Harmful Events
Sergios Koutavidis
11 Ιουνίου 2016
Title: Tornadoes, thunderstorm winds, floods and excessive heat are the most harmful events to human health while flood, hurricane, tornado, storm surge and hail have the direst economic consequences.
Synopsis
Severe weather events cause not only health related problems but can have great econimic consequences. We derived data from U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database and we examine the types of events that are most harmful with respect to population health , and yet which types of events have the the greatest economic consequences
Data processing
An analysis of how different types of natural events have impact on human health and property damage.
library(dplyr)## Warning: package 'dplyr' was built under R version 3.2.5##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondata <- read.csv("file:///C:/Users/skoutavidi001/Documents/coursera/Reproducible2/repdata-data-StormData.csv")
head(data)## 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 6data$EVTYPE = toupper(data$EVTYPE)
Data_names=names(data)Next steps is the tranformation of event types to get unique names throughout our analysis according to guidelines
Results
FATALITIES and INJURIES are both related with human health so we present them in decreasing order
Harmful_Events <- aggregate(FATALITIES ~ EVTYPE, data = data, sum)
Harmful_Events_SUM<-Harmful_Events[Harmful_Events$FATALITIES>0,]
Harmful_Order<- Harmful_Events_SUM[order(Harmful_Events_SUM$FATALITIES,decreasing=TRUE),]
head(Harmful_Order)## EVTYPE FATALITIES
## 755 TORNADO 5633
## 116 EXCESSIVE HEAT 1903
## 138 FLASH FLOOD 978
## 243 HEAT 937
## 417 LIGHTNING 816
## 683 THUNDERSTORM WIND 701Injuries_Events <- aggregate(INJURIES ~ EVTYPE, data = data, sum)
Injuries_Events_SUM<-Injuries_Events[Injuries_Events$INJURIES>0,]
Injuries_Order<- Injuries_Events_SUM[order(Injuries_Events_SUM$INJURIES,decreasing=TRUE),]
head(Injuries_Order)## EVTYPE INJURIES
## 755 TORNADO 91346
## 683 THUNDERSTORM WIND 9353
## 154 FLOOD 6791
## 116 EXCESSIVE HEAT 6525
## 417 LIGHTNING 5230
## 243 HEAT 2100Utilizing the above analysis we depict 2 plots indicating top 10 events causing more Fatalities and Injuries respectively
barplot(Harmful_Order[1:10, 2], col = heat.colors(10), legend.text = Harmful_Order[1:10,
1], ylab = "Fatality", main = "Top 10 events causing most fatalities")
barplot(Injuries_Order[1:10, 2], col = heat.colors(10), legend.text = Injuries_Order[1:10,
1], ylab = "Injury", main = "Top 10 events causing most injuries")
Next thing to be determined is which events cause both Fatalies and Injuries
intersect(Harmful_Order[1:10,1],Injuries_Order[1:10,1])## [1] "TORNADO" "EXCESSIVE HEAT" "FLASH FLOOD"
## [4] "HEAT" "LIGHTNING" "THUNDERSTORM WIND"
## [7] "FLOOD"Result 1:
From 7 major types of events listed in the top 10 causes of fatalities and body injuries, tornadoes are the most harmful event to human health while others like exccesive heat, flash flood, and thunderstorm wind area come next.
Property most harmful events
We examine the uniqueness of property of most harmful events
unique(data$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Munique(data$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MTransformation is required according to guidelines in order to be able to have accurate Total results , as follows: 1) “K” stands for Thousands 2) “M” stands for Millions 3) “B” stands for Billions
{ r transformations2} data[data$PROPDMGEXP == "K", ]$PROPDMG <- data[data$PROPDMGEXP == "K", ]$PROPDMG * 1000 data[data$PROPDMGEXP == "M", ]$PROPDMG <- data[data$PROPDMGEXP == "M", ]$PROPDMG * 1e+06 data[data$PROPDMGEXP == "m", ]$PROPDMG <- data[data$PROPDMGEXP == "m", ]$PROPDMG * 1e+06 data[data$PROPDMGEXP == "B", ]$PROPDMG <- data[data$PROPDMGEXP == "B", ]$PROPDMG * 1e+09 data[data$CROPDMGEXP == "K", ]$CROPDMG <- data[data$CROPDMGEXP == "K", ]$CROPDMG * 1000 data[data$CROPDMGEXP == "k", ]$CROPDMG <- data[data$CROPDMGEXP == "k", ]$CROPDMG * 1000 data[data$CROPDMGEXP == "M", ]$CROPDMG <- data[data$CROPDMGEXP == "M", ]$CROPDMG * 1e+06 data[data$CROPDMGEXP == "m", ]$CROPDMG <- data[data$CROPDMGEXP == "m", ]$CROPDMG * 1e+06 data[data$CROPDMGEXP == "B", ]$CROPDMG <- data[data$CROPDMGEXP == "B", ]$CROPDMG *1e+09
Property and Crop damage type of events are aggregated and ordered respectively
Property_damage <- aggregate(PROPDMG ~ EVTYPE, data = data, sum)
Property_damage_Pos<- Property_damage[Property_damage$PROPDMG>0,]
Property_damage_Order<- Property_damage_Pos[order(Property_damage_Pos$PROPDMG, decreasing = TRUE), ]
Crop_damage_Total <- aggregate(CROPDMG ~ EVTYPE, data = data, sum)
Crop_damage_Total_Pos<- Crop_damage_Total[Crop_damage_Total$CROPDMG>0,]
Cop_damage_Total_Order<- Crop_damage_Total_Pos[order(Crop_damage_Total_Pos$CROPDMG, decreasing = TRUE), ]A Total is calculated by mergning these 2 together
Total_damage <- merge(Property_damage_Order, Cop_damage_Total_Order, by = "EVTYPE")
Total_damage$Total<- Total_damage$PROPDMG+Total_damage$CROPDMG
Total_damage_Order<- Total_damage[order(Total_damage$Total, decreasing = TRUE), ]A plot is showing top 5 natural events causing major economic consequences
barplot(Total_damage_Order[1:5, 2], col = terrain.colors(5), legend.text = Total_damage_Order[1:5,
1], ylab = "Damage", main = "5 natural events causing major economic consequences")
Result 2: Flood , turricane, tornado , storm surge and haail are the 5 natural events causing major economic consequences
Final Results
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? TORNADO ,EXCESSIVE HEAT,FLASh FLOOD,HEAT and LIGHTNING.
Across the United States, which types of events have the greatest economic consequences? FLOOD ,HURRICANE-TYPHOON,TORNADO,STORM SURGE and HAIL.