Reproducible Research - Course Project 2 : NOAA Storm Database analysis / Population health and economic impact of weather events accross the United States

Synopsis: The goal of this project is to analyze the NOAA Storm Database. The datas cover a period from 1950 to November 2011. Two questions will be treated during this analysis:

• 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? In the first part, Data Processing, we will discover the different steps to process and transform the datas. In the second part, Results, we will treat the two questions above

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Data Processing

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Let’s download the datas and have a quick overview

if (!file.exists("StormData.csv.bz2")) {
    download.file(url = "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile="StormData.csv.bz2")
}
datas <- read.csv("StormData.csv.bz2")
head(datas)

##   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              6

dim(datas)

## [1] 902297     37

Let’s process and prepare all the datas for the first question about population health impact

# POPHELATHIMPACT will contain the sum of fatalities and injuries
# We create a dedicated dataset 
# We sort the dataset descending by sum of injury and fatality
# Let's limit the dataset to the top ten event types

datas$POPHEALTHIMPACT = datas$FATALITIES + datas$INJURIES

populationHealthImpactDatas = aggregate(datas$POPHEALTHIMPACT, by=list((datas$EVTYPE)),sum)
colnames(populationHealthImpactDatas) = c("EVTYPE", "SUMFATINJ")

populationHealthImpactDatas = populationHealthImpactDatas[with(populationHealthImpactDatas, order(-SUMFATINJ)),]

populationHealthImpactDatas = populationHealthImpactDatas[1:10,]

Let’s process and prepare all the datas for the second question about economic consequences

# TOTALDMG will contain the sum of property and crops damages
# We create a dedicated dataset 
# We sort the dataset descending by sum of property and crops damages
# Let's limit the dataset to the top ten event types

datas$TOTALDMG = datas$PROPDMG + datas$CROPDMG

economicConsequencesImpactDatas = aggregate(datas$TOTALDMG, by=list((datas$EVTYPE)),sum)
colnames(economicConsequencesImpactDatas) = c("EVTYPE", "SUMECODMG")

economicConsequencesImpactDatas = economicConsequencesImpactDatas[with(economicConsequencesImpactDatas, order(-SUMECODMG)),]

economicConsequencesImpactDatas = economicConsequencesImpactDatas[1:10,]

Results

We create the bar plot for fatalities and injuries

par(mar = c(12, 10, 3, 7), mgp = c(5, 1, 0))
barplot(populationHealthImpactDatas$SUMFATINJ, las = 2, main = "10 types of events most harmful with respect to population health", names.arg = populationHealthImpactDatas$EVTYPE, ylab = "Sum of injuries and fatalities", col = c('red'))

We create the bar plot for properties and crops economic consequences

par(mar = c(12, 8, 3, 7), mgp = c(5, 1, 0))
barplot(economicConsequencesImpactDatas$SUMECODMG, las = 2, main = "10 types of events with the greatest economic consequences", names.arg = economicConsequencesImpactDatas$EVTYPE, ylab = "Sum of properties and crops consequences", col = c('blue'))