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 report discussed the impact of various weather event on public health and economy of the United States.
Following packages have been loaded for this analysis:
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## %in%, :, is.double, match, order, rankThis analysis is based on a data set gathered from 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.
Following code snippet shows the steps to download the NOAA storm data set and load it into a data frame
cache = TRUE
#file_url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
#download.file(file_url,"dataset.csv.bz2",method="libcurl")
#bunzip2("dataset.csv.bz2", overwrite=T, remove=F)
df <- read.csv("dataset.csv")Sample data from the NOAA storm data set is listed below:
head(df)## 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 6Following shows the dimension of the data set:
dim(df)## [1] 902297 37It shows that the data set has 902297 of records and it consists of 37 attributes.
This study will only focus on identifying top 10 weather events that have most impact to public health and economy of the United States from year 1950 to year 2011.
Based on the data set attributes, we think that the following should be aggregated for impact analysis:
| Area of Concern | Attributes |
|---|---|
| Public Health | FATALITIES, INJURIES |
| Economy | PROPDMG, CROPDMG |
The measurement of impact is listed below:
NEI_Coal <- sqldf(‘select year, Emissions from merge_df where shortname like “%Coal%Combustion%”’)
NEI_year <- group_by(NEI_Coal,year) total_em <- summarise(NEI_year, Total_Emissions=sum(Emissions))