Storm Data analysis for loss of health and economy in US for the period(1950-2011).
Synopsis: We have analyzed date U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. To find what are the event types which cause most of the damage to human health and damages our econonmy so that we can tackle and assign our resources to lessen the adverse effect.
Analysis shows that most of the human Fatalities are caused by Tornado followed by Excessive Heat and most of the human Injuries are caused by Tornado followed by TSTM WIND Where as most the damages to economy is caused by Flood followed by HURRICANE/TYPHOON .
Data Processing
library(dplyr)
library(ggplot2)
library(R.utils)Reading Data
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, "StormData.csv.bz2")
bunzip2("StormData.csv.bz2", "StormData.csv")
data=read.csv("StormData.csv")Summary of Data
summary(data)## 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 : 569 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588294Processing data for Analysis
newdata=select(data,FATALITIES,INJURIES,EVTYPE,PROPDMG,CROPDMG,PROPDMGEXP,CROPDMGEXP)
healthfat=newdata%>%select(FATALITIES,EVTYPE)%>%group_by(EVTYPE)%>%summarise(totalfat=sum(FATALITIES))%>%arrange(-totalfat)
head(healthfat)## # A tibble: 6 x 2
## EVTYPE totalfat
## <fct> <dbl>
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504healthinj=newdata%>%select(INJURIES,EVTYPE)%>%group_by(EVTYPE)%>%summarise(totalinj=sum(INJURIES))%>%arrange(-totalinj)
head(healthinj)## # A tibble: 6 x 2
## EVTYPE totalinj
## <fct> <dbl>
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100economicdata=select(newdata,EVTYPE,PROPDMG,CROPDMG,PROPDMGEXP,CROPDMGEXP)
economicdata=subset(economicdata,economicdata$PROPDMGEXP=="k"|economicdata$PROPDMGEXP=="K"|economicdata$PROPDMGEXP=="m"|economicdata$PROPDMGEXP=="M"|economicdata$PROPDMGEXP=="b"|economicdata$PROPDMGEXP=="B")
economicdata=subset(economicdata,economicdata$CROPDMGEXP=="k"|economicdata$CROPDMGEXP=="K"|economicdata$CROPDMGEXP=="m"|economicdata$CROPDMGEXP=="M"|economicdata$CROPDMGEXP=="b"|economicdata$CROPDMGEXP=="B")
head(economicdata)## EVTYPE PROPDMG CROPDMG PROPDMGEXP CROPDMGEXP
## 187566 HURRICANE OPAL/HIGH WINDS 0.1 10 B M
## 187571 THUNDERSTORM WINDS 5.0 500 M K
## 187581 HURRICANE ERIN 25.0 1 M M
## 187583 HURRICANE OPAL 48.0 4 M M
## 187584 HURRICANE OPAL 20.0 10 m m
## 187653 THUNDERSTORM WINDS 50.0 50 K KDecrypting numerical value of symbol to calculate damages
economicdata$PROPDMGEXP=gsub("m",1e+06,economicdata$PROPDMGEXP,ignore.case = TRUE)
economicdata$PROPDMGEXP=gsub("k",1000,economicdata$PROPDMGEXP,ignore.case = TRUE)
economicdata$PROPDMGEXP=gsub("b",1e+09,economicdata$PROPDMGEXP,ignore.case = TRUE)
economicdata$CROPDMGEXP=gsub("m",1e+06,economicdata$CROPDMGEXP,ignore.case = TRUE)
economicdata$CROPDMGEXP=gsub("k",1000,economicdata$CROPDMGEXP,ignore.case = TRUE)
economicdata$CROPDMGEXP=gsub("b",1e+09,economicdata$CROPDMGEXP,ignore.case = TRUE)
economicdata$PROPDMGEXP=as.numeric(economicdata$PROPDMGEXP)
economicdata$CROPDMGEXP=as.numeric(economicdata$CROPDMGEXP)
economicdata$totalloss=(economicdata$PROPDMG*economicdata$PROPDMGEXP)+(economicdata$CROPDMG*economicdata$CROPDMGEXP)
economyloss=economicdata%>%group_by(EVTYPE)%>%summarise(totalloss=sum(totalloss))%>%arrange(-totalloss)Ranking Fatalities and Injuries to human caused by Weather events.
healthfat=healthfat[1:5,]
healthfat## # A tibble: 5 x 2
## EVTYPE totalfat
## <fct> <dbl>
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816healthinj=healthinj[1:5,]
healthinj## # A tibble: 5 x 2
## EVTYPE totalinj
## <fct> <dbl>
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230Ranking Total damages to econmony caused by Weather events.
economyloss=economyloss[1:5,]
economyloss## # A tibble: 5 x 2
## EVTYPE totalloss
## <fct> <dbl>
## 1 FLOOD 138007444500
## 2 HURRICANE/TYPHOON 29348167800
## 3 TORNADO 16520148150
## 4 HURRICANE 12405268000
## 5 RIVER FLOOD 10108369000Plots Fatalilities Plot
fat_plot=ggplot()+geom_bar(data = healthfat,aes(x=EVTYPE,y=totalfat),stat = "identity",show.legend = FALSE)+xlab("Harmful effect")+ylab("Fatalities")+ggtitle("Top 5 event causing fatalities in US")
fat_plot
Injuries Plot
inj_plot=ggplot()+geom_bar(data = healthinj,aes(x=EVTYPE,y=totalinj),stat = "identity",show.legend = FALSE)+xlab("Harmful effect")+ylab("Injuries")+ggtitle("Top 5 event causing injuries in US")
inj_plot
Damages to Economy plot
eco_plot=ggplot()+geom_bar(data = economyloss,aes(x=EVTYPE,y=totalloss),stat = "identity",show.legend = FALSE)+ xlab("Harmful effect")+ ylab("Total Damage")+ggtitle("Top 5 event causing economic damages in US")
eco_plot
Summary: As we can clearly say with Ranking and Plot, most of the human Fatalities are caused by Tornado followed by Excessive Heat and most of the human Injuries are caused by Tornado followed by TSTM WIND Where as most the damages to economy is caused by Flood followed by HURRICANE/TYPHOON ..