Economic and Health Effects of Severe Weather
Synopsis
Loading and Processing Raw Data
Loading
Data were downloaded from https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2. The following links provide the documentation (https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf) and FAQ (https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf) for the data.
if (!file.exists("./storm.csv.bz2")) {
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
"./storm.csv.bz2")
}
# unzip file
if (!file.exists("./storm.csv")) {
library(R.utils)
bunzip2("./storm.csv.bz2", "./storm.csv", remove = FALSE)
}
storm.data <- read.csv("./storm.csv")There are 902297 observations and 37 variables. There are 985 different severe weather classifications.
dim(storm.data)## [1] 902297 37length(unique(storm.data$EVTYPE))## [1] 985head(storm.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 6Processing
For the purpose of this analysis, we are only interested with the type of severe weather event, fatalities, injuries, and damagaes to property and crops. ‘newData’ will be the data frame used for the rest of this analysis and will only include the variables measuring the forementioned statistics.
newData <- storm.data[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP","CROPDMG", "CROPDMGEXP")]
dim(newData)## [1] 902297 7There are 902297 observations and 7 variables in ‘newData’. There are no missing values in the data frame.
sum(is.na(storm.data))## [1] 1745947Quick glance of the data:
summary(newData)## EVTYPE FATALITIES INJURIES
## HAIL :288661 Min. : 0.0000 Min. : 0.0000
## TSTM WIND :219940 1st Qu.: 0.0000 1st Qu.: 0.0000
## THUNDERSTORM WIND: 82563 Median : 0.0000 Median : 0.0000
## TORNADO : 60652 Mean : 0.0168 Mean : 0.1557
## FLASH FLOOD : 54277 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## FLOOD : 25326 Max. :583.0000 Max. :1700.0000
## (Other) :170878
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9head(newData)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0Results
Fatalities
The events that have caused the most fatalities from 1950-2011 are examined in this section.
deaths <- aggregate(FATALITIES~EVTYPE, newData, sum)
deaths <- deaths[order(deaths$FATALITIES, decreasing = TRUE),]
barplot (height = deaths$FATALITIES[1:30], names.arg = deaths$EVTYPE[1:30], las = 2, cex.names= 0.5, col = rainbow (30, start=0, end=1))
title(main = "Top 30 Events With Most Fatalities")
title(ylab = "Total Number of Fatalities")
This bar graph shows the 30 events that caused the most fatalities, in descending order.
Injuries
The events that have caused the most fatalities from 1950-2011 are examined in this section.
injuries <- aggregate(INJURIES~EVTYPE, newData, sum)
injuries <- injuries[order(injuries$INJURIES, decreasing = TRUE), ]
par(mar=c(12, 6, 1, 1))
barplot(height = injuries$INJURIES[1:30], names.arg = injuries$EVTYPE[1:30], las = 2, cex.names = 0.5, col = rainbow(30, start = 0, end = 1))
title(main = "Top 30 Events With Most Injuries", line = 0)
title(ylab = "Total Number of Injuries", line = 4)
This bar graph shows the 30 events that caused the most injuries, in descending order.
Economic Damage
This section examines the events that have caused the most economic damage. Economic damage is represented by the total dollar amount of damage done to property and crops.
symbol <- c("", "+", "-", "?", 0:9, "h", "H", "k", "K", "m", "M", "b", "B");
factor <- c(rep(0,4), 0:9, 2, 2, 3, 3, 6, 6, 9, 9)
multiplier <- data.frame (symbol, factor)
newData$damage.prop <- newData$PROPDMG*10^multiplier[match(newData$PROPDMGEXP,multiplier$symbol),2]
newData$damage.crop <- newData$CROPDMG*10^multiplier[match(newData$CROPDMGEXP,multiplier$symbol),2]
newData$damage <- newData$damage.prop + newData$damage.crop
total.damage <- aggregate (damage~EVTYPE, newData, sum);
total.damage$billion <- total.damage$damage / 1e9;
total.damage <- total.damage [order(total.damage$billion, decreasing=TRUE),]
barplot (height = total.damage$billion[1:30], names.arg = total.damage$EVTYPE[1:30], las = 2, cex.names = 0.5,
col = rainbow (30, start=0, end=1))
title ("Top 30 Events with Most Economic Damage")
title (ylab = "Total damage (billion USD)")
This bar graph shows the 30 events that caused the most economic damage, in descending order.
Results
From this analysis we see that ‘Tornado’ is the event that causes the most fatalities and injuries. However, economically a ‘Flood’ causes the most damage.