Harmful events and consequences to US Population Health and Economy
Synopsis:
This report explores the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database, which tracks characteristics of major storms and weather events in the United States, including event occuring time and location, estimates of fatalities, injuries, and property damages.
The purpose of this report is to address the following questions: 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?
The conclusion summarizes 5 types of events for these two questions, respectively.
Data Processing
I read the csv file from my working directory
storm <- read.csv("storm.csv")The below is a summary of the data,which has 902297 observations.
str(storm)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : Factor w/ 436774 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...Analysis:
This documents investigate two questions:
1.Across the United States, which types of events (as indicated in the ‘EVTYPE’ variable) are most harmful with respect to population health?
I investigate the events which has outstanding value in variable ‘FATALITIES’ and ‘INJURIES’
2.Across the United States, which types of events have the greatest economic consequences? I investigate the events which has outstanding value in variable ‘PROPDMG’ and ‘CROPDMG’
Results:
For question 1: List top 6 of Injuries given by Event.
storm_injuries <- aggregate(INJURIES ~ EVTYPE, data = storm, FUN = sum)
storm_injuries <- storm_injuries[order(storm_injuries$INJURIES, decreasing = TRUE), ]
# Top 6 most harmful causes of injuries
top_storm_injuries <- storm_injuries[1:6, ]
print(top_storm_injuries)## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100List top 6 Fatalies given by Event.
storm_fatalities <- aggregate(FATALITIES ~ EVTYPE, data = storm, FUN = sum)
storm_fatalities <- storm_fatalities[order(storm_fatalities$FATALITIES, decreasing = TRUE), ]
# Top 6 most harmful causes of fatalities
top_storm_fatalities <- storm_fatalities[1:6, ]
print(top_storm_fatalities)## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504Chart “Top 6 Types of Events Causing Injuries Across the U.S”
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.1.3ggplot(top_storm_injuries[1:6, ], aes(EVTYPE, INJURIES)) + geom_bar(stat = "identity") + ylab("Fatalities") + xlab("Event Type") + ggtitle("Top 6 Types of Events Causing Injuries Across the U.S")
Chart “Top Ten Types of Events Causing Death Across the U.S”
ggplot(top_storm_fatalities[1:6, ], aes(EVTYPE, FATALITIES)) + geom_bar(stat = "identity") + ylab("Injuries") + xlab("Event Type") + ggtitle("Top 6 Types of Events Causing Death Across the U.S")
For question 2: List top 6 of Injuries given by Event.
storm_PROPDMG <- aggregate(PROPDMG ~ EVTYPE, data = storm, FUN = sum)
storm_PROPDMG <- storm_PROPDMG[order(storm_PROPDMG$PROPDMG, decreasing = TRUE), ]
# Top 10 most harmful causes of PROPDMG
top_storm_PROPDMG <- storm_PROPDMG[1:6, ]
print(top_storm_PROPDMG)## EVTYPE PROPDMG
## 834 TORNADO 3212258.2
## 153 FLASH FLOOD 1420124.6
## 856 TSTM WIND 1335965.6
## 170 FLOOD 899938.5
## 760 THUNDERSTORM WIND 876844.2
## 244 HAIL 688693.4Chart “Top 6 Types of Events Causing PROPDMG Across the U.S”
library(ggplot2)
ggplot(top_storm_PROPDMG[1:6, ], aes(EVTYPE, PROPDMG)) + geom_bar(stat = "identity") + ylab("PROPDMG") + xlab("Event Type") + ggtitle("Top 6 Types of Events Causing PROPDMG Across the U.S")