Reproducible Research Project 2
Dan Nguyen
2/19/2020
Reproducible Research Project 2: NOAA Storm Data Analysis
Synopsis
This analysis concludes the Coursera Reproducible Research course, part of the Data Science Specialization. The goal of the assignment is to explore the NOAA Storm Database and explore the effects of severe weather events on both population and economy.
The database for this reproduciable research project covers the time period between 1950 and November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
The analysis aims to investigate which different types of sever weather events are most harmful on the populations health in respect of general injuries and fatalities. Further the economic consequences will be analyzed by exploring the financial damage done to both general property and agriculture (i.e. crops)
Data And Processing
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
Storm Data [47Mb] There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation. National Climatic Data Center Storm Events FAQ. The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
Assignment
The basic goal of this reproducible research project assignment is to explore the NOAA Storm Database and answer the following basic questions about severe weather events.
1.Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? 2.Across the United States, which types of events have the greatest economic consequences?
Process and loading the data
The data was downloaded from the above mentioned website and saved on local computer. Then it was loaded on the R using the following code.
filepath<-"C:/Users/dan_nguyen/Documents/repdata_data_StormData.csv"
storm<-read.csv(filepath)
head(storm)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6Extracting the required data for computation
This dataset consists of lot of information most of which is not required for our present study. So, here is the code to extract the required data for health and economic impact analysis against weather.
Extracting the required data for health and economic impact analysis against weather
event <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG",
"CROPDMGEXP")
data <- storm[event]Finding property damage
Property damage exponents for each level was listed out and assigned those values for the property exponent data. Invalid data was excluded by assigning the value as ‘0’. Then property damage value was calculated by multiplying the property damage and property exponent value.The code for this process was listed below # Finding the property damage exponent and levels
unique(data$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m MAssigning values for the property exponent data
data$PROPEXP[data$PROPDMGEXP == "K"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "M"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == ""] <- 1
data$PROPEXP[data$PROPDMGEXP == "B"] <- 1e+09
data$PROPEXP[data$PROPDMGEXP == "m"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "0"] <- 1
data$PROPEXP[data$PROPDMGEXP == "5"] <- 1e+05
data$PROPEXP[data$PROPDMGEXP == "6"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "4"] <- 10000
data$PROPEXP[data$PROPDMGEXP == "2"] <- 100
data$PROPEXP[data$PROPDMGEXP == "3"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "h"] <- 100
data$PROPEXP[data$PROPDMGEXP == "7"] <- 1e+07
data$PROPEXP[data$PROPDMGEXP == "H"] <- 100
data$PROPEXP[data$PROPDMGEXP == "1"] <- 10
data$PROPEXP[data$PROPDMGEXP == "8"] <- 1e+08*Assigning ‘0’ to invalid exponent data
data$PROPEXP[data$PROPDMGEXP == "+"] <- 0
data$PROPEXP[data$PROPDMGEXP == "-"] <- 0
data$PROPEXP[data$PROPDMGEXP == "?"] <- 0*Calculating the property damage value
data$PROPDMGVAL <- data$PROPDMG * data$PROPEXPFinding crop damage
Crop damage exponents for each level was listed out and assigned those values for the crop exponent data. Invalid data was excluded by assigning the value as ‘0’. Then crop damage value was calculated by multiplying the crop damage and crop exponent value.The code for this process was listed below
Exploring the crop exponent data
unique(data$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MAssigning values for the crop exponent data
data$CROPEXP[data$CROPDMGEXP == "M"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "K"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "m"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "B"] <- 1e+09
data$CROPEXP[data$CROPDMGEXP == "0"] <- 1
data$CROPEXP[data$CROPDMGEXP == "k"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "2"] <- 100
data$CROPEXP[data$CROPDMGEXP == ""] <- 1Assigning ‘0’ to invalid exponent data
data$CROPEXP[data$CROPDMGEXP == "?"] <- 0*calculating the crop damage value
data$CROPDMGVAL <- data$CROPDMG * data$CROPEXPFinding totals of each incident by event type.
It was observed that " most harmful to population health" events are fatalities and injuries.So,only those events with fatalities and injuries were selecetd.It was observed that " most harmful to econamic problem“” events are Property and crop damages.So,only those events with property and crop damage were selecetd.Then for each incident (Fatalities,Injuries, Property damage and Crop damage), the total values were estimated. Code for which is as follows. *Totalling the data by event
fatal <- aggregate(FATALITIES ~ EVTYPE, data, FUN = sum)
injury <- aggregate(INJURIES ~ EVTYPE, data, FUN = sum)
propdmg <- aggregate(PROPDMGVAL ~ EVTYPE, data, FUN = sum)
cropdmg <- aggregate(CROPDMGVAL ~ EVTYPE, data, FUN = sum)Plotting events with highest fatalities and highest injuries.
Highest fatalities and highest injuries for Top 8 events were calculated. For better understanding and comparision these values were plotted as follows. *Listing events with highest fatalities
fatal8 <- fatal[order(-fatal$FATALITIES), ][1:8, ]Listing events with highest injuries
injury8 <- injury[order(-injury$INJURIES), ][1:8, ]
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(fatal8$FATALITIES, las = 3, names.arg = fatal8$EVTYPE, main = "Events with Highest Fatalities",
ylab = "Number of fatalities", col = "light blue")
barplot(injury8$INJURIES, las = 3, names.arg = injury8$EVTYPE, main = "Events with Highest Injuries",
ylab = "Number of injuries", col = "light blue")
Property damage and highest crop damage. Highest Property damage and highest crop damage for Top 8 events were calculated. For better understanding and comparision these values were plotted as follows. *Finding events with highest property damage
propdmg8 <- propdmg[order(-propdmg$PROPDMGVAL), ][1:8, ]*Finding events with highest crop damage
cropdmg8 <- cropdmg[order(-cropdmg$CROPDMGVAL), ][1:8, ]
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg8$PROPDMGVAL/(10^9), las = 3, names.arg = propdmg8$EVTYPE,
main = "Events with Highest Property Damages", ylab = "Damage Cost ($ billions)",
col = "lightblue")
barplot(cropdmg8$CROPDMGVAL/(10^9), las = 3, names.arg = cropdmg8$EVTYPE,
main = "Events With Highest Crop Damages", ylab = "Damage Cost ($ billions)",
col = "lightblue")
The results of this indicated tornados caused the maximum number of fatalities and injuries. It was followed by Excessive Heat for fatalities and Thunderstorm wind for injuries. Floods caused the maximum property damage where as Drought caused the maximum crop damage. Second major events that caused the maximum damage was Hurricanes/Typhoos for property damage and Floods for crop damage.