RR Project2 : Exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database

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.

Data Processing

Download the data from file URL and read the data from bz file.

fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, 'data.csv.bz2')
Data <- read.csv("data.csv.bz2", stringsAsFactors = F)
variables <- c("EVTYPE","FATALITIES","INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
Storm <- Data[variables]
head(Storm)
##    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       0

Variable Transformation

Transforming variables In this part, the variables EVTYPE, PROPDMGEXP and CROPDMGEXP are transformed to be more clear and certain. First of all, we rename of values from EVTYPE variable with the intention of doing more easy the reading of results.

Also, We change de values from PROPDMGEXP and CROPDMGEXP because the raw data is in letters classification, and to do analysis at number level was necessary modify the answer at number level.

library(plyr)
## Warning: package 'plyr' was built under R version 3.4.2
Storm$EVTYPE <- mapvalues(Storm$EVTYPE, from = c("TSTM WIND", "THUNDERSTORM WINDS", "RIVER FLOOD", "HURRICANE/TYPHOON", "HURRICANE"), to = c("THUNDERSTORM WIND", "THUNDERSTORM WIND", "FLOOD", "HURRICANE-TYPHOON", "HURRICANE-TYPHOON"))

Storm$PROPDMGEXP <- mapvalues(Storm$PROPDMGEXP, from = c("K", "M", "", "B", "m", "+", "0", "5", "6", "?","4","2","3","h","7","H","-","1","8"), to = c(10^3,10^6,1,10^9,10^6,1,1,10^5,10^6,1,10^4,10^2,10^3,10^3,10^7,10^2,1,1,10^8))

Storm$CROPDMGEXP <- mapvalues(Storm$CROPDMGEXP, from = c("M", "K", "m", "B", "?", "0", "k", "2"), to = c(10^6,10^3,10^3,10^9,1,1,10^3,10^2))

PROP <-  (as.numeric(Storm$PROPDMGEXP)) * Storm$PROPDMG
CROP <-  (as.numeric(Storm$CROPDMGEXP)) * Storm$CROPDMG
Storm$ECONDMG <-  PROP + CROP

Results:

Questions

1.Which types of events are most harmful to population health?
2.Which types of events have the greatest economic consequences?

Approach : Get the total number of Injuries and fatalities for diiferent Event types.And calculate the total damages in decresing order

library(dplyr)
## Warning: package 'dplyr' was built under R version 3.4.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
injuries.damages <- aggregate(INJURIES~EVTYPE, data = Storm, FUN = sum)
fatalities.damages <- aggregate(FATALITIES~EVTYPE, data = Storm, FUN = sum)
total.damages<- cbind(injuries.damages,FATALITIES=(fatalities.damages$FATALITIES))
total.damages2 <- mutate(total.damages, TOTAL=INJURIES+FATALITIES)
## Warning: package 'bindrcpp' was built under R version 3.4.2
total.damages2 <- total.damages2[order(total.damages2$TOTAL, decreasing = TRUE),]
library(knitr)
## Warning: package 'knitr' was built under R version 3.4.3
kable(total.damages2[1:10,], caption="10 Principals weather events that are the most injuries and fatalities impact in population")
10 Principals weather events that are the most injuries and fatalities impact in population
EVTYPE INJURIES FATALITIES TOTAL
831 TORNADO 91346 5633 96979
758 THUNDERSTORM WIND 9353 701 10054
130 EXCESSIVE HEAT 6525 1903 8428
170 FLOOD 6791 472 7263
463 LIGHTNING 5230 816 6046
275 HEAT 2100 937 3037
153 FLASH FLOOD 1777 978 2755
426 ICE STORM 1975 89 2064
968 WINTER STORM 1321 206 1527
403 HURRICANE-TYPHOON 1321 125 1446
Genera te the bar plot to s how harmful l weather eve nts and greatest economic consequences. 
barplot(height = (total.damages2$TOTAL[1:10]/(1e3)), names.arg = total.damages2$EVTYPE[1:10],col=terrain.colors(10, alpha = 1), main = "Top 10 Injuries and Fatalities for US Weather Events", ylab = "Number of Injuries and Fatalities (thousands)", las = 2, cex.names= 0.6)

economic.damage <- aggregate(ECONDMG~EVTYPE, data = Storm, FUN = sum)
economic.damage <- economic.damage[order(economic.damage$ECONDMG, decreasing = TRUE),]
kable(economic.damage[1:10,], caption="10 Principals weather events that are the most economically costly")
10 Principals weather events that are the most economically costly
EVTYPE ECONDMG
35 FLOOD 148607551500
78 HURRICANE-TYPHOON 41806435800
127 TORNADO 16581900013
16 DROUGHT 14206287000
53 HAIL 11020743163
30 FLASH FLOOD 8749813302
85 ICE STORM 5925150850
115 THUNDERSTORM WIND 5497024289
109 STORM SURGE/TIDE 4641493000
150 WILDFIRE 3793838270 
barplot(height = (economic.damage$ECONDMG[1:10]/(1e9)), names.arg = economic.damage$EVTYPE[1:10],col=terrain.colors(10, alpha = 1), main = "Top 10 Economically Costly Events for US Weather Events", ylab = "Cost ($ billions)", las = 2, cex.names= 0.6)

Conclusion :

**Tornados are the most harmfull to population health and Floods cause the greatest economic consequences