Exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database
Yunpeng
July 1, 2018
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 involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
The project focuses on 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
Step 1: Load libraries and prepare the R environment.
# load library
library(lattice)
# for big numbers
options(scipen = 20)Step 2: Load and read the dataset for the project.
fileurl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
if (!file.exists('data')) dir.create('./data')
download.file(fileurl, destfile = './data/repdata%2Fdata%2FStormData.csv.bz2')
rawdata <- read.csv('./data/repdata%2Fdata%2FStormData.csv.bz2')# snapshot of the dataset
str(rawdata)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : Factor w/ 436781 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...Step 3: Pre-process and transform the data
Part 1:
# calculate the sum of "FATALITIES" and "INJURIES" by "EVTYPE" and then merge
sum_fatal <- aggregate(FATALITIES ~ EVTYPE, data=rawdata, sum)
sum_injure <- aggregate(INJURIES ~ EVTYPE, data=rawdata, sum)
sum_health <- merge(sum_fatal, sum_injure, by = "EVTYPE")
# take top3% of the new dataset.
sum1 <- subset(sum_health, sum_health$FATALITIES>quantile(sum_health$FATALITIES, 0.97))Part 2:
# explore the units for "PROPDMG" and "CROPDMG" respectively
table(rawdata$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5
## 465934 1 8 5 216 25 13 4 4 28
## 6 7 8 B h H K m M
## 4 5 1 40 1 6 424665 7 11330table(rawdata$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994# create new vectors of "propdmg2" and "cropdmg2" after considering their units.
rawdata$propdmg2 <- rawdata$PROPDMG
rawdata$propdmg2[which(rawdata$PROPDMGEXP == "K")] =
rawdata$PROPDMG[which(rawdata$PROPDMGEXP == "K")] * 1000
rawdata$propdmg2[which(rawdata$PROPDMGEXP == "M" | rawdata$PROPDMGEXP == "m")] =
rawdata$PROPDMG[which(rawdata$PROPDMGEXP == "M" | rawdata$PROPDMGEXP == "m")] * 1000000
rawdata$propdmg2[which(rawdata$PROPDMGEXP == "B")] =
rawdata$PROPDMG[which(rawdata$PROPDMGEXP == "B")] * 1000000000
rawdata$cropdmg2 <- rawdata$CROPDMG
rawdata$cropdmg2[which(rawdata$CROPDMGEXP == "K" | rawdata$CROPDMGEXP == "k")] =
rawdata$CROPDMG[which(rawdata$CROPDMGEXP == "K" | rawdata$CROPDMGEXP == "k")] * 1000
rawdata$cropdmg2[which(rawdata$CROPDMGEXP == "M" | rawdata$CROPDMGEXP == "m")] =
rawdata$CROPDMG[which(rawdata$CROPDMGEXP == "M" | rawdata$CROPDMGEXP == "m")] * 1000000
rawdata$cropdmg2[which(rawdata$CROPDMGEXP == "B")] =
rawdata$CROPDMG[which(rawdata$CROPDMGEXP == "B")] * 1000000000# calculate the sum of "propdmg2" and "cropdmg2" respectively by "EVTYPE"
# merge them by "EVTYPE" to form a new dataframe.
sum_propdmg <- aggregate(propdmg2 ~ EVTYPE, data = rawdata, sum)
sum_cropdmg <- aggregate(cropdmg2 ~ EVTYPE, data = rawdata, sum)
sum_dmg <- merge(sum_propdmg, sum_cropdmg, by = "EVTYPE")
# take top3% of the dataset.
sum2 <- subset(sum_dmg, sum_dmg$propdmg2 > quantile(sum_dmg$propdmg2, 0.97))Results:
Across the United States, which types of events are most harmful with respect to population health?
# make a stacking barplot with "lattice"
barchart(EVTYPE ~ FATALITIES + INJURIES,
sum1,
stack = TRUE,
col=rainbow(2,alpha=0.5),
box.ratio =100)
From the graph, we can conclude that “TORNADO” is the most harmful.
Across the United States, which types of events have the greatest economic consequences?
# make a stacking barplot with "lattice"
barchart(
EVTYPE ~propdmg2 + cropdmg2,
sum2,
stack = TRUE,
col=rainbow(2,alpha=0.5),
box.ratio =100
)
From the graph, we can see:
- “FLOOD” is the most harmful to properites.
- “DROUGHT” is the most harmful to crops.