Reproducible Research Course Project 02: Health and Economic Impact of US Severe Weather
Jane Nyandele
April 29, 2023
Synopsis
In this project, we analyze the storm database from the U.S. National Oceanic and Atmospheric Administration (NOAA). We estimate fatalities, injuries, property damage, and crop damage for each type of weather event (i.e., Flood, Typhoon, Tornado, Hail, Hurricane, etc.). The goal is to determine which event(s) are most harmful to US population health and which event(s) have the most economic consequences. Our analysis shows that Tornadoes have the greatest health impact on US populations, while floods have the greatest economic impacts.
Introduction
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.
Data
The data for this assignment comes in the form of a comma-separated-value (csv) file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
- Storm Data[47 Mb]
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
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.
Questions
This analysis addresses the following questions: 1. Within the United States, which weather events are most harmful with respect to population health? 2. Within the United States, which weather events have the greatest economic consequences?
Data Processing
0. Load Libraries
library(data.table)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.21. Load the data into R
data <- read.csv("repdata_data_StormData.csv", header = TRUE, sep = ",")2. Inspect the data
Use col.names to check the column names
colnames(data)## [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"3. Subset the data
Only subset columns related to health and economic impacts. For this reason, only subest the following columns: EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
selection <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
data <- data[, selection]
summary(data)## EVTYPE FATALITIES INJURIES PROPDMG
## Length:902297 Min. : 0.0000 Min. : 0.0000 Min. : 0.00
## Class :character 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00
## Mode :character Median : 0.0000 Median : 0.0000 Median : 0.00
## Mean : 0.0168 Mean : 0.1557 Mean : 12.06
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50
## Max. :583.0000 Max. :1700.0000 Max. :5000.00
## PROPDMGEXP CROPDMG CROPDMGEXP
## Length:902297 Min. : 0.000 Length:902297
## Class :character 1st Qu.: 0.000 Class :character
## Mode :character Median : 0.000 Mode :character
## Mean : 1.527
## 3rd Qu.: 0.000
## Max. :990.000Extract only the rows in which fatalities, injuries and damages occurred (i.e. are not = 0)
data <- as.data.table(data)
data <- data[(EVTYPE != "?" & (INJURIES > 0 | FATALITIES > 0 | PROPDMG > 0 | CROPDMG >0)),
c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]4. Convert the exponent columns (PROPDMGEXP and CROPDMGEXP)
Convert the exponent values in the above columns from K, M, B to 1000, 1000000, 1000000000
cols <- c("PROPDMGEXP", "CROPDMGEXP")
data[, (cols) := c(lapply(.SD, toupper)), .SDcols = cols]
PROPDMGKey <- c("\"\"" = 10^0,
"-" = 10^0, "+" = 10^0, "0" = 10^0, "1" = 10^1, "2" = 10^2, "3" = 10^3,
"4" = 10^4, "5" = 10^5, "6" = 10^6, "7" = 10^7, "8" = 10^8, "9" = 10^9,
"H" = 10^2, "K" = 10^3, "M" = 10^6, "B" = 10^9)
CROPDMGKey <- c("\"\"" = 10^0, "?" = 10^0, "0" = 10^0, "K" = 10^3, "M" = 10^6, "B" = 10^9)
data[, PROPDMGEXP := PROPDMGKey[as.character(data[,PROPDMGEXP])]]
data[is.na(PROPDMGEXP), PROPDMGEXP := 10^0 ]
data[, CROPDMGEXP := CROPDMGKey[as.character(data[,CROPDMGEXP])] ]
data[is.na(CROPDMGEXP), CROPDMGEXP := 10^0 ]5. Create new columns for Property Cost and Crop Cost
data <- data[, .(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, PROPCOST = PROPDMG * PROPDMGEXP,
CROPDMG, CROPDMGEXP, CROPCOST = CROPDMG * CROPDMGEXP)]Data Analysis
Estimating the health impact (i.e. total fatalities and injuries)
Estimate the total fatalities and injuries for each weather event, sorted by event type
Health_Impact <- data[, .(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES),
TOTAL_HEALTH_IMPACT = sum(FATALITIES) + sum(INJURIES)), by = .(EVTYPE)]
# Order by total health impact in descending order
Health_Impact <- Health_Impact[order(-TOTAL_HEALTH_IMPACT),]
# Extract the top 10 event types with the greatest health impact
Health_Impact <- Health_Impact[1:10,]
head(Health_Impact, 10)## EVTYPE FATALITIES INJURIES TOTAL_HEALTH_IMPACT
## 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 1527Estimating the economic impact (i.e. the property and crop costs)
Estimate the total of property cost and crop cost to know the economic impact
eco_impact <- data[, .(PROPCOST = sum(PROPCOST), CROPCOST = sum(CROPCOST), TOTAL_ECO_IMPACT =
sum(PROPCOST) + sum(CROPCOST)), by = .(EVTYPE)]
# Order by total economic impact in descending order
eco_impact <- eco_impact[order(-TOTAL_ECO_IMPACT)]
# Extract the top ten weather events with the most economic impact
eco_impact <- eco_impact[1:10, ]
head(eco_impact, 10)## EVTYPE PROPCOST CROPCOST TOTAL_ECO_IMPACT
## 1: FLOOD 144657709807 5661968450 150319678257
## 2: HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3: TORNADO 56947380676 414953270 57362333946
## 4: STORM SURGE 43323536000 5000 43323541000
## 5: HAIL 15735267513 3025954473 18761221986
## 6: FLASH FLOOD 16822673978 1421317100 18243991078
## 7: DROUGHT 1046106000 13972566000 15018672000
## 8: HURRICANE 11868319010 2741910000 14610229010
## 9: RIVER FLOOD 5118945500 5029459000 10148404500
## 10: ICE STORM 3944927860 5022113500 8967041360Results
Question 01: Events most harmful with respect to population health
Generate a histogram to illustrate the top 10 weather event that most affect population health
#elongate the dataframe to specify fatalities and injuries
health_consequences <- melt(Health_Impact, id.vars = "EVTYPE", variable.name = "Fatalities_or_Injuries")
#plot health_consequences
ggplot(health_consequences, aes(x = reorder(EVTYPE, -value), y = value)) +
geom_bar(stat = "identity", aes(fill = Fatalities_or_Injuries), position = "dodge") +
ylab("Total Injuries/Fatalities") +
xlab("Event Type") +
theme(axis.text.x = element_text(angle=65, hjust=1)) +
ggtitle("Top 10 US Weather Events Most Harmful to Population Health") +
theme(plot.title = element_text(hjust = 0.5))
Question 02: Events with the greatest economic consequences
Generate a histogram of the top 10 weather events most with the biggest health consequences
#elongate the dataframe to specify property and crop damage costs
eco_consequences <- melt(eco_impact, id.vars = "EVTYPE", variable.name = "Damage_Type")
#plot economic consequences
ggplot(eco_consequences, aes(x = reorder(EVTYPE, -value), y = value/1e9)) +
geom_bar(stat = "identity", aes(fill = Damage_Type), position = "dodge") +
ylab("Cost/Damage (in billion USD)") +
xlab("Event Type") +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
ggtitle("Top 10 US Weather Events with the Greatest Economic consequences") +
theme(plot.title = element_text(hjust = 0.5))