Reproducible Research: Peer Assessment 2
Juttiga
February 20, 2015
Synopsis
An Analysis Report of Health and Economic Impact by Severe Weather Events. This is based on NOAA Storm Database of US https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf
Storm and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severs events can results in fatalities, injuries and property damage. Preventing such outcomes to the extent possible is a key concern. The U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database tracks characteristics of major storms and weather events in the United States, include when and where they occur, as well as estimates of any fatalities, injuries and property damage. This report contains the exploratory analysis results on the health and economic impact by the severe weather events based on the data from NOAA database.
all R packages loaded here
suppressMessages(library(dplyr))## Warning: package 'dplyr' was built under R version 3.2.3library(knitr)## Warning: package 'knitr' was built under R version 3.2.3suppressMessages(library(R.utils))## Warning: package 'R.utils' was built under R version 3.2.3## Warning: package 'R.oo' was built under R version 3.2.3## Warning: package 'R.methodsS3' was built under R version 3.2.3library(gridExtra)## Warning: package 'gridExtra' was built under R version 3.2.3suppressMessages(library(ggplot2))## Warning: package 'ggplot2' was built under R version 3.2.3downloading zip file , and unzip
if(!file.exists("repdata-data-StormData.csv.bz2")){
dlMethod <- "curl"
}
if(substr(Sys.getenv("OS"),1,7) == "Windows") dlMethod <- "wininet"
Url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(Url,destfile="repdata-data-StormData.csv.bz2",method=dlMethod,mode="wb")
bunzip2(filename="repdata-data-StormData.csv.bz2",destname="repdata-data-StormData.CSV") Fetching Data into R
StormData<-read.csv("repdata-data-StormData.CSV")
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/ 436774 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 ...cleaning data
StormData$YEAR <- as.numeric(format(as.Date(StormData$BGN_DATE, format = "%m/%d/%Y %H:%M:%S"), "%Y"))
StormData1<- select(StormData,YEAR,EVTYPE,FATALITIES,INJURIES,PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)
StormData2<- filter(StormData1,FATALITIES>0|INJURIES>0|PROPDMG>0|CROPDMG>0)
StormData3<-write.csv(format(StormData2),"C:/Bala/Data Scientist/JHU Docs/Reproducible Research/Project2/filtered_dataset.csv",row.name=TRUE)
summary(StormData2)## YEAR EVTYPE FATALITIES
## Min. :1950 TSTM WIND :63234 Min. : 0.0000
## 1st Qu.:1997 THUNDERSTORM WIND:43655 1st Qu.: 0.0000
## Median :2002 TORNADO :39944 Median : 0.0000
## Mean :2000 HAIL :26130 Mean : 0.0595
## 3rd Qu.:2008 FLASH FLOOD :20967 3rd Qu.: 0.0000
## Max. :2011 LIGHTNING :13293 Max. :583.0000
## (Other) :47410
## INJURIES PROPDMG PROPDMGEXP CROPDMG
## Min. : 0.0000 Min. : 0.00 K :231428 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 2.00 : 11585 1st Qu.: 0.000
## Median : 0.0000 Median : 5.00 M : 11320 Median : 0.000
## Mean : 0.5519 Mean : 42.75 0 : 210 Mean : 5.411
## 3rd Qu.: 0.0000 3rd Qu.: 25.00 B : 40 3rd Qu.: 0.000
## Max. :1700.0000 Max. :5000.00 5 : 18 Max. :990.000
## (Other): 32
## CROPDMGEXP
## :152664
## K : 99932
## M : 1985
## k : 21
## 0 : 17
## B : 7
## (Other): 7Filtering the data as per requriment of analysis
StormData4<-read.csv("filtered_dataset.CSV")Data Processing
To address the question related to the impacts of weather events on public health, we’ll summarize the ‘StormData’ separately by fatalities and injuries
Health<-select(StormData4,EVTYPE,FATALITIES,INJURIES)
Health1<-filter(Health,FATALITIES>0|INJURIES>0)
Health_data<-summarise(group_by(Health1,EVTYPE),FATALITIES=sum(FATALITIES),INJURIES=sum(INJURIES))
InjuriesData_top10<-arrange(Health_data,INJURIES) %>% top_n(10)## Selecting by INJURIESFatalData_top10<-arrange(Health_data,FATALITIES)%>% top_n(10)## Selecting by INJURIEShead(InjuriesData_top10)## Source: local data frame [6 x 3]
##
## EVTYPE FATALITIES INJURIES
## (fctr) (int) (int)
## 1 HAIL 15 1361
## 2 THUNDERSTORM WIND 133 1488
## 3 FLASH FLOOD 978 1777
## 4 ICE STORM 89 1975
## 5 HEAT 937 2100
## 6 LIGHTNING 816 5230head(FatalData_top10)## Source: local data frame [6 x 3]
##
## EVTYPE FATALITIES INJURIES
## (fctr) (int) (int)
## 1 HAIL 15 1361
## 2 ICE STORM 89 1975
## 3 THUNDERSTORM WIND 133 1488
## 4 FLOOD 470 6789
## 5 TSTM WIND 504 6957
## 6 LIGHTNING 816 5230Top 10 weather events impacts public health due to INJURIES
InjuriesData_top10## Source: local data frame [10 x 3]
##
## EVTYPE FATALITIES INJURIES
## (fctr) (int) (int)
## 1 HAIL 15 1361
## 2 THUNDERSTORM WIND 133 1488
## 3 FLASH FLOOD 978 1777
## 4 ICE STORM 89 1975
## 5 HEAT 937 2100
## 6 LIGHTNING 816 5230
## 7 EXCESSIVE HEAT 1903 6525
## 8 FLOOD 470 6789
## 9 TSTM WIND 504 6957
## 10 TORNADO 5633 91346Top 10 weather events impacts public health due to Fatalties
FatalData_top10## Source: local data frame [10 x 3]
##
## EVTYPE FATALITIES INJURIES
## (fctr) (int) (int)
## 1 HAIL 15 1361
## 2 ICE STORM 89 1975
## 3 THUNDERSTORM WIND 133 1488
## 4 FLOOD 470 6789
## 5 TSTM WIND 504 6957
## 6 LIGHTNING 816 5230
## 7 HEAT 937 2100
## 8 FLASH FLOOD 978 1777
## 9 EXCESSIVE HEAT 1903 6525
## 10 TORNADO 5633 91346Property Damage from all types of weather events converted to Millions $
Converting all Property damage data noted in PROPDMGEXP coloumn “H”(hundred),“K”(kilo),“M”(million),“B”(billion) to millions
PropDmg<-select(StormData4, EVTYPE,PROPDMG,PROPDMGEXP)
PropDmg1<-filter(PropDmg,PROPDMG> 0)
PropDmg1$PROPDMGNUM =0
PropDmg1[PropDmg1$PROPDMGEXP == "H", ]$PROPDMGNUM = PropDmg1[PropDmg1$PROPDMGEXP == "H", ]$PROPDMG * 0.00001
PropDmg1[PropDmg1$PROPDMGEXP == "K", ]$PROPDMGNUM = PropDmg1[PropDmg1$PROPDMGEXP == "K", ]$PROPDMG * 0.001
PropDmg1[PropDmg1$PROPDMGEXP == "M", ]$PROPDMGNUM = PropDmg1[PropDmg1$PROPDMGEXP == "M", ]$PROPDMG * 1
PropDmg1[PropDmg1$PROPDMGEXP == "B", ]$PROPDMGNUM = PropDmg1[PropDmg1$PROPDMGEXP == "B", ]$PROPDMG * 1000
sum(PropDmg1$PROPDMGNUM)## [1] 427279.7To address the question related to the impacts of weather events on economy, we’ll summarize the ‘StormData’ separately by damage to property and crops.
PropDmg_data<-summarise(group_by(PropDmg1,EVTYPE),PROPDMG=round(sum(PROPDMGNUM),digits=1))
PropDmgData_top10<-arrange(PropDmg_data,PROPDMG) %>% top_n(10)## Selecting by PROPDMGTop 10 Events caused Property damage
PropDmgData_top10## Source: local data frame [10 x 2]
##
## EVTYPE PROPDMG
## (fctr) (dbl)
## 1 HIGH WIND 5270.0
## 2 WINTER STORM 6688.5
## 3 TROPICAL STORM 7703.9
## 4 HURRICANE 11868.3
## 5 HAIL 15727.4
## 6 FLASH FLOOD 16140.8
## 7 STORM SURGE 43323.5
## 8 TORNADO 56925.7
## 9 HURRICANE/TYPHOON 69305.8
## 10 FLOOD 144657.7Crop Damage from all types of weather events converted to Millions $
Converting all Crop damage data noted in CROPDMGEXP coloumn “H”(hundred),“K”(kilo),“M”(million),“B”(billion) to Millions
CropDmg<-select(StormData4, EVTYPE,CROPDMG,CROPDMGEXP)
CropDmg1<-filter(CropDmg,CROPDMG > 0)
CropDmg1$CROPDMGNUM =0
CropDmg1[CropDmg1$CROPDMGEXP == "H", ]$CROPDMGNUM = CropDmg1[CropDmg1$CROPDMGEXP == "H", ]$CROPDMG * 0.00001
CropDmg1[CropDmg1$CROPDMGEXP == "K", ]$CROPDMGNUM = CropDmg1[CropDmg1$CROPDMGEXP == "K", ]$CROPDMG * 0.001
CropDmg1[CropDmg1$CROPDMGEXP == "M", ]$CROPDMGNUM = CropDmg1[CropDmg1$CROPDMGEXP == "M", ]$CROPDMG * 1
CropDmg1[CropDmg1$CROPDMGEXP == "B", ]$CROPDMGNUM = CropDmg1[CropDmg1$CROPDMGEXP == "B", ]$CROPDMG * 1000
sum(CropDmg1$CROPDMGNUM)## [1] 49093.76CropDmg_data<-summarise(group_by(CropDmg1,EVTYPE),CROPDMG=round(sum(CROPDMGNUM),digits=1))
CropDmgData_top10<-arrange(CropDmg_data,CROPDMG) %>% top_n(10)## Selecting by CROPDMGTop 10 Events caused Crop damage
CropDmgData_top10## Source: local data frame [10 x 2]
##
## EVTYPE CROPDMG
## (fctr) (dbl)
## 1 FROST/FREEZE 1094.1
## 2 EXTREME COLD 1293.0
## 3 FLASH FLOOD 1421.3
## 4 HURRICANE/TYPHOON 2607.9
## 5 HURRICANE 2741.9
## 6 HAIL 3025.5
## 7 ICE STORM 5022.1
## 8 RIVER FLOOD 5029.5
## 9 FLOOD 5662.0
## 10 DROUGHT 13972.6RESULTS
PLOTS “HEALTH DAMAGE”"
library(ggplot2)
Injggplot<-ggplot(InjuriesData_top10,aes(EVTYPE,INJURIES,fill=EVTYPE))+
geom_bar(stat="identity")+
theme(axis.text.x = element_text(angle=45,hjust = 1)) +
labs(x="Event Types",y= "Injuries ")+
labs(title=" Injuries from Major Disasters")+
scale_fill_discrete(name = "EVTYPE")+
geom_text(aes(label=INJURIES), vjust = -0.3, size = 3.5)
Fatalggplot<-ggplot(FatalData_top10,aes(EVTYPE,FATALITIES,fill=EVTYPE))+
geom_bar(stat="identity")+
theme(axis.text.x = element_text(angle=45,hjust = 1)) +
labs(x="Event Types",y= "Fatalities ")+
labs(title=" Fatalities from Major Disasters")+
scale_fill_discrete(name = "EVTYPE")+
geom_text(aes(label=FATALITIES), vjust = -0.3, size = 3.5)
grid.arrange(Injggplot,Fatalggplot,nrow=2) 
According to the data, tornadoes are probably the most deadly weather event in the US, followed by excessive heat events.
PLOTS “PROPERTY/CROP DAMAGE”
PropDmgPl<-ggplot(PropDmgData_top10,aes(EVTYPE,PROPDMG,fill=EVTYPE))+
geom_bar(stat="identity")+
theme(axis.text.x = element_text(angle=45,hjust = 1)) +
labs(x="Event Types",y= "Property Damage in Millions ")+
labs(title=" Property Damage from Major Disasters")+
scale_fill_discrete(name = "EVTYPE")+
geom_text(aes(label=PROPDMG), vjust = -0.3, size = 3.5)
CropDmgPl<-ggplot(CropDmgData_top10,aes(EVTYPE,CROPDMG,fill=EVTYPE))+
geom_bar(stat="identity")+
theme(axis.text.x = element_text(angle=45,hjust = 1)) +
labs(x="Event Types",y= "Crop Damage in Millions ")+
labs(title=" Crop Damage from Major Disasters")+
scale_fill_discrete(name = "EVTYPE")+
geom_text(aes(label=CROPDMG), vjust = -0.3, size = 3.5)
grid.arrange(PropDmgPl,CropDmgPl,nrow=2) 
According to the data, FLOODS cause the most Property damages, followed by hurricanes/typhoons and torndaoes.
Drought and flood are causes for the greatest damage to crops.
The weather events have the greatest economic consequences are: flood, drought, Tornado and Typhoon. Across the United States, flood, tornado and typhoon have caused the greatest damage to properties.
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