Reproducible Research Project 2
Studies on the effect of weather events on population health as well as economic consequences in US based on NOAA database
1.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.
In this report,effect of weather events on personal as well as property damages was studied. Barplots were plotted seperately for the top 6 weather events that causes highest fatalities, highest injuries and highest economic loss. Results indicate that most fatalities and injuries were caused by Tornados, and the highest economic damages were caused by flood.
2. Data Processing
The data was downloaded from https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2 and saved on local computer. Then it was loaded on the R using the following code.
# Loading data into R
storm <- read.csv("repdata_data_StormData.csv")
head(storm)## 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 63. Results
Question #1 Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
library(plyr)
#summarize number of injuries and fatalities by different weather events
injuries = ddply(storm, .(EVTYPE), summarize, sum.injuries = sum(INJURIES,na.rm=TRUE))
injuries = injuries[order(injuries$sum.injuries, decreasing = TRUE), ]
head(injuries, 5)## EVTYPE sum.injuries
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230We see that tornado is the most harmful event with injuries. Top 6 events with the most injuries are represented in the below figure:
library(ggplot2)
ggplot(injuries[1:6, ], aes(EVTYPE, sum.injuries, fill = EVTYPE,alpha=0.5)) + geom_bar(stat = "identity") +
xlab("Event Type") + ylab("Number of Injuries") + ggtitle("Injuries by Event type") + coord_flip()
Now we check for the most fatalities events.
fatalities = ddply(storm, .(EVTYPE), summarize, sum = sum(FATALITIES))
fatalities = fatalities[order(fatalities$sum, decreasing = TRUE), ]
head(fatalities, 5)## EVTYPE sum
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816We see that it is tornado again with the most fatalities. Top 6 events with the most fatalities are represented in the below figure:
ggplot(fatalities[1:6, ], aes(EVTYPE, sum, fill=EVTYPE,alpha=0.3)) + geom_bar(stat = "identity") +
xlab("Event Type") + ylab("Number of Fatalities") + ggtitle("Fatalities by Event type") + coord_flip()
Question #2 Across the United States, which types of events have the greatest economic consequences?
#check for PROPDMGEXP and CROPDMGEXP
unique(storm$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Munique(storm$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m M#As some have lower character, we convert them to upper character. Also we
#replace symbols other than character of numeric values to 0.
storm$PROPDMGEXP <- toupper(storm$PROPDMGEXP)
storm$PROPDMGEXP[storm$PROPDMGEXP %in% c("", "+", "-", "?")] = "0"
storm$CROPDMGEXP <- toupper(storm$CROPDMGEXP)
storm$CROPDMGEXP[storm$CROPDMGEXP %in% c("", "?")] = "0"
#Convert PROPDMGEXP and CROPDMGEXP
storm$PROPDMGEXP[storm$PROPDMGEXP %in% c("B")] = "9"
storm$PROPDMGEXP[storm$PROPDMGEXP %in% c("M")] = "6"
storm$PROPDMGEXP[storm$PROPDMGEXP %in% c("K")] = "3"
storm$PROPDMGEXP[storm$PROPDMGEXP %in% c("H")] = "2"
storm$CROPDMGEXP[storm$CROPDMGEXP %in% c("B")] = "9"
storm$CROPDMGEXP[storm$CROPDMGEXP %in% c("M")] = "6"
storm$CROPDMGEXP[storm$CROPDMGEXP %in% c("K")] = "3"
storm$CROPDMGEXP[storm$CROPDMGEXP %in% c("H")] = "2"
#calculate total damage by multiplying the damage by the corresponding exponent for PROP and CROP
storm$PROPDMGEXP <- 10^(as.numeric(storm$PROPDMGEXP))
damage.property = storm$PROPDMG * storm$PROPDMGEXP
storm=as.data.frame(cbind(storm,damage.property))
storm$CROPDMGEXP <- 10^(as.numeric(storm$CROPDMGEXP))
damage.crop = storm$CROPDMG * storm$CROPDMGEXP
storm=as.data.frame(cbind(storm,damage.crop))Now we check for the events with most economic damages (both PROP and CROP).
#calculate PROP, CROP damages separately
Damage.property = ddply(storm, .(EVTYPE), summarize, damage.property = sum(damage.property, na.rm = TRUE))
Damage.property = Damage.property[order(Damage.property$damage.property, decreasing = T), ]
Damage.crop = ddply(storm, .(EVTYPE), summarize, damage.crop = sum(damage.crop, na.rm = TRUE))
Damage.crop = Damage.crop[order(Damage.crop$damage.crop, decreasing = T), ]
# calculate total damages of both
total.damage = damage.property + damage.crop
storm=as.data.frame(cbind(storm,total.damage))
Damage.total = ddply(storm, .(EVTYPE), summarize, damage.total = sum(total.damage, na.rm = TRUE))
Damage.total = Damage.total[order(Damage.total$damage.total, decreasing = T), ]
head(Damage.total)## EVTYPE damage.total
## 170 FLOOD 150319678257
## 411 HURRICANE/TYPHOON 71913712800
## 834 TORNADO 57362333947
## 670 STORM SURGE 43323541000
## 244 HAIL 18761221986
## 153 FLASH FLOOD 18243991079We see that flood cause the most economic loss. Top 6 events with the most economic damages are represented in the below figure:
ggplot(Damage.total[1:6, ], aes(EVTYPE, damage.total, fill = EVTYPE, alpha=0.5)) + geom_bar(stat = "identity") +
xlab("Event Type") + ylab("Total damages") + ggtitle("Total damages by Event type") + coord_flip()