Reproducible Research / Week 4 / Course Project 2
Assignment: Reproducible Research / Week 4 / Course Project 2
OVERVIEW
Weather events cause public health and economic problems for communities and municipalities. Severe events result in fatalities, injuries, and damage. Predicting and/or preventing these outcomes is a primary objective.
This analysis examines the damaging effects of severe weather conditions (e.g. hurricanes, tornadoes, thunderstorms, floods, etc.) on human populations and the econonomy in the U.S. from 1950 to 2011.
As a result, the analysis will highlight the severe weather events associated with the greatest impact on the economy and population health.
SYNOPSIS
This is an exploration of 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, which type of event, as well as the estimates of relevant fatalities, injuries, and various forms of damage.
The dataset used in this project is provided by the U.S. National Oceanic and Atmospheric Administration (NOAA).
This analysis discovered that tornados are responsible for a maximum number of fatalities and injuries.
This analysis also discoered that floods are responsbile for maximum property damage, while Droughts cause maximum crop damage.
Objective: Explore the NOAA Storm Database to help answer important questions about severe weather events.
DATA PROCESSING
if(!file.exists("stormdata.csv"))
{
bunzip2("stormData.csv.bz2","stormdata.csv",remove=F)
}
data <- read.csv("stormdata.csv",header=TRUE,sep=",")
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) :588294names(data)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"Result
Question 1
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
1.1 Variable selection (reducing the data set to only needed columns and variables)
variables<-c("EVTYPE","FATALITIES","INJURIES","PROPDMG", "PROPDMGEXP","CROPDMG","CROPDMGEXP")
strmdata<-data[variables]
dim(strmdata)## [1] 902297 7names(strmdata)## [1] "EVTYPE" "FATALITIES" "INJURIES" "PROPDMG" "PROPDMGEXP"
## [6] "CROPDMG" "CROPDMGEXP"1.2 Reviewing events that cause the most fatalities ( The Top-10 Fatalities by Weather Event )
## Procedure = aggregate the top 10 fatalities by the event type and sort the output in descending order
Fatalities <- aggregate(FATALITIES ~ EVTYPE, data = strmdata, FUN = sum)
Top10_Fatalities <- Fatalities[order(-Fatalities$FATALITIES), ][1:10, ]
Top10_Fatalities ## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 2241.3 Reviewing events that cause the most injuries ( The Top-10 Injuries by Weather Event )
## Procedure = aggregate the top 10 injuries by the event type and sort the output in descending order
Injuries <- aggregate(INJURIES ~ EVTYPE, data = strmdata, FUN = sum)
Top10_Injuries <- Injuries[order(-Injuries$INJURIES), ][1:10, ]
Top10_Injuries ## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100
## 427 ICE STORM 1975
## 153 FLASH FLOOD 1777
## 760 THUNDERSTORM WIND 1488
## 244 HAIL 13611.4 Plot of Top 10 Fatalities & Injuries for Weather Event Types ( Population Health Impact )
## Proecedure = plot graphs showing the top 10 fatalities and injuries
par(mfrow=c(1,2),mar=c(10,3,3,2))
barplot(Top10_Fatalities$FATALITIES,names.arg=Top10_Fatalities$EVTYPE,las=2,col="purple",ylab="fatalities",main="Top 10 fatalities")
barplot(Top10_Injuries$INJURIES,names.arg=Top10_Injuries$EVTYPE,las=2,col="purple",ylab="injuries",main="Top 10 Injuries")
Figure 1: The weather event responsbile for the highest fatalities and injuries is the ‘Tornado’
Question 2
Across the United States, which types of events have the greatest economic consequences?
# Economic consequences means damages, looking apoun the data set we can find columns related to damages as property damage(PROPDMG) and crop damage(CROPDMG) and there are two more columns releated to these columns as PROPDMGEXP and CROPDMGEXP which are the exponent for the PROPDMG and CROPDMG columns respectively.
# We will need to first calculate the actual damange by converting exponent columns in numeric data
## Property damage exponents for each level listed out & assigned those values for the property exponent data.
## Invalid data was excluded by assigning the value as '0'.
## Then, the property damage value was calculated by multiplying the property damage and property exponent value.
unique(strmdata$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m M# Assigning values for the property exponent strmdata
strmdata$PROPEXP[strmdata$PROPDMGEXP == "K"] <- 1000
strmdata$PROPEXP[strmdata$PROPDMGEXP == "M"] <- 1e+06
strmdata$PROPEXP[strmdata$PROPDMGEXP == ""] <- 1
strmdata$PROPEXP[strmdata$PROPDMGEXP == "B"] <- 1e+09
strmdata$PROPEXP[strmdata$PROPDMGEXP == "m"] <- 1e+06
strmdata$PROPEXP[strmdata$PROPDMGEXP == "0"] <- 1
strmdata$PROPEXP[strmdata$PROPDMGEXP == "5"] <- 1e+05
strmdata$PROPEXP[strmdata$PROPDMGEXP == "6"] <- 1e+06
strmdata$PROPEXP[strmdata$PROPDMGEXP == "4"] <- 10000
strmdata$PROPEXP[strmdata$PROPDMGEXP == "2"] <- 100
strmdata$PROPEXP[strmdata$PROPDMGEXP == "3"] <- 1000
strmdata$PROPEXP[strmdata$PROPDMGEXP == "h"] <- 100
strmdata$PROPEXP[strmdata$PROPDMGEXP == "7"] <- 1e+07
strmdata$PROPEXP[strmdata$PROPDMGEXP == "H"] <- 100
strmdata$PROPEXP[strmdata$PROPDMGEXP == "1"] <- 10
strmdata$PROPEXP[strmdata$PROPDMGEXP == "8"] <- 1e+08
# Assigning '0' to invalid exponent strmdata
strmdata$PROPEXP[strmdata$PROPDMGEXP == "+"] <- 0
strmdata$PROPEXP[strmdata$PROPDMGEXP == "-"] <- 0
strmdata$PROPEXP[strmdata$PROPDMGEXP == "?"] <- 0
# Calculating the property damage value
strmdata$PROPDMGVAL <- strmdata$PROPDMG * strmdata$PROPEXP
# Q2.3 Defining & Calcuating [ Crop Damage ]
## Crop damage exponents for each level listed out & assigned those values for the crop exponent data.
## Invalid data was excluded by assigning the value as '0'.
## Then, the crop damage value was calculated by multiplying the crop damage and crop exponent value.
unique(strmdata$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m M# Assigning values for the crop exponent strmdata
strmdata$CROPEXP[strmdata$CROPDMGEXP == "M"] <- 1e+06
strmdata$CROPEXP[strmdata$CROPDMGEXP == "K"] <- 1000
strmdata$CROPEXP[strmdata$CROPDMGEXP == "m"] <- 1e+06
strmdata$CROPEXP[strmdata$CROPDMGEXP == "B"] <- 1e+09
strmdata$CROPEXP[strmdata$CROPDMGEXP == "0"] <- 1
strmdata$CROPEXP[strmdata$CROPDMGEXP == "k"] <- 1000
strmdata$CROPEXP[strmdata$CROPDMGEXP == "2"] <- 100
strmdata$CROPEXP[strmdata$CROPDMGEXP == ""] <- 1
# Assigning '0' to invalid exponent strmdata
strmdata$CROPEXP[strmdata$CROPDMGEXP == "?"] <- 0
# calculating the crop damage
strmdata$CROPDMGVAL <- strmdata$CROPDMG * strmdata$CROPEXP
# Q2.4 Property Damage Summary
## Procedure = aggregate the property damage by the event type and sort the output it in descending order
prop <- aggregate(PROPDMGVAL~EVTYPE,data=strmdata,FUN=sum,na.rm=TRUE)
prop <- prop[with(prop,order(-PROPDMGVAL)),]
prop <- head(prop,10)
#print(prop)# Q2.5 Crop Damage Summary
## Procedure = aggregate the crop damage by the event type and sort the output it in descending order
crop <- aggregate(CROPDMGVAL~EVTYPE,data=strmdata,FUN=sum,na.rm=TRUE)
crop <- crop[with(crop,order(-CROPDMGVAL)),]
crop <- head(crop,10)
#print(crop)# Q2.6 Plot of Top 10 Property & Crop damages by Weather Event Types ( Economic Consequences )
##plot the graph showing the top 10 property and crop damages
par(mfrow=c(1,2),mar=c(11,3,3,2))
barplot(prop$PROPDMGVAL/(10^9),names.arg=prop$EVTYPE,las=2,col="gold",ylab="Prop.damage(billions)",main="Top10 Prop.Damages")
barplot(crop$CROPDMGVAL/(10^9),names.arg=crop$EVTYPE,las=2,col="gold",ylab="Crop damage(billions)",main="Top10 Crop.Damages")
Figure 2: ‘Floods’ are responsbile for the highest property damage while ‘droughts’ cause the greatest crop damage.
Summary of Conclusions
Tornados are responsible for the maximum number of fatalities and injuries, followed by Excessive Heat for fatalities and Thunderstorm wind for injuries.
Floods are responsbile for maximum property damage, while Droughts cause maximum crop damage. Second major events that caused the maximum damage was Hurricanes/Typhoos for property damage and Floods for crop damage.