Synapsis

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:

1. Across the United States, which types of events are most harmful with respect to population health?
2. 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

Data Processing

Data loading

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"))

Injuries and fatalities

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

Crop and property damage in dollars

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),]

Results

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")