Project Name: Using the United States NOAA Storm Database to explore the affect of severe weather conditions-Public health & Economic consequences
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.
The 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.
We subset the fatalities, injuries, property damage and crop damage from the dataset to present the results.
Finding the answers to the following
1.Top weather events that are harmful to public health 2.Top events that had severe damage to the properties and crops
Preprocessing of the NOAA Storm Data zip file downloaded from the coursera to the working directory
unzipping the csv file in to the working directory
unzip("repdata_data_activity.zip")loading the data and creating new data file
stormdata <- read.csv("repdata_data_StormData.csv", sep = ",", header = T)
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 6subsetting the required columns
substormdata <- stormdata[,c(8,23:28)]
head(substormdata)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 01.Across the United States, which types of events are most harmful with respect to population health?
DATA ANALYSIS:
Aggregating number of injuries and fatalities to derive the events that are harmful to public health
injuries_data <- aggregate(INJURIES~EVTYPE, substormdata, sum)
injuries_data <- arrange(injuries_data, desc(INJURIES))
injuries_data <- injuries_data[1:20,]
injuries_data## EVTYPE INJURIES
## 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
## 11 WINTER STORM 1321
## 12 HURRICANE/TYPHOON 1275
## 13 HIGH WIND 1137
## 14 HEAVY SNOW 1021
## 15 WILDFIRE 911
## 16 THUNDERSTORM WINDS 908
## 17 BLIZZARD 805
## 18 FOG 734
## 19 WILD/FOREST FIRE 545
## 20 DUST STORM 440fatalities_data <- aggregate(FATALITIES~EVTYPE, substormdata, sum)
fatalities_data <- arrange(fatalities_data,desc(FATALITIES))
fatalities_data <- fatalities_data[1:20,]
fatalities_data## EVTYPE FATALITIES
## 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
## 11 WINTER STORM 206
## 12 RIP CURRENTS 204
## 13 HEAT WAVE 172
## 14 EXTREME COLD 160
## 15 THUNDERSTORM WIND 133
## 16 HEAVY SNOW 127
## 17 EXTREME COLD/WIND CHILL 125
## 18 STRONG WIND 103
## 19 BLIZZARD 101
## 20 HIGH SURF 101Total number of injuries and fatalities
mergeddata <- merge(injuries_data, fatalities_data, by = "EVTYPE")
harmful_events <- arrange(mergeddata, desc(FATALITIES+INJURIES))
head(harmful_events)## EVTYPE INJURIES FATALITIES
## 1 TORNADO 91346 5633
## 2 EXCESSIVE HEAT 6525 1903
## 3 TSTM WIND 6957 504
## 4 FLOOD 6789 470
## 5 LIGHTNING 5230 816
## 6 HEAT 2100 937RESULT
Barplot showing the top 20 most harmful events with respect to population health across the United States
names_of_events <- harmful_events$EVTYPE
barplot(t(harmful_events[,-1]),names.arg = names_of_events, beside= T, col = c("green", "red"), cex.names= 0.8, las=2, ylim = c(0,92000),
main = "Harmful events with respect to Public health",ylab="Number of injuries and fatalities")
legend("topright",c("fatlalities","injuries"), fill = c("green","red"),bty = "n")
TORNADO are the most harmful to the population health across the U.S.
2.Across the United States, which types of events have the greatest economic consequences?
DATA ANALYISIS
head(substormdata)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0table(substormdata$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(substormdata$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994created new variables PROPDAMAGE and CROPDAMAGE (Damage in USD) from the PROPDMG and CROPDMG respectively
EXPENSE codes: H = Hundred= 10^2, K = Thousand= 10^3, M = Million=10^6, B = Billion=10^9
substormdata$PROPDAMAGE <- 0
substormdata[substormdata$PROPDMGEXP == "H", ]$PROPDAMAGE = substormdata[substormdata$PROPDMGEXP == "H", ]$PROPDMG * 10^2
substormdata[substormdata$PROPDMGEXP == "K", ]$PROPDAMAGE = substormdata[substormdata$PROPDMGEXP == "K", ]$PROPDMG * 10^3
substormdata[substormdata$PROPDMGEXP == "k", ]$PROPDAMAGE = substormdata[substormdata$PROPDMGEXP == "k", ]$PROPDMG * 10^3
substormdata[substormdata$PROPDMGEXP == "m", ]$PROPDAMAGE = substormdata[substormdata$PROPDMGEXP == "m", ]$PROPDMG * 10^6
substormdata[substormdata$PROPDMGEXP == "M", ]$PROPDAMAGE = substormdata[substormdata$PROPDMGEXP == "M", ]$PROPDMG * 10^6
substormdata[substormdata$PROPDMGEXP == "B", ]$PROPDAMAGE = substormdata[substormdata$PROPDMGEXP == "B", ]$PROPDMG * 10^9substormdata$CROPDAMAGE <- 0
substormdata[substormdata$CROPDMGEXP == "H", ]$CROPDAMAGE = substormdata[substormdata$CROPDMGEXP == "H", ]$CROPDMG * 10^2
substormdata[substormdata$CROPDMGEXP == "k", ]$CROPDAMAGE = substormdata[substormdata$CROPDMGEXP == "k", ]$CROPDMG * 10^3
substormdata[substormdata$CROPDMGEXP == "K", ]$CROPDAMAGE = substormdata[substormdata$CROPDMGEXP == "K", ]$CROPDMG * 10^3
substormdata[substormdata$CROPDMGEXP == "m", ]$CROPDAMAGE = substormdata[substormdata$CROPDMGEXP == "m", ]$CROPDMG * 10^6
substormdata[substormdata$CROPDMGEXP == "M", ]$CROPDAMAGE = substormdata[substormdata$CROPDMGEXP == "M", ]$CROPDMG * 10^6
substormdata[substormdata$CROPDMGEXP == "B", ]$CROPDAMAGE = substormdata[substormdata$CROPDMGEXP == "B", ]$CROPDMG * 10^9Total damage to properties and crops
economic_damage <- aggregate(PROPDAMAGE + CROPDAMAGE ~ EVTYPE, substormdata, sum)
names(economic_damage) = c("EVENT_TYPE", "TOTAL_DAMAGE")
economic_damage <- arrange(economic_damage, desc(TOTAL_DAMAGE))
economic_damage <- economic_damage[1:20, ]
economic_damage$TOTAL_DAMAGE <- economic_damage$TOTAL_DAMAGE/10^9
economic_damage$EVENT_TYPE <- factor(economic_damage$EVENT_TYPE, levels = economic_damage$EVENT_TYPE)
head(economic_damage)## EVENT_TYPE TOTAL_DAMAGE
## 1 FLOOD 150.31968
## 2 HURRICANE/TYPHOON 71.91371
## 3 TORNADO 57.35211
## 4 STORM SURGE 43.32354
## 5 HAIL 18.75822
## 6 FLASH FLOOD 17.56213RESULT
Barplot showing the top 20 most harmful events that have the greatest economic consequences across the United States
names_of_events <- economic_damage$EVENT_TYPE
barplot(t(economic_damage[,2]),names.arg = names_of_events, col = "green", beside= T, cex.names= 0.8, las=2, ylim = c(0,152),
main = "Harmful events that caused the greatest damage",ylab = "Damage in the USD(10^9)")