Impact of severe weather events in US

Synopsis

This analysis explores the NOAA Storm Database to identify which types of weather events are most harmful to population health and which have the greatest economic consequences. The dataset includes information on fatalities, injuries, and property and crop damage. Data processing involved selecting relevant variables and converting damage values into numeric form. The results show that tornadoes are the most harmful to population health, while floods and hurricanes cause the greatest economic damage.

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

Data Processing

data <- read.csv("repdata_data_StormData.csv.bz2")

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

convert_exp <- function(exp) {
  ifelse(exp == "K", 1e3,
  ifelse(exp == "M", 1e6,
  ifelse(exp == "B", 1e9, 1)))
}

data$PROPDMGVAL <- data$PROPDMG * convert_exp(data$PROPDMGEXP)
data$CROPDMGVAL <- data$CROPDMG * convert_exp(data$CROPDMGEXP)

Results

Population Health Impact

health <- data %>%
  group_by(EVTYPE) %>%
  summarise(FATALITIES = sum(FATALITIES),
            INJURIES = sum(INJURIES))

health <- mutate(health, TOTAL = FATALITIES + INJURIES)
health <- arrange(health, desc(TOTAL))
top_health <- head(health, 10)
top_health

## # A tibble: 10 × 4
##    EVTYPE            FATALITIES INJURIES TOTAL
##    <chr>                  <dbl>    <dbl> <dbl>
##  1 TORNADO                 5633    91346 96979
##  2 EXCESSIVE HEAT          1903     6525  8428
##  3 TSTM WIND                504     6957  7461
##  4 FLOOD                    470     6789  7259
##  5 LIGHTNING                816     5230  6046
##  6 HEAT                     937     2100  3037
##  7 FLASH FLOOD              978     1777  2755
##  8 ICE STORM                 89     1975  2064
##  9 THUNDERSTORM WIND        133     1488  1621
## 10 WINTER STORM             206     1321  1527

par(mar = c(10, 7, 4, 2))  # increase LEFT margin (7)

barplot(top_health$TOTAL,
        names.arg = top_health$EVTYPE,
        las = 2,
        col = "red",
        main = "Top Events Harmful to Population Health",
        ylab = "Fatalities + Injuries",
        cex.names = 0.7,
        cex.lab = 1.2)   # makes y-label clearer

Figure 1: This plot shows the top 10 weather events that are most harmful to population health, measured by total fatalities and injuries. Tornadoes clearly have the highest impact compared to other event types.

Economic Impact

econ <- data %>%
  group_by(EVTYPE) %>%
  summarise(PROP = sum(PROPDMGVAL),
            CROP = sum(CROPDMGVAL))

econ <- mutate(econ, TOTAL = PROP + CROP)
econ <- arrange(econ, desc(TOTAL))
top_econ <- head(econ, 10)
top_econ

## # A tibble: 10 × 4
##    EVTYPE                     PROP        CROP         TOTAL
##    <chr>                     <dbl>       <dbl>         <dbl>
##  1 FLOOD             144657709807   5661968450 150319678257 
##  2 HURRICANE/TYPHOON  69305840000   2607872800  71913712800 
##  3 TORNADO            56925660790.   414953270  57340614060.
##  4 STORM SURGE        43323536000         5000  43323541000 
##  5 HAIL               15727367053.  3025537890  18752904943.
##  6 FLASH FLOOD        16140812067.  1421317100  17562129167.
##  7 DROUGHT             1046106000  13972566000  15018672000 
##  8 HURRICANE          11868319010   2741910000  14610229010 
##  9 RIVER FLOOD         5118945500   5029459000  10148404500 
## 10 ICE STORM           3944927860   5022113500   8967041360

par(mar = c(10, 7, 4, 2))

barplot(top_econ$TOTAL,
        names.arg = top_econ$EVTYPE,
        las = 2,
        col = "blue",
        main = "Top Events with Economic Damage",
        ylab = "Damage (USD)",
        cex.names = 0.7,
        cex.lab = 1.2)