Course Project 2: Health and Economic Impacts of Severe Weather
Synopsis
The project goal is to analyse the data provided in NOAA Storm Database and examine the effects of weather events on population and economy respectively. Time period between 1950 to November 2011 is covered in the database.
The following investigation analyses and finds out which types of several weather events are most harmful on: - Health (injuries and fatalities) - Property and crops (economic consequences)
Preprocessing
- Loading required libraries to setup
library(dplyr)
library(ggplot2)- Reading the data
download.file('https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2',
destfile = paste0(getwd(), '/data2.csv.bz2'))
stormdata <- read.csv('data2.csv.bz2', stringsAsFactors = F)- Inspecting the data
names(stormdata)## [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
We select the data relevant our analysis of event type and its impact on health and economy: - EVTYPE: Weather event type - FATALITIES: No of fatalities - INJURIES: No 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)
relevantCol <- c('EVTYPE', 'FATALITIES', 'INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')
stormdataSel <- stormdata[, relevantCol]
summary(stormdataSel)## 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 <- stormdataSel[, 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- Finding the top 10 event types that cause most fatalities
HighestFat <- Q1Data[order(Q1Data$FATALITIES, decreasing = T), ]- Finding the top 10 event types that cause most injuries
HighestInj <- Q1Data[order(Q1Data$INJURIES, decreasing = T), ]- Finding the top 10 event types that cause most fatalities and injuries
Q1Data$total <- rowSums(Q1Data[, 2:3])
HighestHealth <- Q1Data[order(Q1Data$total, decreasing = T), ]Find types of events that have the greatest economic consequences
- Write a function to replace EXP type characters to usable numeric EXPTYPE values:
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)
}
}- Calculate property damage cost and crop damage cost by combining PROPDMG and PROPDMGEXP:
Q2Data <- stormdataSel[, c(1, 4:7)] %>%
rowwise() %>%
mutate(PROPDMGCOST = PROPDMG*10**getVal(PROPDMGEXP),
CROPDMGCOST = CROPDMG*10**getVal(CROPDMGEXP))
head(Q2Data)## # A tibble: 6 × 7
## # Rowwise:
## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP PROPDMGCOST CROPDMGCOST
## <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)
summary(Q2DataSum)## EVTYPE PROPDMGCOST CROPDMGCOST
## 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- Finding the top 10 event types that cause the most property
damage
HighestProp <- Q2DataSum[order(Q2DataSum$PROPDMGCOST, decreasing = T), ]- Finding the top 10 event types that cause the most crop damage
HighestCrop <- Q2DataSum[order(Q2DataSum$CROPDMGCOST, decreasing = T), ]- Finding the top 10 event types that cause the most property and crop
damages
Q2DataSum$total <- rowSums(Q2DataSum[, 2:3])
HighestEcon <- Q2DataSum[order(Q2DataSum$total, decreasing = T), ]Results
Find types of events that are most harmful with respect to population health
- Top 10 events that cause most fatalities:
HighestFat[1:10, ]## # A tibble: 10 × 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- Top 10 events that cause most injuries:
HighestInj[1:10, ]## # A tibble: 10 × 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 1361- Top 10 events that cause most health hazards:
HighestHealth[1:10, ]## # A tibble: 10 × 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 1527Following image depicts top 10 event types that cause most population health hazards (sum of fatalities and injuries):
ggplot(data = HighestHealth[1:10, ], aes(x = reorder(EVTYPE, total), y = total)) +
#need to use reorder to prevent the categorical data from reordering
geom_bar(stat = 'identity', fill="lightblue") +
coord_flip() +
xlab('Event type') +
ylab('Total injuries and fatalities') +
ggtitle('Top 10 weather events that causes most population health hazards') +
theme_light()
Find types of events that have the greatest economic consequences
- Top 10 events that cause most property damage
HighestProp[1:10, ]## # A tibble: 10 × 3
## EVTYPE PROPDMGCOST CROPDMGCOST
## <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- Top 10 events that cause most crop damage
HighestCrop[1:10, ]## # A tibble: 10 × 3
## EVTYPE PROPDMGCOST CROPDMGCOST
## <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 1094086000Following image depicts top 10 event types that cause most economic consequences (sum of property damages and crop damages)
ggplot(data = HighestEcon[1:10, ], aes(x = reorder(EVTYPE, total), y = total)) +
#need to use reorder to prevent the categorical data from reordering
geom_bar(stat = 'identity', fill= "lightgreen" ) +
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 consequences') +
theme_light()