Reproducible Research project 2
priya malhotra
November 17, 2018
R Markdown
1.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
2.Data Processing
2.1 data
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
Storm Data -https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2 [47Mb]
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf
National Climatic Data Center Storm Events FAQ https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
2.2 Assignment
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. 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?
2.3 Process
2.3.1 Loading the data
The data was downloaded from the above mentioned website and saved on local computer. Then it was loaded on the R using the following code.
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionstorm<-read.csv("D:/profile/documents/GitHub/StormData.csv")
head(storm)## 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 62.4 Data Processing
2.4.1 Health Impact
To evaluate the health impact, the total fatalities and the total injuries for each event type (EVTYPE) are calculated.
storm_fatalities<- storm %>% select(EVTYPE,FATALITIES) %>% group_by(EVTYPE) %>% summarise(TOTAL_FATALITIES = sum(FATALITIES)) %>% arrange(-TOTAL_FATALITIES)
head(storm_fatalities,n=10)## # A tibble: 10 x 2
## EVTYPE TOTAL_FATALITIES
## <fct> <dbl>
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224storm_injuries<- storm %>% select(EVTYPE,INJURIES) %>% group_by(EVTYPE) %>% summarise(TOTAL_INJURIES = sum(INJURIES)) %>% arrange(-TOTAL_INJURIES)
head(storm_injuries, n=10)## # A tibble: 10 x 2
## EVTYPE TOTAL_INJURIES
## <fct> <dbl>
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 13612.4.2 Economic Impact
The data provides two types of economic impact, namely property damage (PROPDMG) and crop damage (CROPDMG). The actual damage in $USD is indicated by PROPDMGEXP and CROPDMGEXP parameters. The index in the PROPDMGEXP and CROPDMGEXP can be interpreted as the following:-
H, h -> hundreds = x100
K, K -> kilos = x1,000
M, m -> millions = x1,000,000
B,b -> billions = x1,000,000,000
(+) -> x1
(-) -> x0
(?) -> x0
blank -> x0
the total damage caused by each event type is calculated with the following code:
storm_damage<-storm[,c("EVTYPE", "PROPDMG", "PROPDMGEXP", "CROPDMG","CROPDMGEXP")]
code<- sort(unique(as.character(storm_damage$PROPDMGEXP)))
value<- c(0,0,0,1,10,10,10,10,10,10,10,10,10,10^9,10^2,10^2,10^3,10^6,10^6)
code_value<- data.frame(code, value)
storm_damage$prop.value<-code_value$value[match(storm_damage$PROPDMGEXP,code_value$code)]
storm_damage$crop.value<-code_value$value[match(storm_damage$CROPDMGEXP,code_value$code)]
storm_damage<-storm_damage %>% mutate(PROPDMG = PROPDMG*prop.value) %>% mutate(CROPDMG = CROPDMG*crop.value) %>% mutate(TOTALDMG = PROPDMG + CROPDMG)
storm_damage_total<- storm_damage %>% group_by(EVTYPE) %>% summarise(TOTALDMG.EVTYPE = sum(TOTALDMG)) %>% arrange(-TOTALDMG.EVTYPE)
head(storm_damage_total,n=10)## # A tibble: 10 x 2
## EVTYPE TOTALDMG.EVTYPE
## <fct> <dbl>
## 1 FLOOD 150319678250
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57352117607
## 4 STORM SURGE 43323541000
## 5 FLASH FLOOD 17562132111
## 6 DROUGHT 15018672000
## 7 HURRICANE 14610229010
## 8 RIVER FLOOD 10148404500
## 9 ICE STORM 8967041810
## 10 TROPICAL STORM 83822365502.5 Results
2.5.1 Health Impact
The top 10 events with the highest total fatalities and injuries are shown graphically.
plot1<- ggplot(storm_fatalities[1:10,], aes(x = EVTYPE, y = TOTAL_FATALITIES)) + geom_bar(stat = "identity")+ theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))+ggtitle("Top 10 Events with Highest Total Fatalities") +labs(x="EVENT TYPE", y="Total Fatalities")
plot1
plot2<-ggplot(storm_injuries[1:10,], aes(x = EVTYPE, y = TOTAL_INJURIES)) + geom_bar(stat = "identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5,hjust = 1))+ggtitle("Top 10 Events with Highest Total Injuries")+ labs(x= "EVENT TYPE", y="TOTAL INJURIES")
plot2
We can see from plots above Tornado caused maximum Fatalities and maximum Injuries .
2.5.2 Economic Impact
The top 10 events with the highest total economic damages (property and crop combined) are shown graphically.
plot3<- ggplot(storm_damage_total[1:10,], aes(x=EVTYPE, y= TOTALDMG.EVTYPE))+geom_bar(stat = "identity")+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))+ggtitle("Top 10 Events with Highest Economic Impact")+ labs(x= "EVENT TYPE", y= "TOTAL IMPACT (USD)")
plot3
As shown in plot3 Floods have maximum Economic Impact