Reproducible Research Peer Assessment 2
Nidhi Mavani
Sunday, October 26, 2014
1.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. 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 events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete. In this report we aim to address two questions
1. Across the United States, which types of events are most harmful with respect to population health?
2. Across the United States, which types of events have the greatest economic consequences?
2.DataProcessing
The data can be downloaded StormData
Then Load the data into a variable called StormData
stormData <- read.table("repdata-data-StormData.csv.bz2", fill=TRUE, sep=",", header=TRUE, blank.lines.skip=TRUE)Explore the dataset.
str(stormData)## '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 ...Across the United States, which types of events are most harmful with respect to population health?
we subset the dataset, so that only records that contain fatalities and injuries are selected over the years.
fatalData <- subset(stormData, subset=stormData$FATALITIES>0)
injuryData <- subset(stormData, subset=stormData$INJURIES>0)we subset the dataset, so that only records that contain property and crop damage are selected
propData <- subset(stormData, subset=stormData$PROPDMG>0
& !is.na(stormData$PROPDMGEXP))
cropData <- subset(stormData, subset=stormData$CROPDMG>0
& !is.na(stormData$CROPDMGEXP))Aggregated the data with the event type applying function SUM over the coloumn Fatalities
totalFatal <- aggregate(fatalData$FATALITIES, by=list(fatalData$EVTYPE), FUN=sum)
colnames(totalFatal) <- c("Event", "Fatalities")
totalFatal <- totalFatal[order(totalFatal$Fatalities, decreasing=TRUE),]
head(totalFatal)## Event Fatalities
## 141 TORNADO 5633
## 26 EXCESSIVE HEAT 1903
## 35 FLASH FLOOD 978
## 57 HEAT 937
## 97 LIGHTNING 816
## 145 TSTM WIND 504Aggregated the data with the event type applying function SUM over the coloumn Fatalities over the years
totalInjury <- aggregate(injuryData$INJURIES, by=list(injuryData$EVTYPE), FUN=sum)
colnames(totalInjury) <- c("Event", "Injuries")
totalInjury <- totalInjury[order(totalInjury$Injuries, decreasing=TRUE),]
head(totalInjury)## Event Injuries
## 129 TORNADO 91346
## 135 TSTM WIND 6957
## 30 FLOOD 6789
## 20 EXCESSIVE HEAT 6525
## 85 LIGHTNING 5230
## 47 HEAT 2100Merge the Data frames with Event
Then order the data with more of fatalities
merged=merge(totalFatal,totalInjury,by="Event")
mergedorder=merged[order(merged$Fatalities, decreasing=T),]
head(mergedorder)## Event Fatalities Injuries
## 86 TORNADO 5633 91346
## 14 EXCESSIVE HEAT 1903 6525
## 21 FLASH FLOOD 978 1777
## 35 HEAT 937 2100
## 59 LIGHTNING 816 5230
## 89 TSTM WIND 504 6957Plot the fatalities vs injuries
plot(merged$Injuries,mergedorder$Fatalities, ylim = c(0, 6000), xlim = c(0,
100000), pch = 19, col = rgb(0, 1, 0, 0.5), cex = 1.5, xlab = "number of Injuries", ylab = "number of Fatalities", main = "Fatalities and Injuries due to severe weather events")
Across the United States, which types of events have the greatest economic consequences?
There are 2 variables, PROPDMG (property damaage) and CROPDMG (crop damage), that indicate which types of severe weather events have the greatest economic consequences.
PROPDMGEXP contains the exponential where h stands for hundred, k stands for thousdands 2 stands 10^2 and so on
propDmgKey <- c("\"\"" = 10^0,
"-" = 10^0,
"+" = 10^0,
"0" = 10^0,
"1" = 10^1,
"2" = 10^2,
"3" = 10^3,
"4" = 10^4,
"5" = 10^5,
"6" = 10^6,
"7" = 10^7,
"8" = 10^8,
"9" = 10^9,
"H" = 10^2,
"h" = 10^2,
"k" = 10^3,
"m" = 10^6,
"b" = 10^9,
"K" = 10^3,
"M" = 10^6,
"B" = 10^9)
propData$PROPDMGEXP=propDmgKey[as.character(propData$PROPDMGEXP)]
propData$Damage=propData$PROPDMG*propData$PROPDMGEXPAggregate the dAta now based on event and calculating the sum of property damages
totalDamageP <- aggregate(propData$Damage, by=list(propData$EVTYPE), FUN=sum)
colnames(totalDamageP) <- c("Event", "Damages")
totalDamageP <- totalDamageP[order(totalDamageP$Damages, decreasing=TRUE),]
head(totalDamageP)## Event Damages
## 182 HURRICANE/TYPHOON 69305840000
## 282 STORM SURGE 43323536000
## 174 HURRICANE 11868319010
## 342 TROPICAL STORM 7703890550
## 399 WINTER STORM 6688497251
## 159 HIGH WIND 5270046295CROPDMGEXP contains the exponential where H stands for hundred, K stands for thousdands so on
cropDmgKey <- c("\"\"" = 10^0,
"?" = 10^0,
"0" = 10^0,
"K" = 10^3,
"M" = 10^6,
"B" = 10^9)
cropData$CROPDMGEXP <- cropDmgKey[as.character(cropData$CROPDMGEXP)]
cropData$Damage=cropData$CROPDMG*cropData$CROPDMGEXPAggregate the dAta now based on event and calculating the sum of crop damages
totalDamageC <- aggregate(cropData$Damage, by=list(cropData$EVTYPE), FUN=sum)
colnames(totalDamageC) <- c("Event", "Damages")
totalDamageC <- totalDamageC[order(totalDamageC$Damages, decreasing=TRUE),]
head(totalDamageC)## Event Damages
## 10 DROUGHT 13972566000
## 27 FLOOD 5661968450
## 78 RIVER FLOOD 5029459000
## 72 ICE STORM 5022113500
## 64 HURRICANE 2741910000
## 69 HURRICANE/TYPHOON 2607872800Conclusion
Tornado and Excessive heat had highest fatalities
while Hurricane/Typhoon caused hisghest property damage and Droughts caused highest Crop damage