NOAA Storm Analysis

Synopsis

This analysis explores severe weather events and identifies which are most harmful to population health and economy.

Data Processing

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

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

data$FATALITIES <- as.numeric(data$FATALITIES)
data$INJURIES <- as.numeric(data$INJURIES)
health <- data %>%
  group_by(EVTYPE) %>%
  summarise(total = sum(FATALITIES + INJURIES)) %>%
  arrange(desc(total))
data$PROPDMGEXP[data$PROPDMGEXP == "K"] <- 1e3
data$PROPDMGEXP[data$PROPDMGEXP == "M"] <- 1e6
data$PROPDMGEXP[data$PROPDMGEXP == "B"] <- 1e9

data$PROPDMGEXP <- as.numeric(data$PROPDMGEXP)

## Warning: NAs introduced by coercion

data$economic <- data$PROPDMG * data$PROPDMGEXP

economic <- data %>%
  group_by(EVTYPE) %>%
  summarise(total = sum(economic, na.rm = TRUE)) %>%
  arrange(desc(total))
top_health <- head(health, 10)

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

top_economic <- head(economic, 10)

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