Analysis of Damage Caused by Various Wheather Events in USA
Reproducible Research :Project 2 (By Sougata Biswas)
JOHN HOPHINS DATA SCIENCE SPECIALIZATION
Across the United States,storms and other severe weather events like tornadoes, excessive heat, and flash floods etc 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.
Synopsis
The raw data for this assignment come from National Weather Service Instruction in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. 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. Fatalities, injuries, and property damage (in dollars) are totalled over that time.
In this project, I have identified some of the main events which have greatest impact on population health & economic consequences & measured the effects & showed with graphical presentation.
Data Processing
Step 1: Load the data.
storm.data = read.csv(bzfile("repdata-data-StormData.csv.bz2"), header = TRUE)Step 2: See the structure of the data.
str(storm.data)## '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 ...Step 3: Since, only some selected variables are needed for analysis, an updated data set is needed removing the variables not needed for the analysis from the parent data set.
reduced.storm.data <-
storm.data[,c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG")]Step 4: Normalize event names.
reduced.storm.data$EVTYPE <-
gsub("^HEAT$", "EXCESSIVE HEAT", reduced.storm.data$EVTYPE)
reduced.storm.data$EVTYPE <-
gsub("^TSTM WIND$", "THUNDERSTORM WIND", reduced.storm.data$EVTYPE)
reduced.storm.data$EVTYPE <-
gsub("^THUNDERSTORM WIND$", "THUNDERSTORM WINDS", reduced.storm.data$EVTYPE)Step 5: Aggregate data on Fatalities. Find which events are the top 10 causes of Fatalities.
agg.fatalities.data <-
aggregate(
reduced.storm.data$FATALITIES,
by=list(reduced.storm.data$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg.fatalities.data) = c("event.type", "fatality.total")
fatalities.sorted <-
agg.fatalities.data[order(-agg.fatalities.data$fatality.total),]
top.fatalities <- fatalities.sorted[1:10,]
top.fatalities$event.type <-
factor(
top.fatalities$event.type, levels=top.fatalities$event.type,
ordered=TRUE)Step 6: Aggregate data on Injuries or rather population health. Find which events are the top 10 causes of Injuries or rather Population health.
agg.injuries.data <-
aggregate(
reduced.storm.data$INJURIES,
by=list(reduced.storm.data$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg.injuries.data) = c("event.type", "injury.total")
injuries.sorted <- agg.injuries.data[order(-agg.injuries.data$injury.total),]
top.injuries <- injuries.sorted[1:10,]
top.injuries$event.type <-
factor(
top.injuries$event.type, levels=top.injuries$event.type,
ordered=TRUE)Step 7: Aggregate data on Property Damage or greatest economic consequences. Find which events are the top 10 causes of Property Damage or greatest economic consequences.
agg.prop.dmg.data <-
aggregate(
reduced.storm.data$PROPDMG,
by=list(reduced.storm.data$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg.prop.dmg.data) = c("event.type", "prop.dmg.total")
prop.dmg.sorted <- agg.prop.dmg.data[order(-agg.prop.dmg.data$prop.dmg.total),]
top.prop.dmg <- prop.dmg.sorted[1:10,]
top.prop.dmg$event.type <-
factor(
top.prop.dmg$event.type, levels=top.prop.dmg$event.type,
ordered=TRUE)Results
Step 1: Graph the top 10 causes of Fatalities.
library(ggplot2)
ggplot(data=top.fatalities, aes(x=event.type, y=fatality.total)) +
geom_bar(stat="identity") + xlab("Event type") + ylab("Total fatalities") +
ggtitle("Fatalities By Event Type") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Step 2: Graph the top 10 causes of Injuries.
ggplot(data=top.injuries, aes(x=event.type, y=injury.total)) +
geom_bar(stat="identity") + xlab("Event type") + ylab("Total injuries") +
ggtitle("Injuries By Event Type") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Step 3: Graph the top 10 causes of Property Damage.
ggplot(data=top.prop.dmg, aes(x=event.type, y=prop.dmg.total)) +
geom_bar(stat="identity") + xlab("Event type") +
ylab("Total property damage") + ggtitle("Property Damage By Event Type") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))