Severe weather effects in the U.S.

Synopsis

Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

This project will explore the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database to answer two basic questions about sever weather events:

Which types of severe weather events are most harmful with respect to population health in the U.S.?
Which types of severe weather events have the greatest economic impact?

Data Processing

Packages

library(tidyverse)
library(tidydr)
library(knitr)
library(gt)

Read data

Since this happens to be a .csv.bz2 folder we can read it directly instead of unzipping it first.

storm_data <- read.csv("D:/Education/R/Data/JH_C5_week2/repdata_data_StormData.csv.bz2", header = TRUE)

Data types

Let’s make sure our data is of a type we can calculate

str(storm_data)

Impact on health

The first goal of our study is to discover the storm events that are most harmful to population health, so we’ll need to

Group the data by storm events and sum the effects caused to the population in fatalities and injuries due to each one
Rank the events from most impact-full to least in order to answer our question

Aggregate

Aggregate() will allow us to group by EVTYP and sum
Separate fatalities and injuries into separate dataframes
Order the totals in descending and choose the most costly 10 events to show in the results section

impact_fatal <- storm_data |>
        aggregate(FATALITIES~EVTYPE, sum) 
impact_fatal <- impact_fatal[order(impact_fatal$FATALITIES,decreasing = TRUE),]

impact_injured <- storm_data |>
        aggregate(INJURIES~EVTYPE,sum)
impact_injured <- impact_injured[order(impact_injured$INJURIES,decreasing = TRUE),]

Tables

In this section we’ll present the data in table format showing only the 10 most impact-full events, the events that cost the most impact to human health.

Fatalities

table_fatal <- impact_fatal[1:10,] |>
        gt() |> 
        tab_header( title = md("**Number of Fatalities per Event**"),
                    subtitle = "10 most impactful events") |> 
        tab_options(table.align = "left", table.width = pct(50))

Injuries

table_injured <- impact_injured[1:10,] |>
        gt() |> 
        tab_header( title = md("**Number of Injuries per Event**"),
                    subtitle = "10 most impactful events") |> 
        tab_options(table.align = "left", table.width = pct(50))

Combine the two tables

tables <- data.frame(fatal=table_fatal, injured=table_injured)
tables |>
        gt() |>
        cols_label(
                fatal.EVTYPE = md("**Event**"),
                fatal.FATALITIES = md("**Fatalities**"),
                injured.EVTYPE = md("**Event**"),
                injured.INJURIES = md("**Injuries**")
                 ) |> 
        tab_header(title= md("**Event Type and Effect on Population of the U.S.**"),
                   subtitle = "10 most impactful events")

Economic Consequences

Similar to storm effects on human fatality and health, let’s calculate storm damages caused on property and crops. But before we proceed with the aggregation it appears that the data is saved in 5 columns:

colnames(storm_data[c(8,25:28)])

## [1] "EVTYPE"     "PROPDMG"    "PROPDMGEXP" "CROPDMG"    "CROPDMGEXP"

Multipliers

The documentation implied that the cost columns are abbreviations that contained the following: “K”, “M”, “B”
Verify that with

unique(storm_data$PROPDMGEXP)

##  [1] "K" "M" ""  "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"

“..EXP” columns are multipliers for values in the “..DMG” columns
K for thousands, M for millions, B for billions and we’ll ignore the rest
We need to convert the “..DMG” columns to real figures before we aggregate over the values

Convert damage costs columns

We’ll just multiply the representation of columns “…EXP” with the “…DMG” columns to give us whole numbers we can perform addition on.

storm_data <- storm_data |> 
        mutate(PROPDMG_COST= as.numeric(case_when(
                                PROPDMGEXP == "K" ~ as.character(PROPDMG*1000),
                                PROPDMGEXP == "M" ~ as.character(PROPDMG*1000000),
                                PROPDMGEXP == "B" ~ as.character(PROPDMG*1000000000),
                                TRUE ~ PROPDMGEXP))
        ) |> 
        mutate(CROPDMG_COST= as.numeric(case_when(
                        CROPDMGEXP == "K" ~ as.character(CROPDMG*1000),
                        CROPDMGEXP == "M" ~ as.character(CROPDMG*1000000),
                        CROPDMGEXP == "B" ~ as.character(CROPDMG*1000000000),
                        TRUE ~ PROPDMGEXP))
        )

Aggregate

So let’s group and sum the costs to property and crops for each event:

Aggregate() will group by EVTYPE and sum
Order the totals in descending and choose the most costly 10 events to show in the results section

impact_prop <- storm_data |>
        aggregate(PROPDMG_COST~EVTYPE, sum)
impact_prop <- impact_prop[order(impact_prop$PROPDMG_COST,decreasing = TRUE),]


impact_crop <- storm_data |>
        aggregate(CROPDMG_COST~EVTYPE,sum)
impact_crop <- impact_crop[order(impact_crop$CROPDMG_COST,decreasing = TRUE),]

Results

Question 1:

Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

Answer

Tornadoes appear to be the most harmful with respect to population health with 5633 fatalities and 91346 injuries

figure1 <- ggplot(impact_fatal[1:5, ],
                 aes(x= reorder(EVTYPE, - FATALITIES),
                     y= FATALITIES,
                     fill = EVTYPE))
figure1 + geom_col(show.legend = FALSE, width = 0.8, color="black") +
        coord_flip() +
        labs(x= "Weather Event Type",
             y= "# of People Effected") +
        theme_bw()

Event	Fatalities	Event	Injuries
Event Type and Effect on Population of the U.S.
10 most impactful events
TORNADO	5633	TORNADO	91346
EXCESSIVE HEAT	1903	TSTM WIND	6957
FLASH FLOOD	978	FLOOD	6789
HEAT	937	EXCESSIVE HEAT	6525
LIGHTNING	816	LIGHTNING	5230
TSTM WIND	504	HEAT	2100
FLOOD	470	ICE STORM	1975
RIP CURRENT	368	FLASH FLOOD	1777
HIGH WIND	248	THUNDERSTORM WIND	1488
AVALANCHE	224	HAIL	1361

Question 2:

Across the United States, which types of events have the greatest economic consequences?

Answer

Flood appears to be the most costly with $144.67 B

figure2 <- ggplot(impact_prop[1:5, ],
                 aes(x= reorder(EVTYPE, - PROPDMG_COST),
                     y= PROPDMG_COST,
                     fill = EVTYPE))
figure2 + geom_col(show.legend = FALSE, width = 0.8, color="black") +
        coord_flip() +
        labs(x= "Weather Event Type",
             y= "Economic Loss in (USD)") +
        theme_bw()