Reproducible research project 2
Sergio Maldonado
2/9/2020
Impact of Severe Weather Events on Public Health and Economy in the United States
Sinopsis
In this report, I analyzed the impact of different weather events on public health and economy based on the storm database collected from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) from 1950 - 2011. I will use the estimates of fatalities, injuries, property and crop damage toaddress the following questions:
- Across the United States, which types of events are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
After analize these data, we found that excessive heat and tornado are the most harmful with respect to population health, while flood, drought, and hurricane/typhoon have the greatest economic consequences
Data Processing
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.
- Storm Data 47Mb
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation
National Climatic Data Center Storm Events FAQ
Load Libraries
library(dplyr)
library(tidyr)
library(ggplot2)
#library(xtable)First, Downloaded the file and unzip to obtain a csv file. Then, we read the generated csv file. If the data already exists in the working environment, we do not need to load it again. Otherwise, we read the csv file.
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
if (!dir.exists("./data")){
#print("Created dir")
dir.create("./data")
}
if (!"stormData.csv.bz2" %in% dir("./data/")) {
#print("Downdloadind Zip")
download.file(fileUrl, destfile = "data/stormData.csv.bz2",method = "curl")
}
stormdata<-read.csv("stormData.csv.bz2")There are 902297 rows and 37 columns in total.
print(dim(stormdata))## [1] 902297 37head(stormdata, 5)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5The 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.
stormdata$year<-format(strptime(stormdata$BGN_DATE,"%m/%d/%Y %H:%M:%S"),"%Y")
hist(as.numeric(stormdata$year), breaks = 30)
Based on the above histogram, we see that the number of events tracked starts to significantly increase around 1995.
Examing column names
colnames(stormdata)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM" "year"Data Subsetting
I subset the dataset on the parameters of interest. Basically, I removed the columns we don’t need for clarity
stormdata<-stormdata%>%
filter(FATALITIES>0 | INJURIES>0| PROPDMG > 0 | CROPDMG > 0)%>%
select(EVTYPE,FATALITIES,INJURIES,PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)
stormdataChanging Letters to uppercase to make the analysis
stormdata$PROPDMGEXP<-sapply(stormdata$PROPDMGEXP, toupper)
stormdata$CROPDMGEXP<-sapply(stormdata$CROPDMGEXP, toupper)Converting the property damage and crop damage data into comparable numerical forms according to the meaning of units described in the code book. Both PROPDMGEXP and CROPDMGEXP columns record a multiplier for each observation:
- 2 = 10^2
- 3 = 10^3
- 4 = 10^4
- 5 = 10^5
- 6 = 10^6
- 7 = 10^7
- Billion (B) = 10^9
- Hundred (H) = 10^2
- Thousand (K) = 10^3
- Million (M) = 10^6
stormdata$PROPDMGEXP<-recode(stormdata$PROPDMGEXP,"2"=10^2,
"3"=10^3,
"4"=10^4,
"5"=10^5,
"6"=10^6,
"7"=10^7,
"B"=10^9,
"H"=10^2,
"K"=10^3,
"M" = 10^6, .default = 0)
stormdata$CROPDMGEXP<-recode(stormdata$CROPDMGEXP,"B"=10^9,
"H"=10^2,
"K"=10^3,
"M" = 10^6, .default = 0)Creating Economic Cost Columns
stormdata<-stormdata%>%mutate(propCost=PROPDMG*PROPDMGEXP,
cropCost=CROPDMG*CROPDMGEXP)
head(stormdata)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP propCost
## 1 TORNADO 0 15 25.0 1000 0 0 25000
## 2 TORNADO 0 0 2.5 1000 0 0 2500
## 3 TORNADO 0 2 25.0 1000 0 0 25000
## 4 TORNADO 0 2 2.5 1000 0 0 2500
## 5 TORNADO 0 2 2.5 1000 0 0 2500
## 6 TORNADO 0 6 2.5 1000 0 0 2500
## cropCost
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0Calculating Impact on Economy based on Property and Crop damage cost and selecting the top 15 events
#top 15 Cost Damage
Top15_Damage<-stormdata%>%group_by(EVTYPE)%>%
summarise(propCost=sum(propCost),
cropCost=sum(cropCost),
TotalCost=sum(propCost)+sum(cropCost))%>%
arrange(desc(TotalCost))%>%head(15)Calculating Impact on Public Health based on the number of fatalities and injuries that are caused by the severe weather events and selecting the top 15
Top15_Harm<-stormdata%>%group_by(EVTYPE)%>%
summarise(FATALITIES=sum(FATALITIES),
INJURIES=sum(INJURIES),
Total=sum(FATALITIES)+sum(INJURIES))%>%
arrange(desc(Total))%>%head(15)Results
Events that are Most Harmful to Population Health, taking in account the amount of injuries and fatalities.
Top15_Harm## # A tibble: 15 x 4
## EVTYPE FATALITIES INJURIES Total
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 EXCESSIVE HEAT 1903 6525 8428
## 3 TSTM WIND 504 6957 7461
## 4 FLOOD 470 6789 7259
## 5 LIGHTNING 816 5230 6046
## 6 HEAT 937 2100 3037
## 7 FLASH FLOOD 978 1777 2755
## 8 ICE STORM 89 1975 2064
## 9 THUNDERSTORM WIND 133 1488 1621
## 10 WINTER STORM 206 1321 1527
## 11 HIGH WIND 248 1137 1385
## 12 HAIL 15 1361 1376
## 13 HURRICANE/TYPHOON 64 1275 1339
## 14 HEAVY SNOW 127 1021 1148
## 15 WILDFIRE 75 911 986Reshaping data in order to plot
Top15_Harm<-gather(Top15_Harm,key = "Type",value ="values", -EVTYPE)Plotting Impact on Public Health results
ggplot(Top15_Harm,aes(x=reorder(EVTYPE,-values),y=values,fill=Type))+
geom_col(position = "dodge")+
xlab("Severe Weather Type")+ ylab("Total number of health impact") +
theme(axis.text.x=element_text(angle=45,hjust=1))+
ggtitle("Top 15 Fatalities and Injuries by \nSevere Weather Events in\n the U.S. from 1995 - 2011")+
theme(plot.title = element_text(hjust = 0.5))
#reshaping data to original form
#Top15_Harm<-Top15_Harm%>%spread(Type,values)%>%arrange(desc(Total))Based on the above histograms, tornato causes most injuries and fatalities in the United States from 1995 to 2011.
Events that have the greatest economic consequences, taking in account Property and Crop damage cost
Top15_Damage## # A tibble: 15 x 4
## EVTYPE propCost cropCost TotalCost
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 144657709800 5661968450 150319678250
## 2 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3 TORNADO 56947380480 414953110 57362333590
## 4 STORM SURGE 43323536000 5000 43323541000
## 5 HAIL 15735267220 3025954450 18761221670
## 6 FLASH FLOOD 16822673510 1421317100 18243990610
## 7 DROUGHT 1046106000 13972566000 15018672000
## 8 HURRICANE 11868319010 2741910000 14610229010
## 9 RIVER FLOOD 5118945500 5029459000 10148404500
## 10 ICE STORM 3944927810 5022113500 8967041310
## 11 TROPICAL STORM 7703890550 678346000 8382236550
## 12 WINTER STORM 6688497250 26944000 6715441250
## 13 HIGH WIND 5270046260 638571300 5908617560
## 14 WILDFIRE 4765114000 295472800 5060586800
## 15 TSTM WIND 4484928440 554007350 5038935790Reshaping data in order to plot
Top15_Damage<-gather(Top15_Damage,key = "Type",value = "values",-EVTYPE)Plotting Impact on Economy results
ggplot(Top15_Damage,aes(x=reorder(EVTYPE,-values),y=(values/10^6),fill=Type))+
geom_col(position = "dodge")+
xlab("Severe Weather Type")+ ylab("Cost ( Million dollars)") +
#scale_y_continuous(labels = function(x) format(x, scientific = FALSE))+
ggtitle("Top 15 Crop and Property Damage by \nSevere Weather Events in\n the U.S. from 1995 - 2011")+
theme(axis.text.x=element_text(angle=45,hjust=1))+
theme(plot.title = element_text(hjust = 0.5))
#reshaping data to original form
#Top15_Damage<-Top15_Damage%>%spread(Type,values)%>%arrange(desc(TotalCost))Based on the above histograms, flood and hurricane/typhoon cause most property damage; drought and flood causes most crop damage in the United States from 1995 to 2011.However flood is the The most devastating weather event with the greatest economic cosequences (dollars) is a flood.