPDMGEXP == "m"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "0"] <- 1
data$PROPEXP[data$PROPDMGEXP == "5"] <- 1e+05
data$PROPEXP[data$PROPDMGEXP == "6"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "4"] <- 10000
data$PROPEXP[data$PROPDMGEXP == "2"] <- 100
data$PROPEXP[data$PROPDMGEXP == "3"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "h"] <- 100
data$PROPEXP[data$PROPDMGEXP == "7"] <- 1e+07
data$PROPEXP[data$PROPDMGEXP == "H"] <- 100
data$PROPEXP[data$PROPDMGEXP == "1"] <- 10
data$PROPEXP[data$PROPDMGEXP == "8"] <- 1e+08
data$PROPEXP[data$PROPDMGEXP == "+"] <- 0
data$PROPEXP[data$PROPDMGEXP == "-"] <- 0
data$PROPEXP[data$PROPDMGEXP == "?"] <- 0
data$PROPDMGVAL <- data$PROPDMG * data$PROPEXP
head(data$PROPDMGVAL)## [1] 25000 2500 25000 2500 2500 2500# Similarly converting crop damage
unique(data$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m Mdata$CROPEXP[data$CROPDMGEXP == "M"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "K"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "m"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "B"] <- 1e+09
data$CROPEXP[data$CROPDMGEXP == "0"] <- 1
data$CROPEXP[data$CROPDMGEXP == "k"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "2"] <- 100
data$CROPEXP[data$CROPDMGEXP == ""] <- 1
data$CROPEXP[data$CROPDMGEXP == "?"] <- 0
data$CROPDMGVAL <- data$CROPDMG * data$CROPEXP
head(data$CROPDMGVAL)## [1] 0 0 0 0 0 0Answering the given questions
1.Across the United States, which types of events (as indicated in the “EVTYPE”) are most harmful with respect to population health?
Aggregating and Sorting
fatalities <- aggregate(FATALITIES ~ EVTYPE, data=data, FUN = sum)
fatalities <- fatalities[order(fatalities$FATALITIES, decreasing=TRUE),]
injuries <- aggregate(INJURIES ~ EVTYPE, data=data, FUN = sum)
injuries <- injuries[order(injuries$INJURIES, decreasing=TRUE),]
head(fatalities)## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504head(injuries)## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100Plot 1
par(mfrow=c(1,2))
p1 <- barplot (height = fatalities$FATALITIES[1:10], names.arg = fatalities$EVTYPE[1:10], las = 2, cex.names= 0.6, col = "light blue" , main = "Fatalities Count vs Event Type")
p2 <- barplot (height = injuries$INJURIES[1:10], names.arg = injuries$EVTYPE[1:10], las = 2, cex.names= 0.6, col = "light blue" , main = "Injuries Count vs Event Type")
2.Across the United States, which types of events have the greatest economic consequences?
Aggregating and Sorting
property <- aggregate(PROPDMGVAL ~ EVTYPE, data, FUN = sum)
property <- property[order(property$PROPDMGVAL, decreasing=TRUE),]
crop <- aggregate(CROPDMGVAL ~ EVTYPE, data, FUN = sum)
crop <- crop[order(crop$CROPDMGVAL, decreasing=TRUE),]
head(property)## EVTYPE PROPDMGVAL
## 170 FLOOD 144657709807
## 411 HURRICANE/TYPHOON 69305840000
## 834 TORNADO 56947380617
## 670 STORM SURGE 43323536000
## 153 FLASH FLOOD 16822673979
## 244 HAIL 15735267513head(crop)## EVTYPE CROPDMGVAL
## 95 DROUGHT 13972566000
## 170 FLOOD 5661968450
## 590 RIVER FLOOD 5029459000
## 427 ICE STORM 5022113500
## 244 HAIL 3025954473
## 402 HURRICANE 2741910000Plot 2
par(mfrow=c(1,2))
barplot (height = property$PROPDMGVAL[1:10]/(10^9), names.arg = property$EVTYPE[1:10], las = 2, cex.names= 0.6,col = "blue" , main = "Property Damage vs Event Type")
barplot (height = crop$CROPDMGVAL[1:10]/(10^9), names.arg = crop$EVTYPE[1:10], las = 2, cex.names= 0.6,col = "blue" , main = "Crop Damage vs Event Type")
Results
From Plot 1 , Tornados are the most harmful events that caused maximum number of fatalities and injuries followed by Excessive Heat and Thunderstorm wind in case of fatalities and injuries respectively.
From Plot 2 , Floods are the events that have the greatest economic consequences causing the maximum property damage while Drought caused the maximum crop damage followed by Hurricanes and Floods for property damage and crop damage respectively