Analysis of Weather data

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.

Data Processing

#set working directory
setwd(".")
#download file
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",destfile = "./stormData.csv.bz2",method = "curl")
#assign that data to stormData
stormData <- read.csv("./stormData.csv.bz2")
#check the dimensions of stormData!
dim(stormData)
## [1] 902297     37

#Math and Data Organization First, load the necessary libraries:

library(ggplot2)
library(plyr)

Then let’s create us some subsets!

Finding the total Harm with sum of FATALITIES and INJURIES by EVTYPE

injuryDataFrame <- ddply(stormData, .(EVTYPE), summarize, TotalHarm = sum(FATALITIES + INJURIES))
injuryDataFrame <- injuryDataFrame[order(injuryDataFrame$TotalHarm, decreasing = T), ]

Top 10 Harm

TopHarm <- injuryDataFrame[1:10, ]

Property Damage: Find the sum of PROPDMG by EVTYPE and PROPDMGEXP.

prop <- ddply(stormData, .(EVTYPE, PROPDMGEXP), summarize, PROPDMG = sum(PROPDMG))

Property Damage: Finding the value of property Damage

prop <- mutate(prop, PropertyDamage = ifelse(toupper(PROPDMGEXP) =='K', PROPDMG*1000, ifelse(toupper(PROPDMGEXP) =='M', PROPDMG*1000000, ifelse(toupper(PROPDMGEXP) == 'B', PROPDMG*1000000000, ifelse(toupper(PROPDMGEXP) == 'H', PROPDMG*100, PROPDMG)))))

Property Damage: Finding the property damage based on event type

prop <- subset(prop, select = c("EVTYPE", "PropertyDamage"))
prop.total <- ddply(prop, .(EVTYPE), summarize, TotalPropDamage = sum(PropertyDamage))

Crop Damage: Sum of the the CROPDMG by EVTYPE and CROPDMGEXP.

crop <- ddply(stormData, .(EVTYPE, CROPDMGEXP), summarize, CROPDMG = sum(CROPDMG))

Crop Damage: Real crop damage based on CROPDMGEXP.

crop <- mutate(crop, CropDamage = ifelse(toupper(CROPDMGEXP) =='K', CROPDMG*1000, ifelse(toupper(CROPDMGEXP) =='M', CROPDMG*1000000, ifelse(toupper(CROPDMGEXP) == 'B', CROPDMG*1000000000, ifelse(toupper(CROPDMGEXP) == 'H', CROPDMG*100, CROPDMG)))))

Crop Damage: Sum of the crop damage by event type

crop <- subset(crop, select = c("EVTYPE", "CropDamage"))
crop.total <- ddply(crop, .(EVTYPE), summarize, TotalCropDamage = sum(CropDamage))

Total Damage : Merging the Property & Crop Damage

damageDataFrame <- merge(prop.total, crop.total, by="EVTYPE")
damageDataFrame <- mutate(damageDataFrame, TotalDamage = TotalPropDamage + TotalCropDamage)
damageDataFrame <- damageDataFrame[order(damageDataFrame$TotalDamage, decreasing = T), ]

Top 10 Damage

TopDamage <- damageDataFrame[1:10, ]

Results 1 . Population Health Casualties This is the result of top 10 harmful types based on the sum of casualties.

TopHarm
##                EVTYPE TotalHarm
## 834           TORNADO     96979
## 130    EXCESSIVE HEAT      8428
## 856         TSTM WIND      7461
## 170             FLOOD      7259
## 464         LIGHTNING      6046
## 275              HEAT      3037
## 153       FLASH FLOOD      2755
## 427         ICE STORM      2064
## 760 THUNDERSTORM WIND      1621
## 972      WINTER STORM      1527
totalHarmPlot <- ggplot(TopHarm, aes( EVTYPE,TotalHarm, fill=EVTYPE)) + geom_bar(stat="identity") + xlab("Top 10 events")+ ylab("Total Harm / Fatalties")+ ggtitle("Fatalities due to severe weather events in the U.S from 1950-2011") + theme(axis.text.x=element_text(angle=45,hjust=1))
totalHarmPlot

Most fatalties are caused by tornadoes.

  1. Economic Casulaties Here are the top 10 sources of damages:
TopDamage
##                EVTYPE TotalPropDamage TotalCropDamage  TotalDamage
## 170             FLOOD    144657709807      5661968450 150319678257
## 411 HURRICANE/TYPHOON     69305840000      2607872800  71913712800
## 834           TORNADO     56937160779       414953270  57352114049
## 670       STORM SURGE     43323536000            5000  43323541000
## 244              HAIL     15732267543      3025954473  18758222016
## 153       FLASH FLOOD     16140812067      1421317100  17562129167
## 95            DROUGHT      1046106000     13972566000  15018672000
## 402         HURRICANE     11868319010      2741910000  14610229010
## 590       RIVER FLOOD      5118945500      5029459000  10148404500
## 427         ICE STORM      3944927860      5022113500   8967041360
totaldamagePlot <- ggplot(TopDamage, aes( EVTYPE,TotalDamage, fill=EVTYPE)) + geom_bar(stat="identity") + xlab("Top 10 events")+ ylab("Total Economic damage")+ ggtitle("Total Economic damage due to severe weather events in the U.S from 1950-2011") + theme(axis.text.x=element_text(angle=45,hjust=1))
totaldamagePlot

Most damages are caused by flooding.

This is the plot based on the TotalCropDamage

totalcropDamagePlot <- ggplot(TopDamage, aes( EVTYPE,TotalCropDamage, fill=EVTYPE)) + geom_bar(stat="identity") + xlab("Top 10 events")+ ylab("Total Crop Economic damage")+ ggtitle("Total Economic Crop damage due to severe weather events in the U.S from 1950-2011") + theme(axis.text.x=element_text(angle=45,hjust=1))
totalcropDamagePlot

Most Crop Damages are caused by drought.