Synopsis
The 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. In this work we explore the data for the purpose to answer two questions:
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? In this section we assess the number of fatalities and injuries with respect to types of events.
Across the United States, which types of events have the greatest economic consequences? In this section we assess the amounts of property damage with respect to types of events.
The main method which we use in the analysis is frequency analysis. The results shows that we can pick out “Top 10” of the most harmful types of events with respect to population health (namely fatalities and injures) and property damages (see section Results).
Data Processing
The analisys includes the following steps.
data = read.csv("C:/Users/User/Dropbox/reproducible research/assigment2/repdata_data_StormData.csv.bz2")
The first step is to calculate the total number of fatalities across the types of events.
sum.fatal.by.type = by(data$FATALITIES, data$EVTYPE, sum, na.rm = T)
Next we find the types of events in which non-zero fatalities are observed and sort them in increasing order.
sum.fatal.nonzero = sort(sum.fatal.by.type[sum.fatal.by.type > 0])
We consider as the most harmful the last 10 types of events in the variational series.
tail(sum.fatal.nonzero, 10)
## data$EVTYPE
## AVALANCHE HIGH WIND RIP CURRENT FLOOD TSTM WIND
## 224 248 368 470 504
## LIGHTNING HEAT FLASH FLOOD EXCESSIVE HEAT TORNADO
## 816 937 978 1903 5633
Among this list we can emphasize
Error in names(tail(sum.fatal.nonzero), 1) :
2 аргумента переданы 'names', а требуется 1
and EXCESSIVE HEAT as the most dangerous events in sense of mortality.
Next we examine the number of injures in the same way as fatalities.
sum.inj.by.type = by(data$INJURIES, data$EVTYPE, sum, na.rm = T)
sum.inj.nonzero = sort(sum.inj.by.type[sum.inj.by.type > 0])
tail(sum.inj.nonzero, 10)
## data$EVTYPE
## HAIL THUNDERSTORM WIND FLASH FLOOD ICE STORM
## 1361 1488 1777 1975
## HEAT LIGHTNING EXCESSIVE HEAT FLOOD
## 2100 5230 6525 6789
## TSTM WIND TORNADO
## 6957 91346
In this case we can mark out TORNADO as the most harmful type of events.
We examine the amounts of property damage in the same way as fatalities and injuries. We consider the amounts of damage more $100000 as outstanding values
sum.propdmg.by.type = by(data$PROPDMG, data$EVTYPE, sum, na.rm = T)
sum.propdmg.nonzero = sort(sum.propdmg.by.type[sum.propdmg.by.type > 0])
sum.propdmg.nonzero[sum.propdmg.nonzero > 1e+05]
## data$EVTYPE
## HEAVY SNOW WINTER STORM HIGH WIND
## 122252 132721 324732
## THUNDERSTORM WINDS LIGHTNING HAIL
## 446293 603352 688693
## THUNDERSTORM WIND FLOOD TSTM WIND
## 876844 899938 1335966
## FLASH FLOOD TORNADO
## 1420125 3212258
Results
The most harmful types of events with respect to population health
We used the numbers of fatalities and injuries as the main measurements of harmfulness of the events. The distributions of the two variables are represented in the figure 1.
Figure 1. Distribution of total numbers of fatalities (left panel) and injures (right panel) in different types of events.
In the fig. 1 we can see two obvious outliers in fatalities. These are TORNADO (the most dangerous) and EXCESSIVE HEAT (the second).
In addition we can pick out “Top 10” of the most harmful in respect of mortality events:
## data$EVTYPE
## AVALANCHE HIGH WIND RIP CURRENT FLOOD TSTM WIND
## 224 248 368 470 504
## LIGHTNING HEAT FLASH FLOOD EXCESSIVE HEAT TORNADO
## 816 937 978 1903 5633
In the right panel of fig.1 there is one remarkable outlier, this is TORNADO. The “Top 10 most harmful types of events” in this case is the follow:
## data$EVTYPE
## HAIL THUNDERSTORM WIND FLASH FLOOD ICE STORM
## 1361 1488 1777 1975
## HEAT LIGHTNING EXCESSIVE HEAT FLOOD
## 2100 5230 6525 6789
## TSTM WIND TORNADO
## 6957 91346
Types of events with the greatest economic consequences
The distribution of amounts of property damages in different types of events is presented in figure 2.
Figure 2. Distribution of total amounts of property damages in different types of events.
There is one outlier in the figure 2 - 3.2123 × 106.
If we consider the amounts of damage more $100000 as remarkable, there are 11 the most destructive types of events:
## data$EVTYPE
## HEAVY SNOW WINTER STORM HIGH WIND
## 122252 132721 324732
## THUNDERSTORM WINDS LIGHTNING HAIL
## 446293 603352 688693
## THUNDERSTORM WIND FLOOD TSTM WIND
## 876844 899938 1335966
## FLASH FLOOD TORNADO
## 1420125 3212258
Summary
The most dangerous with respect to as population health as economic consequences are tornados. Beside this type of events we can pick out the others, the lists are presented in Results section.