Reproducible Research 2
Walter Appati
2024-12-03
1. Title
The Impact of Severe Weather Events on Health and Economy Across the United States
2. Synopsis
This analysis examines the impact of severe weather events in the United States using NOAA Storm Database data from 1950 to November 2011. It identifies the event types most harmful to population health and those causing the greatest economic damage. Health impacts are measured by fatalities and injuries, while economic impacts consider property and crop damage. Tornadoes are the most harmful to health, causing the highest fatalities and injuries, with heatwaves and floods also significant. Economically, floods lead to the greatest financial losses, followed by hurricanes and tornadoes. These findings can guide policymakers in prioritizing resources and strategies to mitigate the effects of severe weather events.
3. Data Processing
1. Load the Data
- We read the compressed .bz2 file directly into R using read.csv().
data <- read.csv("~/repdata_data_StormData.csv.bz2")2. Inspect the Data
- We explore the structure using str(), head(), and summary() to understand the variables, especially EVTYPE, FATALITIES, INJURIES, and economic damage columns (PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP).
str(data)## '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 : 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: 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 ...summary(data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:19.0 Class :character Class :character Class :character
## Median :30.0 Mode :character Mode :character Mode :character
## Mean :31.2
## 3rd Qu.:45.0
## Max. :95.0
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## 3. Clean and Transform
- We standardize event types (EVTYPE) for consistency.
- We also convert exponential damage variables (PROPDMGEXP, CROPDMGEXP) to numeric multipliers (e.g., K = 1,000, M = 1,000,000).
- Then finally, we create aggregate variables for total health impact (FATALITIES + INJURIES) and economic impact (property + crop damage).
library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Define a helper function to handle the exponent mapping
convert_exp <- function(exp) {
case_when(
exp %in% c("K", "k") ~ 1e3,
exp %in% c("M", "m") ~ 1e6,
exp %in% c("B", "b") ~ 1e9,
exp %in% c("", " ", NA) ~ 1, # Empty, missing, or blank values
TRUE ~ 1 # Default for unexpected values
)
}
# Apply the helper function and calculate new columns
data <- data %>%
mutate(
PROPDMGEXP = convert_exp(PROPDMGEXP),
CROPDMGEXP = convert_exp(CROPDMGEXP),
Total_Economic_Impact = PROPDMG * PROPDMGEXP + CROPDMG * CROPDMGEXP,
Total_Health_Impact = FATALITIES + INJURIES
)4. Pretesting the Results
- After applying this code, check the unique values of the transformed columns and verify the total economic impact:
# Check transformed exponents
unique(data$PROPDMGEXP)## [1] 1e+03 1e+06 1e+00 1e+09unique(data$CROPDMGEXP)## [1] 1e+00 1e+06 1e+03 1e+09# Summary of economic impact
summary(data$Total_Economic_Impact)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00e+00 0.00e+00 0.00e+00 5.28e+05 1.00e+03 1.15e+114. Results
Q1: Most Harmful Events for Population Health
top_health <- data %>%
group_by(EVTYPE) %>%
summarize(Total_Health_Impact = sum(Total_Health_Impact)) %>%
arrange(desc(Total_Health_Impact)) %>%
slice(1:10)
library(ggplot2)
ggplot(top_health, aes(x = reorder(EVTYPE, Total_Health_Impact), y = Total_Health_Impact)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Top 10 Most Harmful Weather Events to Population Health",
x = "Event Type", y = "Total Health Impact (Fatalities + Injuries)")
- Based on the analysis, tornadoes are the most harmful weather events to population health, causing the highest total number of fatalities and injuries across the United States. Excessive heat events rank second, significantly contributing to fatalities. These findings highlight the critical need for preparedness and targeted safety measures for these specific event types.
Q2: Greatest Economic Consequences
top_economic <- data %>%
group_by(EVTYPE) %>%
summarize(Total_Economic_Impact = sum(Total_Economic_Impact)) %>%
arrange(desc(Total_Economic_Impact)) %>%
slice(1:10)
ggplot(top_economic, aes(x = reorder(EVTYPE, Total_Economic_Impact), y = Total_Economic_Impact)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Top 10 Weather Events by Economic Impact",
x = "Event Type", y = "Total Economic Impact (USD)")
- According to the analysis, flood cause the greatest economic losses, resulting in the highest combined damage to property and crops across the United States. Hurricane and tornadoes follow closely, contributing significantly to financial damage. These findings highlight the substantial economic risks posed by severe weather events, emphasizing the need for robust infrastructure and resource allocation to mitigate these impacts.