Synopsis

The goal of the assignment is to explore the NOAA Storm Database and investigate which types of severe weather events are most harmful on:

  1. Population health (injuries and fatalities)
  2. Economy (property and crop damages)

The events in the database start in the year 1950 and end in November 2011.

Data Processing

Load libraries.

library(dplyr)
## Warning: package 'dplyr' was built under R version 3.2.5
## 
## 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)
## Warning: package 'ggplot2' was built under R version 3.2.5

Download and unzip the storm data file.

dataFile = "repdata%2Fdata%2FStormData.csv.bz2"
if (!file.exists(dataFile)){
        fileURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
        download.file(fileURL, zipFile, method="curl")
}

Load storm data (only relevant columns for the analysis).

storm <- read.csv(dataFile)[,c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
head(storm, 10)
##     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       0           
## 7  TORNADO          0        1     2.5          K       0           
## 8  TORNADO          0        0     2.5          K       0           
## 9  TORNADO          1       14    25.0          K       0           
## 10 TORNADO          0        0    25.0          K       0

Discard events with non casualties or property / cost damages:

storm <- storm %>% filter(INJURIES > 0 | FATALITIES > 0 | PROPDMG > 0 | CROPDMG > 0)

Add casualties column (sum of fatalities and injuries).

storm$casualties <- storm$FATALITIES + storm$INJURIES

Define function to convert property / crop damage exponents to numeric values for cost calculations.

exponentMap <- function(exponent) {
        if (exponent == "")
                return (10^0)
        map <- c("0" = 10^0, "1" = 10^1, "2" = 10^2, "3" = 10^3,
          "4" = 10^4, "5" = 10^5, "6" = 10^6, "7" = 10^7,
          "8" = 10^8, "9" = 10^9, "h" = 10^2, "k" = 10^3,
          "m" = 10^6, "b" = 10^9, "-" = 10^0, "?" = 10^0,
          "+" = 10^0)
        return (map[tolower(exponent)])
}

Add economic cost column (sum of property and crop costs).

storm$economicCost <- storm$PROPDMG * unlist(lapply(storm$PROPDMGEXP, exponentMap)) + storm$CROPDMG * unlist(lapply(storm$CROPDMGEXP, exponentMap))

Group casualties by event type.

casualtiesByEvtype <- storm %>%
        group_by(EVTYPE) %>%
        summarise(casualties = sum(casualties))
head(casualtiesByEvtype, 10)
## # A tibble: 10 × 2
##                    EVTYPE casualties
##                    <fctr>      <dbl>
## 1      HIGH SURF ADVISORY          0
## 2             FLASH FLOOD          0
## 3               TSTM WIND          0
## 4         TSTM WIND (G45)          0
## 5                       ?          0
## 6     AGRICULTURAL FREEZE          0
## 7           APACHE COUNTY          0
## 8  ASTRONOMICAL HIGH TIDE          0
## 9   ASTRONOMICAL LOW TIDE          0
## 10               AVALANCE          1

Group economic cost by event type.

economicCostByEvtype <- storm %>%
        group_by(EVTYPE) %>%
        summarise(economicCost = sum(economicCost))
head(economicCostByEvtype, 10)
## # A tibble: 10 × 2
##                    EVTYPE economicCost
##                    <fctr>        <dbl>
## 1      HIGH SURF ADVISORY       200000
## 2             FLASH FLOOD        50000
## 3               TSTM WIND      8100000
## 4         TSTM WIND (G45)         8000
## 5                       ?         5000
## 6     AGRICULTURAL FREEZE     28820000
## 7           APACHE COUNTY         5000
## 8  ASTRONOMICAL HIGH TIDE      9425000
## 9   ASTRONOMICAL LOW TIDE       320000
## 10               AVALANCE            0

Results

Top 20 types of storm events which are most harmful to population health.

n <- 20
topCasualtiesByEvtype <- slice(casualtiesByEvtype, order(-casualtiesByEvtype$casualties)[1:n])

ggplot(topCasualtiesByEvtype, aes(x=reorder(EVTYPE, -casualties), y = casualties)) +
        geom_bar(stat="identity", fill = "indianred") +
        geom_text(aes(label=paste(sprintf("%d", casualties))), position=position_dodge(width=0.9), vjust=-0.25, size=3) +
        labs(title = "Top 20 most harmful storm event types to population health", x = "Event Type", y = "Casualties") +
        theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
        theme(plot.title = element_text(hjust = 0.5))

Top 20 types of storm events with greatest economic impact.

n <- 20
topEconomicCostByEvtype <- slice(economicCostByEvtype, order(-economicCostByEvtype$economicCost)[1:n])

ggplot(topEconomicCostByEvtype, aes(x=reorder(EVTYPE, -economicCost), y = economicCost/10^9)) +
        geom_bar(stat="identity", fill = "indianred") +
        geom_text(aes(label=paste(sprintf("%.02f", economicCost/10^9))), position=position_dodge(width=0.9), vjust=-0.25, size=3) +
        labs(title = "Top 20 most harmful storm event types to economy", x = "Event Type", y = "Cost in US$ billions") +
        theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
        theme(plot.title = element_text(hjust = 0.5))