Most harmful events across the United States
Synopsis
In this report we aim to explore 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 from 1950 to November 2011, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage. Our specific goal is to know what types of wheather events are most harmful with respect to population health and which ones have the greatest economic consequences.
Data Processing
From the National Weather Service Storm Data Documentation we obtained data on storms occured across the United States. 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.
We first read data from the raw text file included in the zip archive. For reading the data we are going to use the function freadof the data.table package. dplry and ggplot2 packages are going to be required for the data analysis.
StormData=fread("repdata%2Fdata%2FStormData.csv")##
Read 22.7% of 967216 rows
Read 42.4% of 967216 rows
Read 55.8% of 967216 rows
Read 74.4% of 967216 rows
Read 82.7% of 967216 rows
Read 902297 rows and 37 (of 37) columns from 0.523 GB file in 00:00:07## Warning in fread("repdata%2Fdata%2FStormData.csv"): Read less rows (902297)
## than were allocated (967216). Run again with verbose=TRUE and please
## report.StormData=as.data.frame(StormData)After reading data, we check the first few rows ih the dataset. There are 902,297 rows.
str(StormData)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ 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 : chr "3" "2" "2" "2" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...head(StormData[,1:8])## 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
## 1 TORNADO
## 2 TORNADO
## 3 TORNADO
## 4 TORNADO
## 5 TORNADO
## 6 TORNADOResults
Population health analysis
The columns we are interested in are the FATALITIES and INJURIES variables, which contain the number of dead and injured people. First, we are going to subset the dataset with the variables we are interested in.
#Subsetting most harmful events for population health
StormHealth=select(StormData,c(EVTYPE,FATALITIES,INJURIES))
StormHealth$EVTYPE=as.factor(StormHealth$EVTYPE)
#Grouping for types of events
StormHealth5=group_by(StormHealth,EVTYPE)
StormHealth5=summarize(StormHealth5,FATALITIES=sum(FATALITIES),
INJURIES=sum(INJURIES),TOTAL=sum(c(FATALITIES,INJURIES)))
#Ordering according to number of total fatalities and injuries
StormHealth5=StormHealth5[with(StormHealth5,order(-TOTAL)),][1:5,] #5 most harmful events
MostHarmful=as.character(StormHealth5$EVTYPE[1]) #Most harmful eventIn order to show the 5 most harmful events respect to population health, we can make a barplot in terms of number of people dead or injured by type of event.
#Data for plotting
data=as.data.frame(t(StormHealth5[,2:4]))
names(data)=StormHealth5$EVTYPE
barplot(as.matrix(data),beside=T,main="5 most harmful events in United States",
ylab="people",col=c("yellow","blue","red"),cex.axis=0.8,cex.names=0.8,cex.lab=1.1)
legend("topright", c("Fatalities","Injuries","Total"),fill=c("yellow","blue","red"))
According to barplot, we can easyly see that the most harmful type of event across the United States is TORNADO
Economic consequences
In this case, the columns we are interested in are the PROPDMG and CROPDMG variables, which contain the economic property and crop consequences. PROPDMGEXP and CROPDMGEXPcolumns have the exponent of the PROPDMG and CROPDMG variables.
First, we are going to subset the dataset with the variables we are interested in.
#Subsetting 5 types of events that have the greatest economic consequences
StormEc=select(StormData,c(EVTYPE,PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP))
#Some variables have to be factor in order to do some changes
StormEc$EVTYPE=as.factor(StormEc$EVTYPE)
StormEc$PROPDMGEXP=as.factor(StormEc$PROPDMGEXP)
StormEc$CROPDMGEXP=as.factor(StormEc$CROPDMGEXP)We have to replace the key letter in the exponent column with the correct number.
#Replacing exponent code for exponent number
StormEc$PROPDMGEXP=mapvalues(StormEc$PROPDMGEXP,
c("K","M","","B","m","+","0","5","6","?","4","2","3","h","7","H","-","1","8"),
c(1e3,1e6,1,1e9,1e6,1,1,1e5,1e6,1,1e4,1e2,1e3,1,1e7,1e2,1,10,1e8))
StormEc$CROPDMGEXP=mapvalues(StormEc$CROPDMGEXP,
c("","M","K","m","B","?","0","k","2"),
c( 1,1e6,1e3,1e6,1e9,1,1,1e3,1e2))
#Transforming PROPDMG and CROPDMG in the correct number with the exponent
StormEc$PROPDMGEXP=as.numeric(levels(StormEc$PROPDMGEXP))[StormEc$PROPDMGEXP] #transform a factor to numeric
StormEc$PROPDMG=StormEc$PROPDMG*StormEc$PROPDMGEXP
StormEc$CROPDMGEXP=as.numeric(levels(StormEc$CROPDMGEXP))[StormEc$CROPDMGEXP]
StormEc$CROPDMG=StormEc$CROPDMG*StormEc$CROPDMGEXP
#Subsetting 5 most harmful events for economic consequences
StormEc=select(StormEc,c(EVTYPE,PROPDMG,CROPDMG))In order to show the 5 type of events that have the greatest economic consequences, we can make a barplot in terms of property and crop economic consequences by type of event.
#Grouping for types of events
StormEc5=group_by(StormEc,EVTYPE)
StormEc5=summarize(StormEc5,property=sum(PROPDMG),crop=sum(CROPDMG),
total=sum(c(PROPDMG,CROPDMG)))
#Ordering according to economic consequences
StormEc5=StormEc5[with(StormEc5,order(-total)),][1:5,]
MostHarmfulEc=as.character(StormEc5$EVTYPE[1])
#Plotting...
dataEc=as.data.frame(t(StormEc5[,2:4]))
names(dataEc)=StormEc5$EVTYPE
barplot(as.matrix(dataEc),beside=T,
main="top 5 events that have the greatest economic consequences in United States",
ylab="dollars",col=c("yellow","blue","red"),cex.axis=0.8,cex.names=0.8,cex.lab=1.1)
legend("topright", c("Property","Crop","Total"),fill=c("yellow","blue","red"))
According to barplot, we can say that the type of event that has the greatest economic consequences is FLOOD