Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. The basic goal of this project is to explore the NOAA Storm Database in order to answer the following questions:
- Across the United States, which types of events are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
The database for the following analysis was obtained from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database from https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2.. 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.
Documentation available in https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf
The data is loaded and stored in the dataframe “db”
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, destfile = 'repdata%2Fdata%2FStormData.csv.bz2')
db <- tbl_df(read.csv("repdata%2Fdata%2FStormData.csv.bz2"))
The total injuries by each event type are calculated and stored in “dfInj”. Also the data is ordered from highest to lowest injueries caused.
dfInj<-summarise(group_by(db, EVTYPE), totalInj = sum(INJURIES))
dfInj<-dfInj[order(dfInj$totalInj, decreasing = T),]
head(dfInj)
## # A tibble: 6 x 2
## EVTYPE totalInj
## <fct> <dbl>
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
The same process is repeated with the fatalities caused by event and the results are stored in “dfFat”.
dfFat<-summarise(group_by(db, EVTYPE), totalFat = sum(FATALITIES))
dfFat<-dfFat[order(dfFat$totalFat, decreasing = T),]
head(dfFat)
## # A tibble: 6 x 2
## EVTYPE totalFat
## <fct> <dbl>
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
Now exploring the damage multipliers for both, property and crops, we realize that there are not representing values (-, ?, +) and there are some upper and lower case values.
For property damage:
table(db$PROPDMGEXP)
##
## - ? + 0 1 2 3 4 5 6
## 465934 1 8 5 216 25 13 4 4 28 4
## 7 8 B h H K m M
## 5 1 40 1 6 424665 7 11330
For crop damage:
table(db$CROPDMGEXP)
##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994
In order to avoid confusion, all these values are converted to lower case.
db$PROPDMGEXP <- tolower(db$PROPDMGEXP)
db$CROPDMGEXP <- tolower(db$CROPDMGEXP)
A function is created, it receives a damage exponent and returns the equivalent numeric values for it.
multiplier <- function(x) {
if (x == "h") x<-100
if (x == "k") x<-1000
if (x == "m") x<-1000000
if (x == "b") x<-1000000000
x
}
A new dataframe that contains the significant values of damage exponent (h, k, m, b) is created.
damage<- filter(db, (PROPDMGEXP %in% c("h", "k", "m", "b") & (CROPDMGEXP %in% c("h", "k", "m", "b"))))
damage<-select(damage, c(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))
Then, the function “multiplier” is applied for converting all the damage exponents into numbers. And these numbers are multiplied to get the actual damage for each row, in Total Property Damage (tdp) and Total Crop Damage
damage$PROPDMGEXP <- sapply(damage$PROPDMGEXP, multiplier)
damage$CROPDMGEXP <- sapply(damage$CROPDMGEXP, multiplier)
damage <- mutate(damage, tpd=PROPDMG*PROPDMGEXP)
damage <- mutate(damage, tcd=CROPDMG*CROPDMGEXP)
As we did before, all the data is grouped by event type and the total sum of damage is calculated, for each crop and property damage. After that, Crop and Property damage are merged in one dataframe. The crop damages are added to the property damages in order to get the total damage. Finally, the data is ordered by the amount of total damage.
dfCrop<-summarise(group_by(damage, EVTYPE), totalDmg = sum(tcd))
dfProp<-summarise(group_by(damage, EVTYPE), totalDmg = sum(tpd))
dfDamage <- merge(dfCrop,dfProp, by="EVTYPE", suffixes = c("Crop", "Property"))
dfDamage <- mutate(dfDamage, totalDmg=totalDmgCrop+totalDmgProperty)
dfDamage<-dfDamage[order(dfDamage$totalDmg, decreasing = T),]
The event types with the highest number of injuries are:
ggplot(head(dfInj,10), aes(x=EVTYPE, y=totalInj))+geom_bar(stat="identity",
fill="steelblue")+coord_flip()+ylab("Total number of injuries")+
xlab("Event type")+ labs(title="Events with the highest injueries caused")
The event types with the highest number of fatalities are:
ggplot(head(dfFat,10), aes(x=EVTYPE, y=totalFat))+geom_bar(stat="identity",
fill="steelblue")+coord_flip()+ylab("Total number of fatalities")+
xlab("Event type")+ labs(title="Events with the highest fatalities caused")
The event types with the highest damages are:
ggplot(head(dfDamage,10), aes(x=EVTYPE, y=totalDmg))+geom_bar(stat="identity",
fill="steelblue")+coord_flip()+ylab("Damage caused in dollars")+
xlab("Event type")+ labs(title="Events with the greatest economic consecuences")
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