Reproducible research

Synopsis Based on recorded storm data from 1950 through 2011, this document tries to provide some insight into the effects of severe weather on the public. These kinds of answers can be beneficial to plan responses to severe weather events and to prepare contingency plans.

Data Processing Load the libraries we will need:

library(ggplot2)

Read in the data, mapping as many fields to numerical fields as possible.

data <- read.csv("repdataFdataFStormData.csv", header = TRUE, sep = ",", 
    colClasses = c("numeric", "character", "character", "character", "numeric", 
        "character", "character", "character", "numeric", "character", "character", 
        "character", "character", "numeric", "character", "character", "character", 
        "character", "numeric", "numeric", "character", "numeric", "numeric", 
        "numeric", "numeric", "character", "numeric", "character", "character", 
        "character", "character", "numeric", "numeric", "numeric", "numeric", 
        "character", "numeric"))

We first remove the data that does not contain information we are interested in by filtering out data that did not cause fatalities, injuries or damage.


smallData <- data[data$FATALITIES > 0 | data$INJURIES > 0 | data$PROPDMG > 0 | data$CROPDMG > 0, ]

In order to report on the data, we will add an additional column named category that contains the event category as used by the NCDC: - convection

extreme temperature flood marine tropical cyclon winter other This is also the order of importance with which we will treat the various events.

convection <- c("?.*LIGHTING|LIGHTN|LIGNTNING|TORNADO|TORNDAO|WIND|TSTM|THUNDER|WND|HAIL|TURBULENCE|GUSTNADO?.*")
temperature <- c("?.*COLD|HEAT|WARM|TEMPERATURE|THERMIA?.*")
flood <- c("?.*FLOOD|RISING|STREAM FLD?.*")
marine <- c("?.*COASTAL|TSUNAMI|CURRENT|SWELLS|TIDE|WAVE|SEAS|SURF|HIGH WATER?.*")
cyclones <- c("?.*CYCLONE|HURRICANE|TYPHOON?.*")
winter <- c("?.*WINT|ICE|AVALAN|SLEET|SNOW|FREEZ|BLIZZ|FROST|GLAZE|MIXED?.*")

smallData[grepl(winter, smallData$EVTYPE, ignore.case = TRUE), "category"] = "Winter"
smallData[grepl(cyclones, smallData$EVTYPE, ignore.case = TRUE), "category"] = "Tropical Cyclones"
smallData[grepl(marine, smallData$EVTYPE, ignore.case = TRUE), "category"] = "Marine"
smallData[grepl(flood, smallData$EVTYPE, ignore.case = TRUE), "category"] = "Flood"
smallData[grepl(temperature, smallData$EVTYPE, ignore.case = TRUE), "category"] = "Extreme Temperatures"
smallData[grepl(convection, smallData$EVTYPE, ignore.case = TRUE), "category"] = "Convection"
smallData[is.na(smallData$category), "category"] = "Other"

The PROPDMG and CROPDMG fields need some conversion before we can do math on them. Here is the code for that. We add two extra columns that contain the property and crop damage.


smallData$propertydamageEXP = 1
smallData[grepl("h|H", smallData$PROPDMGEXP, ignore.case = TRUE), "propertydamageEXP"] = 100
smallData[grepl("k|K", smallData$PROPDMGEXP, ignore.case = TRUE), "propertydamageEXP"] = 1000
smallData[grepl("m|M", smallData$PROPDMGEXP, ignore.case = TRUE), "propertydamageEXP"] = 1e+06
smallData[grepl("b|B", smallData$PROPDMGEXP, ignore.case = TRUE), "propertydamageEXP"] = 1e+09
smallData$propertydamage = smallData$PROPDMG * smallData$propertydamageEXP

smallData$cropdamageEXP = 1
smallData[grepl("k|K", smallData$CROPDMGEXP, ignore.case = TRUE), "cropdamageEXP"] = 1000
smallData[grepl("m|M", smallData$CROPDMGEXP, ignore.case = TRUE), "cropdamageEXP"] = 1e+06
smallData[grepl("b|B", smallData$CROPDMGEXP, ignore.case = TRUE), "cropdamageEXP"] = 1e+09
smallData$cropdamage = smallData$CROPDMG * smallData$cropdamageEXP

Results

Calculate a total of all fatalities and injuries, so that we can find what events have the highest number of incidents. The new column is called incidents.

smallData$incidents = smallData$FATALITIES + smallData$INJURIES

We create a new set with the aggregate of the incidents grouped by the event types.

incidentData <- aggregate(list(incidents = smallData$incidents), by = list(event = smallData$category), FUN = sum, na.rm = TRUE)

Here is the overview of the event categories with the number of incidents


library(ggplot2)
incidentData$event <- reorder(incidentData$event, -incidentData$incidents)
ggplot(incidentData, aes(y = incidents)) + theme(axis.text.x = element_text(angle = 90, 
    hjust = 1)) + geom_bar(aes(x = event), data = incidentData, stat = "identity") + 
    ggtitle("Fatalities and Injuries") + xlab("Event Category") + ylab("No. of Fatalities and Injuries")

Add a field with a total of all damage, both to property as well as to crop, so that we can find our what events cause the highest amount of damage. The new column is called damage. The column is in billions of dollars.

smallData$damage = ((smallData$propertydamage + smallData$cropdamage)/1e+09)

We create a new set with the aggregate of the damage grouped by the event types.

damageData <- aggregate(list(damage = smallData$damage), by = list(event = smallData$category),FUN = sum, na.rm = TRUE)

Here is the overview of the event categories with the amount of damage


library(ggplot2)
damageData$event <- reorder(damageData$event, -damageData$damage)
ggplot(damageData, aes(y = damage)) + theme(axis.text.x = element_text(angle = 90, 
    hjust = 1)) + geom_bar(aes(x = event), data = damageData, stat = "identity") + 
    ggtitle("Property and crop damage") + xlab("Event Category") + ylab("Amount of damage (billions of $)")

Reproducible research

vinit pandey

July 3, 2018