Economic and Public Health Impacts of Weather Events in the U.S.
Ximena Ramirez
7/1/2020
Synopsis
Storms and other severe weather events cause both public health and economic problems for communities and municipalities within the United States. 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.
The analysis addresses 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?
According to the analysis, the results are:
- Tornados are the most harmful with respect to population health.
- Floods have the greatest economic consequences.
Data Processing
The following packages will be used:
#Load necessary packages
library(plyr)
library(dplyr)
library(ggplot2)Download and read file containing the original data set.
#download file
filename <- "repdata_data_StormData.csv.bz2"
if (!file.exists(filename)) {
fileURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileURL, destfile = filename)
}
#read file
stormdata <- read.csv(filename)Take a quick look at the data, and see if it is possible to subset the data frame.
names(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"head(stormdata)## 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
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE 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
## 6 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
## 6 0 1.5 177 2 0 0 6 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
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6str(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/ 436781 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 ...The following variables will be defined and used for further analysis in order to answer the questions:
- EVTYPE: Event Type
- FATALITIES: Number of Fatalities
- INJURIES: Number of Injuries
- PROPDMG: Property Damage
- PROPDMGEXP: Unit of Porperty Damage (K, B, M)*
- CROPDMG: Crop Damage
- CROPDMGEXP: Unit of Crop Damage (K, B, M)*
*Note: As specified by the National Weather Service Storm Data Documentation, “Estimates should be rounded to three significant digits, followed by an alphabetical character signifying the magnitude of the number… Alphabetical characters used to signify magnitude include “K” for thousands, “M” for millions, and “B” for billions."
#subset variables specified above
data_sub <- select(stormdata, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP"))
str(data_sub)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ 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 ...Looking at the output from str(data_sub), PROPDMGEXP and CROPDMGEXP are factor variables (expected), but they have more than 3 levels (unexpected).
unique(data_sub$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Munique(data_sub$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MThese variables will be refactored due to possible input error. (See Note above.) The factor levels will be changed according to the following:
K, M, B levels will be changed to their numeric values; 10^3, 10^6, 10^9, respectively.
“?”, “-”, “+” will have a value of 0
"“,”0" will have a value of 1.
data_sub$PROPDMGEXP <- mapvalues(data_sub$PROPDMGEXP, from = levels(data_sub$PROPDMGEXP), to = c(1, 0, 0, 0, 1, 10, 10^2, 10^3, 10^4, 10^5, 10^6, 10^7, 10^8, 10^9, 10^2, 10^2, 10^3, 10^6, 10^6))
data_sub$CROPDMGEXP <- mapvalues(data_sub$CROPDMGEXP, from = levels(data_sub$CROPDMGEXP), to = c(1, 0, 1, 10^2, 10^9, 10^3, 10^3, 10^6, 10^6))Now multiply the property and crop damages by their respective exponent column, in order to find the total values for property damage and crop damage.
#Calculate total property damage
data_sub$PROPDMGEXP <- as.numeric(as.character(data_sub$PROPDMGEXP)) #change to character and then numeric to perserve exponent
data_sub <- mutate(data_sub, PROPDMG.TOTAL = PROPDMG * PROPDMGEXP)
#Calculate total crop damage
data_sub$CROPDMGEXP <- as.numeric(as.character(data_sub$CROPDMGEXP)) #change to character and then numeric to perserve exponent
data_sub <- mutate(data_sub, CROPDMG.TOTAL = CROPDMG * CROPDMGEXP)The data_sub data frame will now be used for analysis in order to answer the stated questions.
Analysis & Results
Which events are most harmful to population health?
The data from the INJURIES and FATALITIES variables indicate impact to public health. A boxplot will display the event type with its associated injuries and fatalities.
data_sub %>%
select(INJURIES, EVTYPE) %>%
group_by(EVTYPE) %>%
summarise(injury = sum(INJURIES)) %>%
top_n(n = 10, wt = injury) %>% #filter out top 10 events wrt injury
ggplot(aes(x = reorder(EVTYPE, -injury), y = injury, fill = EVTYPE)) + #reorder barplot to plot injury in descending order
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Event", y = "Injuries", title = "Top 10 Highest Injury Events") +
geom_text(aes(label=injury), size = 4, hjust = 0.5, vjust = -0.1) + #add totals on top of each bar
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) #rotate yaxis labels
Looking at the graph, TORNADO events have caused the most injuries at 91,346.
Now for fatalities:
data_sub %>%
select(FATALITIES, EVTYPE) %>%
group_by(EVTYPE) %>%
summarise(fatality = sum(FATALITIES)) %>%
top_n(n = 10, wt = fatality) %>%
ggplot(aes(x = reorder(EVTYPE, -fatality), y = fatality, fill = EVTYPE)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Event", y = "Fatalities", title = "Top 10 Highest Fatality Events") +
geom_text(aes(label=fatality), size = 4, hjust = 0.5, vjust = -0.1) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Looking at the graph, TORNADO events have caused the most fatalities at 5,633.
Therefore, it is concluded that TORNADO events are the most harmful with respect to public health.
Which events have the greatest economic consequences?
The data from the PROPDMG.TOTAL and CROPDMG.TOTAL variables indicate total propoerty and crop damage, respectively. To illustrate economic impact, a boxplot plotting event type and combined property and crop damages will be created.
data_sub %>%
select(PROPDMG.TOTAL, CROPDMG.TOTAL, EVTYPE) %>%
#combine crop and prop damages
group_by(EVTYPE) %>%
summarise(damage = sum(PROPDMG.TOTAL + CROPDMG.TOTAL)/10^9) %>%
#divide by 10^9 to look at damages in billions of dollars
top_n(n = 10, wt = damage) %>% #filter out top 10 events wrt injury
ggplot(aes(x = reorder(EVTYPE, -damage), y = damage, fill = EVTYPE)) +
geom_bar(stat = "identity", show.legend = FALSE) +
labs(x = "Event", y = "Damages (in billions of dollars)", title = "Top 10 Highest Damage Events") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Looking at the graph, FLOOD events have caused the most damages at about $150 billion.
Results
According to the analysis,
- Across the United States, tornados are the most harmful with respect to population health.
- Across the United States, floods have the greatest economic consequences.