NOAA Storm Database Impacts - RepData

Since 1950 storm weather has contributed to 15,145 fatalities, 140,528 injuries and $10,884,500 in property damages in the US.

This publication investigates the top 15 contributors.

Background

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.

We’re going to explore the NOAA Storm Database and answer some basic questions about severe weather events:

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

The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. It can be downloaded from the following web site:

Storm Data [47Mb]

Documentation of the database if available by the links below. The links contain information about how some of the variables are constructed/defined.

National Weather Service Storm Data Documentation
National Climatic Data Center Storm Events FAQ

The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.

Download

The following code will download, decompress the file and read the data only if it does not exist.

# If the file does not exist, download it from URL

if (!file.exists("./data/NOAA_storm_data.csv")) {
    download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", 
                  "./data/NOAA_storm_data.csv.bz2")
}

# Decompress the file using bunzip2


if(!file.exists("./data/NOAA_storm_data.csv")) {
    library(R.utils)
    bunzip2("./data/NOAA_storm_data.csv.bz2", 
            "./data/NOAA_storm_data.csv",
            remove = FALSE,overwrite=TRUE)
}

Read

StormData <- read.csv("./data/NOAA_storm_data.csv")
head(StormData)

##   STATE__           BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1       1  4/18/1950 0:00:00     0130       CST     97     MOBILE    AL
## 2       1  4/18/1950 0:00:00     0145       CST      3    BALDWIN    AL
## 3       1  2/20/1951 0:00:00     1600       CST     57    FAYETTE    AL
## 4       1   6/8/1951 0:00:00     0900       CST     89    MADISON    AL
## 5       1 11/15/1951 0:00:00     1500       CST     43    CULLMAN    AL
## 6       1 11/15/1951 0:00:00     2000       CST     77 LAUDERDALE    AL
##    EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO         0                                               0
## 2 TORNADO         0                                               0
## 3 TORNADO         0                                               0
## 4 TORNADO         0                                               0
## 5 TORNADO         0                                               0
## 6 TORNADO         0                                               0
##   COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1         NA         0                      14.0   100 3   0          0
## 2         NA         0                       2.0   150 2   0          0
## 3         NA         0                       0.1   123 2   0          0
## 4         NA         0                       0.0   100 2   0          0
## 5         NA         0                       0.0   150 2   0          0
## 6         NA         0                       1.5   177 2   0          0
##   INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1       15    25.0          K       0                                    
## 2        0     2.5          K       0                                    
## 3        2    25.0          K       0                                    
## 4        2     2.5          K       0                                    
## 5        2     2.5          K       0                                    
## 6        6     2.5          K       0                                    
##   LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1     3040      8812       3051       8806              1
## 2     3042      8755          0          0              2
## 3     3340      8742          0          0              3
## 4     3458      8626          0          0              4
## 5     3412      8642          0          0              5
## 6     3450      8748          0          0              6

Libraries

Load the tidyverse and ggplot2 libraries to replicate the data analysis and visualization.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 2.2.1     ✔ purrr   0.2.4
## ✔ tibble  1.3.4     ✔ dplyr   0.7.4
## ✔ tidyr   0.7.2     ✔ stringr 1.2.0
## ✔ readr   1.1.1     ✔ forcats 0.2.0

## ── Conflicts ────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(ggplot2)

Data Processing

Data related to public health and economics, requires looking at propery damage, fatalities and injuries in aggregate. This answers the question: ‘what events caused the most health or economic impacts’. For brevity we will look at the top 15 events for each impacted category. The data tends to be steep, supporting 15 as representative for the entire set.

Results

Storm weather has contributed to 15,145 fatalities, 140,528 injuries and $10,884,500 in property damages in the US.

# Subset
storm_data <- select(StormData, EVTYPE, FATALITIES, INJURIES, PROPDMG)

# Rename cols
names(storm_data) <- c("event","fatalities","injuries","property_damage")

sum(storm_data$fatalities)

## [1] 15145

sum(storm_data$injuries)

## [1] 140528

sum(storm_data$property_damage)

## [1] 10884500

Aggregate fatality, injury and property damage data group the data by event type. The results are calculated by the sum of the events and only the top 15 are included in the analysis.

af <- aggregate(fatalities ~ event, data = storm_data, FUN = sum)
ai <- aggregate(injuries ~ event, data = storm_data, FUN = sum)
ap <- aggregate(property_damage ~ event, data = storm_data, FUN = sum)

top_f <- af[order(-af$fatalities), ][1:15, ]
top_i <- ai[order(-ai$injuries), ][1:15, ]
top_p <- ap[order(-ap$property_damage), ][1:15, ]

Figures

Fatalities

Tornadoes are responsible for the most fatalities.

fatalities_plot <- ggplot(data = top_f, aes(x = reorder(event, fatalities), y = fatalities)) + 
  geom_bar( stat = "identity", aes(fill = -fatalities)) +
  ylab("Fatalities") +
  xlab("Events") +
  theme_minimal() +
  theme(legend.position='none') +
  coord_flip()

fatalities_plot + 
  ggtitle("Number of Fatalities by Event")

Injuries

Tornadoes are also responsible for the most fatalities.

injuries_plot <- ggplot(data = top_i, aes(x = reorder(event, injuries), y = injuries)) + 
  geom_bar( stat = "identity", aes(fill = -injuries)) +
  ylab("Injuries") +
  xlab("Events") +
  theme_minimal() +
  theme(legend.position='none') +
  coord_flip()

injuries_plot + 
  ggtitle("Number of Injuries by Event")

Property Damage

property_damage_plot <- ggplot(data = top_p, aes(x = reorder(event, property_damage), y = property_damage)) + 
  geom_bar( stat = "identity", aes(fill = -property_damage)) +
  ylab("Cost of Property Damages") +
  xlab("Events") +
  theme_minimal() +
  theme(legend.position='none') +
  coord_flip()

property_damage_plot + 
  ggtitle("Cost of Property Damage by Event ($)")

Conclusion

Tornadoes account for the highest number of fatalities, injuries and property damages of all storm events.

NOAA Storm Database Impacts - RepData_PeerAssessment2

Ken W.

1/4/2018