Week 4 Assignment: Health and Economic Impacts of Severe Weather
Beydia Mohamed
Instructions
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 basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.
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. The data for this assignment can be downloaded from here
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.
Synopsis
The following analysis answers the follwing two questions:
1. Across the United States, which types of events (as indicated in the ENVTYPE variable) are most harmful with respect to population health?
2. Across the United States, which types of events have the greatest economic consequences?
Preprocessing
Loading and preprocessing the data
- Environment setup
library(dplyr)
library(ggplot2)- Read in the data
if (!file.exists('data2.csv.bz2')){
download.file('https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2',
destfile = paste0(getwd(), '/data2.csv.bz2'),
method = 'curl', quiet = T)
}
raw <- read.csv('data2.csv.bz2', stringsAsFactors = F)- Inspect the data
names(raw)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"- Select data
Since we’re only interested in the correlation of the nation-wide event type to health and economic consequense, only the following columns will be selected for further analysis:
- EVTYPE: event type
- FATALITIES: number of fatalities
- INJURIES: number of injuries
- PROPDMG: property damage (dollars)
- PROPDMGEXP: magnitude of property damage (K = thousands, M = millions, B = billions)
- CROPDMG: crop damage (dollars)
- CROPDMGEXP: magnitude of crop damage (H = hundreds, K = thousands, M = millions, B = billions)
selectCol <- c('EVTYPE', 'FATALITIES', 'INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')
rawSel <- raw[, selectCol]
summary(rawSel)## EVTYPE FATALITIES INJURIES PROPDMG
## Length:902297 Min. : 0.0000 Min. : 0.0000 Min. : 0.00
## Class :character 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00
## Mode :character Median : 0.0000 Median : 0.0000 Median : 0.00
## Mean : 0.0168 Mean : 0.1557 Mean : 12.06
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50
## Max. :583.0000 Max. :1700.0000 Max. :5000.00
## PROPDMGEXP CROPDMG CROPDMGEXP
## Length:902297 Min. : 0.000 Length:902297
## Class :character 1st Qu.: 0.000 Class :character
## Mode :character Median : 0.000 Mode :character
## Mean : 1.527
## 3rd Qu.: 0.000
## Max. :990.000Data analysis
Find types of events that are most harmful with respect to population health
- Make a table that counts the total fatalities and injuries in each event type
Q1data <- rawSel[, 1:3] %>%
group_by(EVTYPE) %>%
summarise_all(sum)
summary(Q1data)## EVTYPE FATALITIES INJURIES
## Length:985 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 0.00 1st Qu.: 0.0
## Mode :character Median : 0.00 Median : 0.0
## Mean : 15.38 Mean : 142.7
## 3rd Qu.: 0.00 3rd Qu.: 0.0
## Max. :5633.00 Max. :91346.0- Find the top 10 event types that causes the most fatalities
topFat <- Q1data[order(Q1data$FATALITIES, decreasing = T), ]- Find the top 10 event types that causes the most injuries
topInj <- Q1data[order(Q1data$INJURIES, decreasing = T), ]- Find the top 10 event types that causes the most fatalities and injuries
Q1data$total <- rowSums(Q1data[, 2:3])
topHealth <- Q1data[order(Q1data$total, decreasing = T), ]Find types of events that have the greatest economic consequences
The actual values are encoded in the ‘EXP’ column for property and crop damage
unique(rawSel$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"Note that there are numbers, characters, and capitals all mixed together (even though now they’re all factors)
- Write a function that transform the ‘EXP’ column to the factor value of 10 (10**expType)
getVal <- function(expType) {
if (expType %in% c('h', 'H')) {
return(2)
} else if (expType %in% c('k', 'K')) {
return(3)
} else if (expType %in% c('m', 'M')) {
return(6)
} else if (expType %in% c('b', 'B')) {
return(9)
} else if (suppressWarnings(!is.na(as.numeric(expType)))) {
#won't show: NAs introduced by coercion when given a non-number
return(as.numeric(expType))
} else {
return(0)
}
}- Unit testing
c(10**getVal('h'), 10**getVal(4), 10**getVal('B'), 10**getVal('?'))## [1] 1e+02 1e+04 1e+09 1e+00- Make a table that applys the function and calculates the actual value
Q2data <- rawSel[, c(1, 4:7)] %>%
rowwise() %>%
mutate(PROP = PROPDMG*10**getVal(PROPDMGEXP),
CROP = CROPDMG*10**getVal(CROPDMGEXP))
head(Q2data)## Source: local data frame [6 x 7]
## Groups: <by row>
##
## # A tibble: 6 x 7
## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP PROP CROP
## <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
## 1 TORNADO 25 K 0 "" 25000 0
## 2 TORNADO 2.5 K 0 "" 2500 0
## 3 TORNADO 25 K 0 "" 25000 0
## 4 TORNADO 2.5 K 0 "" 2500 0
## 5 TORNADO 2.5 K 0 "" 2500 0
## 6 TORNADO 2.5 K 0 "" 2500 0Q2dataSum <- Q2data[, c(1, 6, 7)] %>%
group_by(EVTYPE) %>%
summarise_all(sum)## Warning: Grouping rowwise data frame strips rowwise naturesummary(Q2dataSum)## EVTYPE PROP CROP
## Length:985 Min. :0.000e+00 Min. :0.000e+00
## Class :character 1st Qu.:0.000e+00 1st Qu.:0.000e+00
## Mode :character Median :0.000e+00 Median :0.000e+00
## Mean :4.347e+08 Mean :4.985e+07
## 3rd Qu.:5.105e+04 3rd Qu.:0.000e+00
## Max. :1.447e+11 Max. :1.397e+10- Find the top 10 event types that causes the most property damage
topProp <- Q2dataSum[order(Q2dataSum$PROP, decreasing = T), ]- Find the top 10 event types that causes the most crop damage
topCrop <- Q2dataSum[order(Q2dataSum$CROP, decreasing = T), ]- Find toe top 10 event types that causes the most property and crop damages
Q2dataSum$total <- rowSums(Q2dataSum[, 2:3])
topEcon <- Q2dataSum[order(Q2dataSum$total, decreasing = T), ]Results
Find types of events that are most harmful with respect to population health
If separated by fatalities and injuries, the top 10 events that causes the most
- fatilities
topFat[1:10, ]## # A tibble: 10 x 3
## EVTYPE FATALITIES INJURIES
## <chr> <dbl> <dbl>
## 1 TORNADO 5633 91346
## 2 EXCESSIVE HEAT 1903 6525
## 3 FLASH FLOOD 978 1777
## 4 HEAT 937 2100
## 5 LIGHTNING 816 5230
## 6 TSTM WIND 504 6957
## 7 FLOOD 470 6789
## 8 RIP CURRENT 368 232
## 9 HIGH WIND 248 1137
## 10 AVALANCHE 224 170- injuries
topInj[1:10, ]## # A tibble: 10 x 3
## EVTYPE FATALITIES INJURIES
## <chr> <dbl> <dbl>
## 1 TORNADO 5633 91346
## 2 TSTM WIND 504 6957
## 3 FLOOD 470 6789
## 4 EXCESSIVE HEAT 1903 6525
## 5 LIGHTNING 816 5230
## 6 HEAT 937 2100
## 7 ICE STORM 89 1975
## 8 FLASH FLOOD 978 1777
## 9 THUNDERSTORM WIND 133 1488
## 10 HAIL 15 1361If adding the numbers of fatalities and injuries and ranked by the total number, the top 10 events that causes the most population health are as follows
topHealth[1:10, ]## # A tibble: 10 x 4
## EVTYPE FATALITIES INJURIES total
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 EXCESSIVE HEAT 1903 6525 8428
## 3 TSTM WIND 504 6957 7461
## 4 FLOOD 470 6789 7259
## 5 LIGHTNING 816 5230 6046
## 6 HEAT 937 2100 3037
## 7 FLASH FLOOD 978 1777 2755
## 8 ICE STORM 89 1975 2064
## 9 THUNDERSTORM WIND 133 1488 1621
## 10 WINTER STORM 206 1321 1527The following figure depicts top 10 event types that causes population health hazards (sum of fatalities and injuries)
ggplot(data = topHealth[1:10, ], aes(x = reorder(EVTYPE, total), y = total)) +
#need to use reorder to prevent the categorical data from reordering
geom_bar(stat = 'identity') +
coord_flip() +
xlab('Event type') +
ylab('Total injuries and fatalities') +
ggtitle('Top 10 weather events that causes population health hazards') +
theme_classic()
As the figure is shown, tornados are the most dangerous weather event that causes injuries and fatalities
Find types of events that have the greatest economic consequences
If separated by property and crop damages, the top 10 events that causes the most
- property damage
topProp[1:10, ]## # A tibble: 10 x 3
## EVTYPE PROP CROP
## <chr> <dbl> <dbl>
## 1 FLOOD 144657709807 5661968450
## 2 HURRICANE/TYPHOON 69305840000 2607872800
## 3 TORNADO 56947380676. 414953270
## 4 STORM SURGE 43323536000 5000
## 5 FLASH FLOOD 16822673978. 1421317100
## 6 HAIL 15735267513. 3025954473
## 7 HURRICANE 11868319010 2741910000
## 8 TROPICAL STORM 7703890550 678346000
## 9 WINTER STORM 6688497251 26944000
## 10 HIGH WIND 5270046295 638571300- crop damage
topCrop[1:10, ]## # A tibble: 10 x 3
## EVTYPE PROP CROP
## <chr> <dbl> <dbl>
## 1 DROUGHT 1046106000 13972566000
## 2 FLOOD 144657709807 5661968450
## 3 RIVER FLOOD 5118945500 5029459000
## 4 ICE STORM 3944927860 5022113500
## 5 HAIL 15735267513. 3025954473
## 6 HURRICANE 11868319010 2741910000
## 7 HURRICANE/TYPHOON 69305840000 2607872800
## 8 FLASH FLOOD 16822673978. 1421317100
## 9 EXTREME COLD 67737400 1292973000
## 10 FROST/FREEZE 9480000 1094086000If combining the amounts lost in property and crop damage and ranked by the total number, the top 10 events that causes the most economic damages are
ggplot(data = topEcon[1:10, ], aes(x = reorder(EVTYPE, total), y = total)) +
#need to use reorder to prevent the categorical data from reordering
geom_bar(stat = 'identity') +
coord_flip() +
scale_y_continuous(trans = 'log10') +
xlab('Event type') +
ylab('Total property and crop damages (log10)') +
ggtitle('Top 10 weather events that causes economic hazards') +
theme_classic()
As the figure is shown, flash flood causes by a significant amount of property and crop damages. It should be noted that the property damage amount is significantly more that crop damage amount, as shown in summary below - the mean and maximum values fro property loss is 10**3 more than crops!
summary(topEcon[, -4])## EVTYPE PROP CROP
## Length:985 Min. :0.000e+00 Min. :0.000e+00
## Class :character 1st Qu.:0.000e+00 1st Qu.:0.000e+00
## Mode :character Median :0.000e+00 Median :0.000e+00
## Mean :4.347e+08 Mean :4.985e+07
## 3rd Qu.:5.105e+04 3rd Qu.:0.000e+00
## Max. :1.447e+11 Max. :1.397e+10