Storm Data Analysis
Synopsys
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.
- Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
This analysis is based on 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 file can be downloaded from the course web site:
Storm Data [47Mb]
Data Processing
#Required packages
library(ggplot2)# Download and unzip the file:
if(!file.exists("./stormData")) {dir.create("./stormData")}
urlzip <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(urlzip, destfile = "./stormData/StormData.csv.bz2" )
# Load data into R
stormData <- read.csv("./stormData/StormData.csv.bz2")
# See the structure of tha data
str(stormData)## '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 ...Fatalities calculated by type:
fatal <- aggregate(FATALITIES ~ EVTYPE, data = stormData, sum)
fatal <- fatal[fatal$FATALITIES > 0, ]
fatal <- fatal[order(fatal$FATALITIES, decreasing = T),]
head(fatal, 10)## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 224Result:
ggplot(fatal[1:10,], aes(reorder(EVTYPE, -FATALITIES), FATALITIES, fill = EVTYPE)) +
geom_bar(stat = "identity") +
geom_text(aes(label = FATALITIES), vjust = -0.5, colour = "black") +
labs(title = "The 10 most Fatal Events", y = "Fatalities", x = "Events") +
scale_fill_discrete(guide = FALSE)
Injuries calculated by type:
inj <- aggregate(INJURIES ~ EVTYPE, data = stormData, sum)
inj <- inj[inj$INJURIES > 0, ]
inj <- inj[order(inj$INJURIES, decreasing = T), ]
head(inj, 10)## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100
## 427 ICE STORM 1975
## 153 FLASH FLOOD 1777
## 760 THUNDERSTORM WIND 1488
## 244 HAIL 1361Result:
ggplot(inj[1:10,], aes(reorder(EVTYPE, -INJURIES), INJURIES, fill = EVTYPE)) +
geom_bar(stat = "identity") +
geom_text(aes(label = INJURIES), vjust = -0.5, colour = "black") +
labs(title = "The 10 Events caused most Injuries", y = "Injuries", x = "Events") +
scale_fill_discrete(guide = FALSE)
The events caused both major fatalities and injuries
intersect(fatal[1:10, 1], inj[1:10,1 ])## [1] "TORNADO" "EXCESSIVE HEAT" "FLASH FLOOD" "HEAT"
## [5] "LIGHTNING" "TSTM WIND" "FLOOD"For the second qestion we need to calculate the economic cost of the storm events:
economicDamage <- aggregate(CROPDMG + PROPDMG ~ EVTYPE, data = stormData, sum)
economicDamage <- economicDamage[order(economicDamage$`CROPDMG + PROPDMG`, decreasing = T),]
head(economicDamage, 10)## EVTYPE CROPDMG + PROPDMG
## 834 TORNADO 3312276.7
## 153 FLASH FLOOD 1599325.1
## 856 TSTM WIND 1445168.2
## 244 HAIL 1268289.7
## 170 FLOOD 1067976.4
## 760 THUNDERSTORM WIND 943635.6
## 464 LIGHTNING 606932.4
## 786 THUNDERSTORM WINDS 464978.1
## 359 HIGH WIND 342014.8
## 972 WINTER STORM 134699.6Result:
ggplot(economicDamage[1:10,], aes(reorder(EVTYPE, -`CROPDMG + PROPDMG`), `CROPDMG + PROPDMG`, fill = EVTYPE)) +
geom_bar(stat = "identity") +
geom_text(aes(label = `CROPDMG + PROPDMG`), vjust = 1.2, colour = "white", size = 3.5) +
labs(title = "The 10 Events caused most economic damage in $", y = "Cost", x = "Events") +
scale_fill_discrete(guide = FALSE)