Storm Data Analysis(RR Assignment week4)

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.

In this report,effect of weather events on personal as well as property damages was studied. Barplots were plotted seperately for the top 8 weather events that causes highest fatalities and highest injuries. Results indicate that most Fatalities and injuries were caused by Tornados.Also, barplots were plotted for the top 8 weather events that causes the highest property damage and crop damage.

Data processing

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 [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

National Climatic Data Center Storm Events FAQ

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. ####Assignment The basic goal of this assignment is to explore the NOAA Storm Database and answer the following 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? ####Loading the Data

repdata_data_StormData.csv <-read.csv("C:/Users/admin/Downloads/repdata_data_StormData.csv.bz2")
storm <-repdata_data_StormData.csv

Extracting the required data

This dataset consists of lot of information most of which is not required for our present study. So, here is the code to extract the required data for health and economic impact analysis against weather.

event <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", 
           "CROPDMGEXP")
data <- storm[event]

Finding property damage

Property damage exponents for each level was listed out and assigned those values for the property exponent data. Invalid data was excluded by assigning the value as ‘0’. Then property damage value was calculated by multiplying the property damage and property exponent value.The code for this process was listed below

unique(data$PROPDMGEXP)

##  [1] "K" "M" ""  "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"

Assigning values for the property exponent data

data$PROPEXP[data$PROPDMGEXP == "K"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "M"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == ""] <- 1
data$PROPEXP[data$PROPDMGEXP == "B"] <- 1e+09
data$PROPEXP[data$PROPDMGEXP == "m"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "0"] <- 1
data$PROPEXP[data$PROPDMGEXP == "5"] <- 1e+05
data$PROPEXP[data$PROPDMGEXP == "6"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "4"] <- 10000
data$PROPEXP[data$PROPDMGEXP == "2"] <- 100
data$PROPEXP[data$PROPDMGEXP == "3"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "h"] <- 100
data$PROPEXP[data$PROPDMGEXP == "7"] <- 1e+07
data$PROPEXP[data$PROPDMGEXP == "H"] <- 100
data$PROPEXP[data$PROPDMGEXP == "1"] <- 10
data$PROPEXP[data$PROPDMGEXP == "8"] <- 1e+08

data$PROPEXP[data$PROPDMGEXP == "+"] <- 0
data$PROPEXP[data$PROPDMGEXP == "-"] <- 0
data$PROPEXP[data$PROPDMGEXP == "?"] <- 0

data$PROPDMGVAL <- data$PROPDMG * data$PROPEXP

Finding crop damage

Finding crop damage Crop damage exponents for each level was listed out and assigned those values for the crop exponent data. Invalid data was excluded by assigning the value as ‘0’. Then crop damage value was calculated by multiplying the crop damage and crop exponent value.The code for this process was listed below

unique(data$CROPDMGEXP)

## [1] ""  "M" "K" "m" "B" "?" "0" "k" "2"

data$CROPEXP[data$CROPDMGEXP == "M"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "K"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "m"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "B"] <- 1e+09
data$CROPEXP[data$CROPDMGEXP == "0"] <- 1
data$CROPEXP[data$CROPDMGEXP == "k"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "2"] <- 100
data$CROPEXP[data$CROPDMGEXP == ""] <- 1
data$CROPEXP[data$CROPDMGEXP == "?"] <- 0
data$CROPDMGVAL <- data$CROPDMG * data$CROPEXP

Finding totals of each incident by event type

It was observed that " most harmful to population health" events are fatalities and injuries.So,only those events with fatalities and injuries were selecetd.

It was observed that " most harmful to econamic problem“” events are Property and crop damages.So,only those events with property and crop damage were selecetd.

Then for each incident (Fatalities,Injuries, Property damage and Crop damage), the total values were estimated. Code for which is as follows.

Totalling the data by event

fatal <- aggregate(FATALITIES ~ EVTYPE, data, FUN = sum)
injury <- aggregate(INJURIES ~ EVTYPE, data, FUN = sum)
propdmg <- aggregate(PROPDMGVAL ~ EVTYPE, data, FUN = sum)
cropdmg <- aggregate(CROPDMGVAL ~ EVTYPE, data, FUN = sum)

Plotting events with highest fatalities and highest injuries

Highest fatalities and highest injuries for Top 8 events were calculated. For better understanding and comparision these values were plotted as follows:

fatal8 <- fatal[order(-fatal$FATALITIES), ][1:8, ]

injury8 <- injury[order(-injury$INJURIES), ][1:8, ]
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(fatal8$FATALITIES, las = 3, names.arg = fatal8$EVTYPE, main = "Events with Highest Fatalities", 
        ylab = "Number of fatalities", col = "yellow")
barplot(injury8$INJURIES, las = 3, names.arg = injury8$EVTYPE, main = "Events with Highest Injuries", 
        ylab = "Number of injuries", col = "yellow")

Plotting events with highest Property damage and highest crop damage

Highest Property damage and highest crop damage for Top 8 events were calculated. For better understanding and comparision these values were plotted as follows:

propdmg8 <- propdmg[order(-propdmg$PROPDMGVAL), ][1:8, ]
cropdmg8 <- cropdmg[order(-cropdmg$CROPDMGVAL), ][1:8, ]
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg8$PROPDMGVAL/(10^9), las = 3, names.arg = propdmg8$EVTYPE, 
        main = "Events with Highest Property Damages", ylab = "Damage Cost ($ billions)", 
        col = "yellow")
barplot(cropdmg8$CROPDMGVAL/(10^9), las = 3, names.arg = cropdmg8$EVTYPE, 
        main = "Events With Highest Crop Damages", ylab = "Damage Cost ($ billions)", 
        col = "yellow")

Results and Conclusions

Tornados caused the maximum number of fatalities and injuries. It was followed by Excessive Heat for fatalities and Thunderstorm wind for injuries.

Floods caused the maximum property damage where as Drought caused the maximum crop damage. Second major events that caused the maximum damage was Hurricanes/Typhoos for property damage and Floods for crop damage.