title: “Project 2” date: “19 December 2017” output: html_document: keep_md: yes
Synopsis
In this report we will analysis the NOAA Storm Events data. The data collected from year 1950 to 2011, with 902297 records. Fatalities, injuries, and property damage (in dollars) are totalled over that time. Data will be cleansed, re-orderd and analyised in order to identified
Data Processing
url = "http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, dest = "storm.bz2")
stormData<-read.csv(bzfile("storm.bz2"))
head(stormData, 3)## 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
## 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
## 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
## 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
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3Subsetting and summarised require Data
New_stormData<- stormData[,c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG","PROPDMGEXP","CROPDMG","CROPDMGEXP")]
summary(New_stormData)## EVTYPE FATALITIES INJURIES
## HAIL :288661 Min. : 0.0000 Min. : 0.0000
## TSTM WIND :219940 1st Qu.: 0.0000 1st Qu.: 0.0000
## THUNDERSTORM WIND: 82563 Median : 0.0000 Median : 0.0000
## TORNADO : 60652 Mean : 0.0168 Mean : 0.1557
## FLASH FLOOD : 54277 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## FLOOD : 25326 Max. :583.0000 Max. :1700.0000
## (Other) :170878
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9assigning new column name
New_stormData$EVTYPE <-
gsub("^HEAT$", " EXCESSIVE HEAT", New_stormData$EVTYPE)
New_stormData$EVTYPE <-
gsub("^TSTM WIND$", "THUNDERSTORM WIND", New_stormData$EVTYPE)
New_stormData$EVTYPE <-
gsub("^THUNDERSTORM WIND$", "THUNDERSTORM WINDS", New_stormData$EVTYPE)The purpose of the following code is to aggregate the data in order to prepare it for the results. Data will be aggregated (SUM) on Event Type level, the TOP 10 events will be then indentified.
agg_fatData <-
aggregate(
New_stormData$FATALITIES,
by=list(New_stormData$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg_fatData) <- c("event.type", "fatality.total")
fatalities.sorted <-
agg_fatData[order(-agg_fatData$fatality.total),]
top.fatalities <- fatalities.sorted[1:10,]
top.fatalities$event.type <-
factor(
top.fatalities$event.type, levels=top.fatalities$event.type,
ordered=TRUE)Ploting graph for Top 10 causes of Fatalities
In this case Tornado are most harmful events for population.
library(ggplot2)
ggplot(data=top.fatalities, aes(x=event.type, y=fatality.total)) +
geom_bar(stat="identity",fill= "blue") + xlab("Event type") + ylab("Total fatalities") +
ggtitle("Fatalities By Event Type")+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
Aggregate data on injuries to find which events are the top 10 causes of injuries
agg_injData <-
aggregate(
New_stormData$INJURIES,
by=list(New_stormData$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg_injData) <- c("event.type", "injury.total")
injuries.sorted <- agg_injData[order(-agg_injData$injury.total),]
top.injuries <- injuries.sorted[1:10,]
top.injuries$event.type <-
factor(
top.injuries$event.type, levels=top.injuries$event.type,
ordered=TRUE)Ploting graph for Top 10 causes of injuries
ggplot(data=top.injuries, aes(x=event.type, y=injury.total)) +
geom_bar(stat="identity") + xlab("Event type") + ylab("Total injuries") +
ggtitle("Injuries By Event Type") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
Aggregate data on Property Damage to find which events are the top 10 causes of Property Damage.
agg_prodamData <-
aggregate(
New_stormData$PROPDMG,
by=list(New_stormData$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg_prodamData) <- c("event.type", "prop.dmg.total")
prop.dmg.sorted <- agg_prodamData[order(-agg_prodamData$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$event.type,
ordered=TRUE)Ploting graph for Top 10 causes of Property Damage.
ggplot(data=top.prop.dmg, aes(x=event.type, y=prop.dmg.total)) +
geom_bar(stat="identity") + xlab("Event type") +
ylab("Total property damage") + ggtitle("Property Damage By Event Type") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
Aggregate data on crop Damage to find which events are the top 10 causes of crop Damage.
agg_cropData <-
aggregate(
New_stormData$CROPDMG,
by=list(New_stormData$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg_cropData) <- c("event.type", "crop.dmg.total")
crop.dmg.sorted <- agg_cropData[order(-agg_cropData$crop.dmg.total),]
top.crop.dmg <- crop.dmg.sorted[1:10,]
top.crop.dmg$event.type <-
factor(
top.crop.dmg$event.type, levels=top.crop.dmg$event.type,
ordered=TRUE)Ploting graph for Top 10 causes of crop Damage. This graph showa hail is most charmful cause for Crops
ggplot(data=top.crop.dmg, aes(x=event.type, y=crop.dmg.total)) +
geom_bar(stat="identity") + xlab("Event type") +
ylab("Total crop damage") + ggtitle("Property Damage By Event Type") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
Tornados caused the maximum number of fatalities and injuries