Project 2: Storm Data Analysis_1950 to 2011
Tony Tu
2026-06-08
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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 come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
Storm Data (47 Mb): https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service: https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf
National Climatic Data Center Storm Events: https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf
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.
Here is the preview and summary of the data:
setwd("D:/Rstudio files/Reproducible Research_Project 1/Project 2")
url_data <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
url_variables <- "https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf"
url_FAQ <- "https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf"
## download data to my local folder
destFile <- "StormData.csv.bz2"
if (!file.exists(destFile)) {
download.file(url_data, destfile = destFile, method = "auto")
}
variable_File <- "repdata_peer2_doc_pd01016005curr.pdf"
if (!file.exists(variable_File)) {
download.file(url_variables, destfile = variable_File, method = "auto")
}
FAQ_File <- "repdata_peer2_doc_NCDC Storm Events-FAQ Page.pdf"
if (!file.exists(FAQ_File)) {
download.file(url_FAQ, destfile = FAQ_File, method = "auto")
}
## read the data file
data <- read.csv(destFile, stringsAsFactors = FALSE)
head(data)## 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 6Data Processing:
For Question 1:
Population health includes the impact of the event to FATALITIES and INJURIES. Thus, we need to calculate the impact of the event to the total events of fatalities and injuries.
## create a new dataset containing only the EVTYPE and a new variable Total_harm (=Fatalities+Injuries)
healthData <- data[, c("EVTYPE", "FATALITIES", "INJURIES")]
healthData$TOTAL_HARM <- healthData$FATALITIES + healthData$INJURIES
healthData <- healthData[!is.na(healthData$EVTYPE), ]
head(healthData)## EVTYPE FATALITIES INJURIES TOTAL_HARM
## 1 TORNADO 0 15 15
## 2 TORNADO 0 0 0
## 3 TORNADO 0 2 2
## 4 TORNADO 0 2 2
## 5 TORNADO 0 2 2
## 6 TORNADO 0 6 6## Aggregate by event type:
healthSummary <- aggregate(
TOTAL_HARM ~ EVTYPE,
data = healthData,
sum
)
healthSummary <- healthSummary[order(-healthSummary$TOTAL_HARM), ]
head(healthSummary, 15)## EVTYPE TOTAL_HARM
## 834 TORNADO 96979
## 130 EXCESSIVE HEAT 8428
## 856 TSTM WIND 7461
## 170 FLOOD 7259
## 464 LIGHTNING 6046
## 275 HEAT 3037
## 153 FLASH FLOOD 2755
## 427 ICE STORM 2064
## 760 THUNDERSTORM WIND 1621
## 972 WINTER STORM 1527
## 359 HIGH WIND 1385
## 244 HAIL 1376
## 411 HURRICANE/TYPHOON 1339
## 310 HEAVY SNOW 1148
## 957 WILDFIRE 986Result for Question 1:
make a plot to show the impact of events to population health
You can also embed plots, for example:
library(ggplot2)
# Select Top 15 Events
topEvents <- head(healthSummary, 15)
#Make a plot using ggplot
ggplot(topEvents, aes(x = reorder(EVTYPE, TOTAL_HARM), y = TOTAL_HARM)) +
geom_bar(stat = "identity", fill="blue") +
coord_flip() +
labs(
title = "Top Weather Events by Population Health Impact",
x = "Event Type",
y = "Total Harm (Fatalities & Injuries)"
) 
topEvents1 <- head(healthSummary[1, 1])Result 1: Across the United States, TORNADO events are the most harmful with respect to population health, resulting in the highest combined number of fatalities and injuries.
For Question 2:
Data processing:
Economic consequences includes “PROPDMG” (property damage value), “PROPDMGEXP” (property damage magnifitude), “CROPDMG” (crop damage value), and “CROPDMGEXP” (crop damage magnitude).
## create economic dagame variables:
ecoDMG <- data[, c("EVTYPE", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
head(ecoDMG)## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 25.0 K 0
## 2 TORNADO 2.5 K 0
## 3 TORNADO 25.0 K 0
## 4 TORNADO 2.5 K 0
## 5 TORNADO 2.5 K 0
## 6 TORNADO 2.5 K 0## Calculate the actual property damage:
ecoDMG$PROP_factor <- ifelse(ecoDMG$PROPDMGEXP == "K", 1000,
ifelse(ecoDMG$PROPDMGEXP=="M", 1000000,
ifelse(ecoDMG$PROPDMGEXP=="B", 1000000000, 1)))
ecoDMG$PROP_cost <- ecoDMG$PROPDMG * ecoDMG$PROP_factor
ecoDMG$CROP_factor <- ifelse(ecoDMG$CROPDMGEXP %in% c("K","k"), 1000,
ifelse(ecoDMG$CROPDMGEXP %in% c("M","m"), 1000000,
ifelse(ecoDMG$CROPDMGEXP %in% c("B","b"), 1000000000, 1)))
ecoDMG$CROP_cost <- ecoDMG$CROPDMG * ecoDMG$CROP_factor
## Aggregate by event type
ecoDMG$Total_ecoDMG <- ecoDMG$PROP_cost + ecoDMG$CROP_cost
ecoDMG <- ecoDMG[!is.na(ecoDMG$EVTYPE)
& !is.na(ecoDMG$PROPDMG) &
!is.na(ecoDMG$CROPDMG), ]
## select Top 15 economic damage events
ecoDMG_summary <- aggregate(Total_ecoDMG ~ EVTYPE, data = ecoDMG, sum)
ecoDMG_summary <- ecoDMG_summary[order(-ecoDMG_summary$Total_ecoDMG), ]
head(ecoDMG_summary, 15)## EVTYPE Total_ecoDMG
## 170 FLOOD 150319678257
## 411 HURRICANE/TYPHOON 71913712800
## 834 TORNADO 57340614060
## 670 STORM SURGE 43323541000
## 244 HAIL 18753321526
## 153 FLASH FLOOD 17562129167
## 95 DROUGHT 15018672000
## 402 HURRICANE 14610229010
## 590 RIVER FLOOD 10148404500
## 427 ICE STORM 8967041360
## 848 TROPICAL STORM 8382236550
## 972 WINTER STORM 6715441251
## 359 HIGH WIND 5908617595
## 957 WILDFIRE 5060586800
## 856 TSTM WIND 5038935845topDMG <- head(ecoDMG_summary, 15)
#Make a plot using ggplot
ggplot(topDMG,
aes(x=reorder(EVTYPE, Total_ecoDMG), y= Total_ecoDMG)) +
geom_bar(stat="identity", fill="red") +
coord_flip() +
labs(
title="Top Weather Events by Economic Damage Impact",
x= "Event Type",
y= "Total Economic Damage (Property & Crop)"
)
topEvent2 <- ecoDMG_summary[1, 1]Result 2: Across the United States, FLOOD has the greatest economic consequences, resulting in the highest costs of properties and crops.