Synopsis

This report analyzes the NOAA storm dataset to determine which types of events are most harmful to population health and which have the greatest economic impact. The analysis shows that tornadoes are the most harmful to population health, while floods and hurricanes cause the greatest economic damage.

Data Processing

The dataset was loaded using read.csv(). Relevant variables such as event type, fatalities, injuries, and damage values were used for the analysis. Property and crop damage values were converted into actual numeric values using multipliers such as K (thousands), M (millions), and B (billions).

data <- read.csv(file.choose(), stringsAsFactors = FALSE)
convert_exp <- function(exp) {
  if (exp == "K") return(1e3)
  if (exp == "M") return(1e6)
  if (exp == "B") return(1e9)
  return(1)
}

data$PROPDMGEXP <- toupper(data$PROPDMGEXP)
data$CROPDMGEXP <- toupper(data$CROPDMGEXP)

data$prop <- data$PROPDMG * sapply(data$PROPDMGEXP, convert_exp)
data$crop <- data$CROPDMG * sapply(data$CROPDMGEXP, convert_exp)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.5.3
## 
## 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 4.5.3
health <- data %>%
  group_by(EVTYPE) %>%
  summarise(total = sum(FATALITIES + INJURIES, na.rm = TRUE)) %>%
  arrange(desc(total)) %>%
  head(10)

health
## # A tibble: 10 × 2
##    EVTYPE            total
##    <chr>             <dbl>
##  1 TORNADO           96979
##  2 EXCESSIVE HEAT     8428
##  3 TSTM WIND          7461
##  4 FLOOD              7259
##  5 LIGHTNING          6046
##  6 HEAT               3037
##  7 FLASH FLOOD        2755
##  8 ICE STORM          2064
##  9 THUNDERSTORM WIND  1621
## 10 WINTER STORM       1527
economic <- data %>%
  group_by(EVTYPE) %>%
  summarise(total = sum(prop + crop, na.rm = TRUE)) %>%
  arrange(desc(total)) %>%
  head(10)

economic
## # A tibble: 10 × 2
##    EVTYPE                    total
##    <chr>                     <dbl>
##  1 FLOOD             150319678257 
##  2 HURRICANE/TYPHOON  71913712800 
##  3 TORNADO            57352114049.
##  4 STORM SURGE        43323541000 
##  5 HAIL               18758221521.
##  6 FLASH FLOOD        17562129167.
##  7 DROUGHT            15018672000 
##  8 HURRICANE          14610229010 
##  9 RIVER FLOOD        10148404500 
## 10 ICE STORM           8967041360

Results

Tornadoes are the most harmful to population health due to the highest number of fatalities and injuries. Floods and hurricanes contribute the most to economic damage because of large-scale property and crop losses.

The figure below shows the top 10 weather event types that are most harmful to population health, based on total fatalities and injuries.

ggplot(health, aes(x = reorder(EVTYPE, total), y = total)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  ggtitle("Top Events Harmful to Population Health")

The figure below shows the top 10 weather event types that cause the greatest economic damage, based on total property and crop losses.

ggplot(economic, aes(x = reorder(EVTYPE, total), y = total)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  ggtitle("Top Events Economic Damage")