Reproducible Research: Peer Assessment 2
Saiteja
19 July 2015
An analysis report on the impact of storms on the economy and health
Synopsis
This report analyses the impact of various storm events on the public health and economy of the United States using the data from National Oceanic and Atmospheric Administration (NOAA) for the years 1950 to 2011. The data will be used to predict which type of storm event is the most devastating on both economy and public health.
Data Processing
Loading the data
#Setting the Working Directory
setwd("/Users/saiteja/Documents/Coursera/reproducible research/peer ass 2/")
### Downloading the file if it does not exist
if(!file.exists("StormData.csv.bz2")){
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2","StormData.csv.bz2")
}
#Unzipping the file and reading the csv
storm_RawData <- read.csv(bzfile("StormData.csv.bz2"),header = TRUE)
summary(storm_RawData)## 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) :588295Considering the data collected after the year 1980 as there are very few recordings before this year
#Extract Year value as numeric from BGN_DATE of raw data
storm_RawData$date <- as.numeric(format(as.Date(storm_RawData$BGN_DATE,format="%m/%d/%Y%H:%M:%S"),"%Y"))
#Storing all the data after 1980 in storm_after80 variable
storm_after80 <- storm_RawData[storm_RawData$date >1980,]1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
So, we need to group data that has caused injuries and were fatal.We first sort the data with number of fatalities by each event in descending order.
fatalities <- aggregate(FATALITIES ~ EVTYPE, data = storm_after80, sum)
fatalities <- fatalities[order(-fatalities$FATALITIES),]
fatalities_20 <- fatalities[1:20,]
injuries <- aggregate(INJURIES ~ EVTYPE, data = storm_after80, sum)
injuries <- injuries[order(-injuries$INJURIES),]
injuries_20 <- injuries[1:20,]2. Across the United States, which types of events have the greatest economic consequences?
Both Property and Crop damages are listed along with a Multiplication factor PROPDMGEXP and CROPDMGEXP respectively. To get the actual numeric value of the damages, we need to comvert PROPDMG and CROPDMG by multiplying with the exponent. First, we need to identify all the charcters in PROPDMGEXP and CROPDMGEXP. So,
unique(storm_after80$PROPDMGEXP)## [1] M K 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 Munique(storm_after80$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MAssigining respective numeric values to the exponents.
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP==""]<- 1
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="-"]<- 0
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="?"]<- 0
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="+"]<- 0
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="0"]<- 1
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="1"]<- 1
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="2"]<- 100
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="3"]<- 1000
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="4"]<- 10000
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="5"]<- 1e+05
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="6"]<- 1e+06
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="7"]<- 1e+07
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="8"]<- 1e+08
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="B"]<- 1e+09
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="h"]<- 100
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="H"]<- 100
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="K"]<- 1000
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="M"]<- 1e+06
storm_after80$PROPDMGEXP_NUM[storm_after80$PROPDMGEXP=="m"]<- 1e+06
storm_after80$PROPDMG_WITH_EXP<- storm_after80$PROPDMG*
storm_after80$PROPDMGEXP_NUMstorm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP==""]<- 1
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="?"]<- 0
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="0"]<- 1
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="2"]<- 100
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="B"]<- 1e+09
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="k"]<- 1000
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="K"]<- 1000
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="m"]<- 1e+06
storm_after80$CROPDMGEXP_NUM[storm_after80$CROPDMGEXP=="M"]<- 1e+06
storm_after80$CROPDMG_WITH_EXP <- storm_after80$CROPDMG *
storm_after80$CROPDMGEXP_NUMAggregating the damages and sorting them in descending order to pick the first twenty observations.
prop_damage <- aggregate(PROPDMG_WITH_EXP ~ EVTYPE, data = storm_after80, sum)
prop_damage <- prop_damage[order(-prop_damage$PROPDMG_WITH_EXP),]
prop_damage_20 <- prop_damage[1:20,]crop_damage <- aggregate(CROPDMG_WITH_EXP ~ EVTYPE, data = storm_after80, sum)
crop_damage <- crop_damage[order(-crop_damage$CROPDMG_WITH_EXP),]
crop_damage_20 <- crop_damage[1:20,]Results:
library(ggplot2)
p_fat <- ggplot(fatalities_20,aes(x = factor(fatalities_20$EVTYPE),y = fatalities_20$FATALITIES))
p_fat<- p_fat + geom_bar(aes(fill = factor(fatalities_20$EVTYPE)),stat="identity")
p_fat <- p_fat + theme(axis.text.x = element_text(angle = 90, hjust = 1))
p_fat <- p_fat + xlab("Event Type") + ylab("Number of fatalities")
p_fat <- p_fat + guides(fill=FALSE) + coord_flip()library(ggplot2)
p_inj <- ggplot(injuries_20,aes(x = factor(injuries_20$EVTYPE),y = injuries_20$INJURIES))
p_inj<- p_inj + geom_bar(aes(fill = factor(injuries_20$EVTYPE)),stat="identity")
p_inj <- p_inj + theme(axis.text.x = element_text(angle = 90, hjust = 1))
p_inj <- p_inj + xlab("Event Type") + ylab("Number of Injuries")
p_inj <- p_inj + guides(fill=FALSE) +coord_flip()Plot showing the number of fatalities and injuries caused by event type:
library(gridExtra)
## Making the plots side by side
grid.arrange(p_fat,p_inj,ncol =2, top = "Fatalities & Injuries caused by different natural storm events")
In both the cases, the most harmful storm event is Tornado.
Plot of property and crop damage with the event type:
prop_p <- ggplot(prop_damage_20,aes(x = factor(prop_damage_20$EVTYPE),y = prop_damage_20$PROPDMG_WITH_EXP))
prop_p<- prop_p + geom_bar(aes(fill = factor(prop_damage_20$EVTYPE)),stat="identity")
prop_p <- prop_p + theme(axis.text.x = element_text(angle = 90, hjust = 1))
prop_p <- prop_p + xlab("Event Type") + ylab("Property Damage")
prop_p <- prop_p + guides(fill=FALSE) +coord_flip()crop_p <- ggplot(crop_damage_20,aes(x = factor(crop_damage_20$EVTYPE),y = crop_damage_20$CROPDMG_WITH_EXP))
crop_p<- crop_p + geom_bar(aes(fill = factor(crop_damage_20$EVTYPE)),stat="identity")
crop_p <- crop_p + theme(axis.text.x = element_text(angle = 90, hjust = 1))
crop_p <- crop_p + xlab("Event Type") + ylab("Crop Damage")
crop_p <- crop_p + guides(fill=FALSE) +coord_flip()library(gridExtra)
## Making the plots side by side
grid.arrange(prop_p,crop_p,ncol =2, top = "Property & Crop Damage caused by different natural storm events" )
Most propery damage is caused by Floods while the crop damage is most by Drought.
Conclusion
he data acquired from “National Oceanic & Atmospheric Administration” shows the following effects of storm events on health and economy of USA after 1980:
The population health is most effected by Tornadoes followed by Excessive Heat.
The property damage is most due to the Floods followed by Typhoons/Hurricanes.
The crop damage is most due to the Drought followed by Floods