Analysis of the Storm Data

Synopsis

Using the storm data we will answer two questions

Across the United States, which types of events are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?

Data Processing

We will download the raw data and save it in the working directory and then read in file

rawdataURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
rawdatafile <- "stormdata.csv.bz2"

download.file(rawdataURL,rawdatafile)

rawdata <- read.csv(rawdatafile)

We will use the dplyr library to process the data and ggplot2 to make visualisations

library("dplyr")

## 
## Vedhæfter pakke: 'dplyr'

## De følgende objekter er maskerede fra 'package:stats':
## 
##     filter, lag

## De følgende objekter er maskerede fra 'package:base':
## 
##     intersect, setdiff, setequal, union

library("ggplot2")

## Warning: pakke 'ggplot2' blev bygget under R version 4.3.2

To answer the two questions we will we will use fatalities (FATALITIES), injuries(INJURIES), property damage (PROPDMG) and crop damage (CROPDMG). We will create a data frame with the sum of fatalities, injuries, property damage and crop damage for each event type (EVTYPE). Below are the first 10 entries in the data frame, no sorting has been applied.

processeddata <- rawdata %>% group_by(EVTYPE) %>% summarise(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES),CROPDMG = sum(CROPDMG), PROPDMG = sum(PROPDMG))
print(processeddata,n=10)

## # A tibble: 985 × 5
##    EVTYPE                  FATALITIES INJURIES CROPDMG PROPDMG
##    <chr>                        <dbl>    <dbl>   <dbl>   <dbl>
##  1 "   HIGH SURF ADVISORY"          0        0       0     200
##  2 " COASTAL FLOOD"                 0        0       0       0
##  3 " FLASH FLOOD"                   0        0       0      50
##  4 " LIGHTNING"                     0        0       0       0
##  5 " TSTM WIND"                     0        0       0     108
##  6 " TSTM WIND (G45)"               0        0       0       8
##  7 " WATERSPOUT"                    0        0       0       0
##  8 " WIND"                          0        0       0       0
##  9 "?"                              0        0       0       5
## 10 "ABNORMAL WARMTH"                0        0       0       0
## # ℹ 975 more rows

Results

Across the United States, which types of events are most harmful with respect to population health?

To answer this question we will use fatalities and injuries as the indicators of harm caused by the events.

The below table shows the event types with the most fatalities

print(processeddata %>% select(EVTYPE,FATALITIES) %>% arrange(desc(FATALITIES)),n=10)

## # A tibble: 985 × 2
##    EVTYPE         FATALITIES
##    <chr>               <dbl>
##  1 TORNADO              5633
##  2 EXCESSIVE HEAT       1903
##  3 FLASH FLOOD           978
##  4 HEAT                  937
##  5 LIGHTNING             816
##  6 TSTM WIND             504
##  7 FLOOD                 470
##  8 RIP CURRENT           368
##  9 HIGH WIND             248
## 10 AVALANCHE             224
## # ℹ 975 more rows

The below table shows the event types with the most fatalities

print(processeddata %>% select(EVTYPE,INJURIES) %>% arrange(desc(INJURIES)),n=10)

## # A tibble: 985 × 2
##    EVTYPE            INJURIES
##    <chr>                <dbl>
##  1 TORNADO              91346
##  2 TSTM WIND             6957
##  3 FLOOD                 6789
##  4 EXCESSIVE HEAT        6525
##  5 LIGHTNING             5230
##  6 HEAT                  2100
##  7 ICE STORM             1975
##  8 FLASH FLOOD           1777
##  9 THUNDERSTORM WIND     1488
## 10 HAIL                  1361
## # ℹ 975 more rows

The below plot shows the relationship between fatalities and injuries. We know from the above tables that Tornados have the highest number of fatalities and injuries.

#Plot the result of the transactions per day
harmg <- ggplot(processeddata, aes(x=INJURIES, y=FATALITIES))
harmp <- harmg+geom_point()+geom_smooth(method=lm)+labs(title = "Fatalities v Injuries", x = "Inuries", y = "Fatalities")
print(harmp)

## `geom_smooth()` using formula = 'y ~ x'

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

To answer this question we will use property damage and crop damage as the indicators of the economic consequences.

The below table shows the event types with the most property damage.

print(processeddata %>% select(EVTYPE,PROPDMG) %>% arrange(desc(PROPDMG)),n=10)

## # A tibble: 985 × 2
##    EVTYPE              PROPDMG
##    <chr>                 <dbl>
##  1 TORNADO            3212258.
##  2 FLASH FLOOD        1420125.
##  3 TSTM WIND          1335966.
##  4 FLOOD               899938.
##  5 THUNDERSTORM WIND   876844.
##  6 HAIL                688693.
##  7 LIGHTNING           603352.
##  8 THUNDERSTORM WINDS  446293.
##  9 HIGH WIND           324732.
## 10 WINTER STORM        132721.
## # ℹ 975 more rows

The below table shows the event types with the most crop damage

print(processeddata %>% select(EVTYPE,CROPDMG) %>% arrange(desc(CROPDMG)),n=10)

## # A tibble: 985 × 2
##    EVTYPE             CROPDMG
##    <chr>                <dbl>
##  1 HAIL               579596.
##  2 FLASH FLOOD        179200.
##  3 FLOOD              168038.
##  4 TSTM WIND          109203.
##  5 TORNADO            100019.
##  6 THUNDERSTORM WIND   66791.
##  7 DROUGHT             33899.
##  8 THUNDERSTORM WINDS  18685.
##  9 HIGH WIND           17283.
## 10 HEAVY RAIN          11123.
## # ℹ 975 more rows

The below plot shows the relationship between proprty damage and crop damage.

#Plot the result of the transactions per day
econg <- ggplot(processeddata, aes(x=PROPDMG, y=CROPDMG))
econp <- econg+geom_point()+geom_smooth(method=lm)+scale_y_continuous(labels = scales::comma)+scale_x_continuous(labels = scales::comma)+labs(title = "Property Damage v Crop Damage", x = "Property Damage", y = "Crop Damage")
print(econp)