# Loading data
dsNOAA <- read.csv(choose.files(), sep=",", header=T)
# Subset (NOAA) storm database
tidyNOAA <- dsNOAA[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]
# Convert H, K, M, B units to calculate Property Damage 
tidyNOAA$PROPDMGNUM = 0
tidyNOAA[tidyNOAA$PROPDMGEXP == "H", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "H", ]$PROPDMG * 10^2
tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMG * 10^3
tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMG * 10^6
tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMG * 10^9
# Convert H, K, M, B units to calculate Crop Damage
tidyNOAA$CROPDMGNUM = 0
tidyNOAA[tidyNOAA$CROPDMGEXP == "H", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "H", ]$CROPDMG * 10^2
tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMG * 10^3
tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMG * 10^6
tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMG * 10^9

Results Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health ?

# import ggplot2 library to plot the result
library(ggplot2)
# plot number of fatalities with the most harmful event type
fatalities <- aggregate(FATALITIES ~ EVTYPE, data=tidyNOAA, sum)
fatalities <- fatalities[order(-fatalities$FATALITIES), ][1:10, ]
fatalities$EVTYPE <- factor(fatalities$EVTYPE, levels = fatalities$EVTYPE)

ggplot(fatalities, aes(x = EVTYPE, y = FATALITIES)) + 
    geom_bar(stat = "identity", fill = "red", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event Type") + ylab("Fatalities") + ggtitle("Number of fatalities by top 10 Weather Events")

# import ggplot2 library to plot the result
library(ggplot2)
# plot number of injuries with the most harmful event type
injuries <- aggregate(INJURIES ~ EVTYPE, data=tidyNOAA, sum)
injuries <- injuries[order(-injuries$INJURIES), ][1:10, ]
injuries$EVTYPE <- factor(injuries$EVTYPE, levels = injuries$EVTYPE)

ggplot(injuries, aes(x = EVTYPE, y = INJURIES)) + 
    geom_bar(stat = "identity", fill = "red", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event Type") + ylab("Injuries") + ggtitle("Number of injuries by top 10 Weather Events")

Across the United States, which types of events have the greatest economic consequences ?

# import ggplot2 library to plot the result
library(ggplot2)
# plot number of damages with the most harmful event type
damages <- aggregate(PROPDMGNUM + CROPDMGNUM ~ EVTYPE, data=tidyNOAA, sum)
names(damages) = c("EVTYPE", "TOTALDAMAGE")
damages <- damages[order(-damages$TOTALDAMAGE), ][1:10, ]
damages$EVTYPE <- factor(damages$EVTYPE, levels = damages$EVTYPE)

ggplot(damages, aes(x = EVTYPE, y = TOTALDAMAGE)) + 
    geom_bar(stat = "identity", fill = "red", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event Type") + ylab("Damages ($)") + ggtitle("Property & Crop Damages by top 10 Weather Events")