Storm Analysis
Bhavna Gandhi
15 February 2018
Synopsis
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.
library("ggplot2")
library("gridExtra")
library("R.utils")## Loading required package: R.oo## Loading required package: R.methodsS3## R.methodsS3 v1.7.1 (2016-02-15) successfully loaded. See ?R.methodsS3 for help.## R.oo v1.21.0 (2016-10-30) successfully loaded. See ?R.oo for help.##
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## cat, commandArgs, getOption, inherits, isOpen, parse, warningsR Markdown
stormData <- read.csv(file="G:/R_Work/StormData/repdata-data-StormData.csv.bz2",header=TRUE)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) :588295Data Processing
Trim data set for both questions
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 ))Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Create separate data set for Injury and Fatalities Fatalities
eventHealth$FATALITIES <- as.numeric(eventHealth$FATALITIES)
eventHealth$INJURIES <- as.numeric(eventHealth$INJURIES)
eventHealth_Death <- aggregate(eventHealth$FATALITIES, by = list(eventHealth$EVTYPE), FUN =sum)
colnames(eventHealth_Death) <- c("EVENTTYPE", "FATALITIES")
eventHealth_Inj <- aggregate(eventHealth$INJURIES, by = list(eventHealth$EVTYPE), FUN = sum)
colnames(eventHealth_Inj) <- c("EVENTTYPE", "INJURIES")
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
Populate the top 5 major cause of Both fatalities and injuriees
# plot top 5 events for fatalities and injuries
# Plot Fatalities and store at Death_plot
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 injuries and store at variable Inj_plot
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))
grid.arrange(Death_plot, Inj_plot, ncol = 2)
## We can easily see that Tornado is the major cause with respect to population health, both for causing fatalities and injuries.
2. Across the United States, which types of events have the greatest economic consequences?
Data Processing
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)
eventEconomic$TOTALDMG <- (eventEconomic$CROPDMG * eventEconomic$CROPDMGEXP) + (eventEconomic$PROPDMG * eventEconomic$PROPDMGEXP)
eventEconomic$TOTALDMG <- as.numeric(eventEconomic$TOTALDMG)
eventEconomic_DMG <- cbind(eventEconomic,eventEconomic$TOTALDMG)
#eventEconomic_DMG <- aggregate(eventEconomic$TOTALDMG, by = list(eventEconomic$EVTYPE), FUN = sum)
colnames(eventEconomic_DMG) <- c("EVTYPE", "TOTALDMG")
#
#eventEconomic <- aggregate(eventEconomic$TOTALDMG, by = list(eventEconomic$EVTYPE), FUN = sum)
#eventEconomic <- eventEconomic[order(eventEconomic$TOTALDMG), decreasing = TRUE,]
eventEconomic <- eventEconomic[1:5, ]Results
Now plot the graph
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")