title: “Harmful Weather Events” author: “Christopher” date: “July 21, 2019” output: html_document ============================================================================= sYNOPSIS ============================================================================= Across the United States, excessive heat,tornadoes and flash floods are most harmful with respect to population health.
Across the United States, tornadoes,thunderstorm, winds and flash floods have the greatest economic consequences.
Our raw data is taken from National Weather Service Instruction 10-1065. The events in the database start in the year 1950 and end in November 2011. Injuries,Fatalities, Property Damage and Crop Damage(in Dollars)are calculated during that times.
Downloads and reads the dataset into R
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url,"StormData.csv.bz2")
data <- read.csv("C:/Users/Hlangano/Downloads/repdata_data_StormData (4).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 6Subsetting the storm data in memory
Subdata <- c("EVTYPE","FATALITIES","INJURIES","PROPDMG","PROPDMGEXP","CROPDMG","CROPDMGExP")Convert B,M,K,H units to calculate Property Damage
## Convert B,M,K,H units to calculate Crop damage
r Subdata$CropDamage <- 0
## Warning in Subdata$CropDamage <- 0: Coercing LHS to a list
r Subdata[Subdata$CROPDMGEXP == "B"]$CropDamage <- Subdata[Subdata$CROPDMGEXP == "B"]$CROPDMG*10^9 Subdata[Subdata$CROPDMGEXP == "M"]$CropDamage <- Subdata[Subdata$CROPDMGEXP == "M"]$CROPDMG*10^6 Subdata[Subdata$CROPDMGEXP == "K"]$CropDamage <- Subdata[Subdata$CROPDMGEXP == "K"]$CROPDMG*10^3 Subdata[Subdata$CROPDMGEXP == "H"]$CropDamage <- Subdata[Subdata$CROPDMGEXP == "H"]$CROPDMG*10^2 ## What causes most injuries
injuries <- aggregate(data$INJURIES,by = list(EVTYPE = data$EVTYPE),sum)
injuries <- injuries[order(injuries$x,decreasing = TRUE), ]
head(injuries)## EVTYPE x
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100## Plot for most injuries caused
r ggplot(injuries[1:5,], aes(EVTYPE, y= x)) + geom_bar(stat ="identity") + xlab("Events Type")+ ylab("Number of Injuries")+ggtitle("Injuries by Event Type")
## The 5 most fatalities events
fatalities <- aggregate(data$FATALITIES,by = list(EVTYPE = data$EVTYPE),sum)
fatalities <- fatalities[order(fatalities$x,decreasing = TRUE), ]
head(fatalities)## EVTYPE x
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504## Plot for fatalities
ggplot(fatalities[1:5,], aes(EVTYPE, y= x)) + geom_bar(stat ="identity") + xlab("Events Type")+ ylab("Number of Fatalities")+ggtitle("Injuries by Event Type")
## We combined the exponents with the value
Subdata$PROPDMGEXP <- 10^(as.numeric(Subdata$PROPDMGEXP))
Subdata$CROPDMGEXP <- 10^(as.numeric(Subdata$CROPDMGEXP))## The top 5 events which the highest total economic damages
Subdata$CROPEXP[Subdata$CROPDMGEXP ==""] <- 1
# Assigning "0" to invalid exponent data
Subdata$CROPEXP[Subdata$CROPDMGEXP == "?"] <- 0Calculating the property damage value
Subdata$CROPDMGVAL <- Subdata$CROPDMG*Subdata$CROPEXP
# Assigning "0" to invalid exponet data
Subdata$PROPEXP[Subdata$PROPDMGEXP ==""] <- 1
Subdata$PROPEXP[Subdata$PRPODMGEXP =="+"] <- 0
Subdata$PROPEXP[Subdata$PRPODMGEXP =="+"] <- 0
Subdata$PROPEXP[Subdata$PRPODMGEXP =="?"] <- 0
Subdata$PROPEXP[Subdata$PRPODMGEXP =="+"] <- 0
Subdata$PROPEXP[Subdata$PRPODMGEXP =="?"] <- 0Results
Damage to human life
Tornadoes caused most injuries and fatalities
Damage to Property and Crop
Floods are responsible for the most economic damage.