Exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database
Wing Chum
March 12, 2017
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.
Synopsis
Using the data from National Oceanic and Atmospheric Administration’s (NOAA), we looked at data from 1950 to 2011. Based on the data from NOAA, it shows that Tornado is the most harmful weather event where flooding have the greatest econmic consequences.
Library Setup
library("ggplot2")
library("gridExtra")## Warning: package 'gridExtra' was built under R version 3.3.3Data Processing
The storm data can be found here: https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
Data used in this analysis was downloaded on Mar 12, 2017.
## Load the data into stormData
if (!exists("stormData")) {
# Extract file if it is not already extracted
if (file.exists("repdata-data-StormData.csv.bz2")) {
# Read data into the varirable called stormData
stormData <- read.csv("repdata-data-StormData.csv.bz2", sep = ",")
}
}Summary of the data
summary(stormData)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## 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 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295head(stormData)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6There are two questions we want to answer.
1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
2: Across the United States, which types of events have the greatest economic consequences?
Now lets look at the data by different event type to help answer the first question
stormEvent <- stormData[, c("BGN_DATE", "EVTYPE", "FATALITIES", "INJURIES",
"PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
eventHealth <- subset(stormEvent, !stormEvent$FATALITIES == 0 & !stormEvent$INJURIES ==
0, select = c(EVTYPE, FATALITIES, INJURIES))
eventEconomic <- subset(stormEvent, !stormEvent$PROPDMG == 0 & !stormEvent$CROPDMG ==
0, select = c(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))
# Create separate data set for Injury and Fatalities Fatalities
eventHealth_Death <- aggregate(eventHealth$FATALITIES, by = list(eventHealth$EVTYPE),
FUN = sum)
# Give proper name for columns
colnames(eventHealth_Death) <- c("EVENTTYPE", "FATALITIES")
# Injury
eventHealth_Inj <- aggregate(eventHealth$INJURIES, by = list(eventHealth$EVTYPE),
FUN = sum)
# Give column name
colnames(eventHealth_Inj) <- c("EVENTTYPE", "INJURIES")
# Let's reorder 2 dataset and filter top 5 events for both dataset
eventHealth_Death <- eventHealth_Death[order(eventHealth_Death$FATALITIES, decreasing = TRUE),
][1:5, ]
eventHealth_Inj <- eventHealth_Inj[order(eventHealth_Inj$INJURIES, decreasing = TRUE),
][1:5, ]Results
Lets plot out the data to answer the first questions (which type of event is most harmful)
# Plot the number of death
Death_plot <- ggplot() + geom_bar(data = eventHealth_Death, aes(x = EVENTTYPE,
y = FATALITIES, fill = interaction(FATALITIES, EVENTTYPE)), stat = "identity",
show.legend = F) + theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Harmful Events") + ylab("No. of fatailities") + ggtitle("Top 5 weather events causing fatalities") +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
# Plot the number of injuries
Inj_plot <- ggplot() + geom_bar(data = eventHealth_Inj, aes(x = EVENTTYPE, y = INJURIES,
fill = interaction(INJURIES, EVENTTYPE)), stat = "identity", show.legend = F) +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) + xlab("Harmful Events") +
ylab("No. of Injuries") + ggtitle("Top 5 weather events causing Injuries") +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
# Draw two plots generated above dividing space in two columns
grid.arrange(Death_plot, Inj_plot, nrow = 2)
Base on the above graph, we can see that Tornado is the most harmful type of event.
Lets plot of the graph for the damage to answer the second question (which type of event have the greatest economic consequences?)
# select required entries for economy
eventEconomic <- subset(eventEconomic, eventEconomic$PROPDMGEXP == "K" | eventEconomic$PROPDMGEXP ==
"k" | eventEconomic$PROPDMGEXP == "M" | eventEconomic$PROPDMGEXP == "m" |
eventEconomic$PROPDMGEXP == "B" | eventEconomic$PROPDMGEXP == "b")
eventEconomic <- subset(eventEconomic, eventEconomic$CROPDMGEXP == "K" | eventEconomic$CROPDMGEXP ==
"k" | eventEconomic$CROPDMGEXP == "M" | eventEconomic$CROPDMGEXP == "m" |
eventEconomic$CROPDMGEXP == "B" | eventEconomic$CROPDMGEXP == "b")
# Convert ecnomic values to number
eventEconomic$PROPDMGEXP <- gsub("m", 1e+06, eventEconomic$PROPDMGEXP, ignore.case = TRUE)
eventEconomic$PROPDMGEXP <- gsub("k", 1000, eventEconomic$PROPDMGEXP, ignore.case = TRUE)
eventEconomic$PROPDMGEXP <- gsub("b", 1e+09, eventEconomic$PROPDMGEXP, ignore.case = TRUE)
eventEconomic$PROPDMGEXP <- as.numeric(eventEconomic$PROPDMGEXP)
eventEconomic$CROPDMGEXP <- gsub("m", 1e+06, eventEconomic$CROPDMGEXP, ignore.case = TRUE)
eventEconomic$CROPDMGEXP <- gsub("k", 1000, eventEconomic$CROPDMGEXP, ignore.case = TRUE)
eventEconomic$CROPDMGEXP <- gsub("b", 1e+09, eventEconomic$CROPDMGEXP, ignore.case = TRUE)
eventEconomic$CROPDMGEXP <- as.numeric(eventEconomic$CROPDMGEXP)
eventEconomic$PROPDMGEXP <- as.numeric(eventEconomic$PROPDMGEXP)
# then sum the damages by each event type
eventEconomic$TOTALDMG <- (eventEconomic$CROPDMG * eventEconomic$CROPDMGEXP) +
(eventEconomic$PROPDMG * eventEconomic$PROPDMGEXP)
eventEconomic <- aggregate(eventEconomic$TOTALDMG, by = list(eventEconomic$EVTYPE),
FUN = sum)
colnames(eventEconomic) <- c("EVTYPE", "TOTALDMG")
# Rank the event type by highest damage cost and take top 5 columns
eventEconomic <- eventEconomic[order(eventEconomic$TOTALDMG, decreasing = TRUE),
]
eventEconomic <- eventEconomic[1:5, ]
ggplot() + geom_bar(data = eventEconomic, aes(x = EVTYPE, y = TOTALDMG, fill = interaction(TOTALDMG,
EVTYPE)), stat = "identity", show.legend = F) + theme(axis.text.x = element_text(angle = 30,
hjust = 1)) + xlab("Event Type") + ylab("Total Damage")
Base on the above graph, we can see that flood have the greatest econmic consequences.
Summary
Base on the data from 1950 to 20111, Tornado is the most harmful event in the United States and Flood have the greatest econmic consequences.