Overview

Synopsis: Analysis of the NOAA Storm Database to analyze severe weather outcomes.

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.6.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

plot <- function(names, totals, columns, main, colors){
  colnames(totals) <- names
  
  par(las=2,mar=c(6,4,1,1))
  barplot(totals, col=colors,main=main,cex.names  = 0.6,cex.axis = 0.6)
  legend("topright", columns,fill=colors,bty = "n")
}

StormData <- read.csv("repdata_data_StormData.csv")
colnames(StormData)

##  [1] "STATE__"    "BGN_DATE"   "BGN_TIME"   "TIME_ZONE"  "COUNTY"    
##  [6] "COUNTYNAME" "STATE"      "EVTYPE"     "BGN_RANGE"  "BGN_AZI"   
## [11] "BGN_LOCATI" "END_DATE"   "END_TIME"   "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE"  "END_AZI"    "END_LOCATI" "LENGTH"     "WIDTH"     
## [21] "F"          "MAG"        "FATALITIES" "INJURIES"   "PROPDMG"   
## [26] "PROPDMGEXP" "CROPDMG"    "CROPDMGEXP" "WFO"        "STATEOFFIC"
## [31] "ZONENAMES"  "LATITUDE"   "LONGITUDE"  "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS"    "REFNUM"

Time to clean the data

event_types <- as.data.frame(table(StormData$EVTYPE))
event_types <- event_types[order(event_types$Var1), ]

Group events according to their labels.

StormData$EVTYPE <- as.character(StormData$EVTYPE)
StormData$EVTYPE[grepl("/|&|and", StormData$EVTYPE,ignore.case = TRUE)] <- "Multiple Event"
StormData$EVTYPE[grepl("volc", StormData$EVTYPE,ignore.case = TRUE)] <- "Volcano"
StormData$EVTYPE[grepl("wind|wnd", StormData$EVTYPE,ignore.case = TRUE)] <- "WIND"
StormData$EVTYPE[grepl("funnel|tornado", StormData$EVTYPE,ignore.case = TRUE)] <- "Tornado"
StormData$EVTYPE[grepl("glaze", StormData$EVTYPE,ignore.case = TRUE)] <- "Glaze"
StormData$EVTYPE[grepl("hail", StormData$EVTYPE,ignore.case = TRUE)] <- "Hail"
StormData$EVTYPE[grepl("dust", StormData$EVTYPE,ignore.case = TRUE)]  <- "DUST"
StormData$EVTYPE[grepl("flood", StormData$EVTYPE,ignore.case = TRUE)] <- "FLOOD"
StormData$EVTYPE[grepl("ic(e|y)", StormData$EVTYPE,ignore.case = TRUE)] <- "Ice"
StormData$EVTYPE[grepl("fire|smoke", StormData$EVTYPE,ignore.case = TRUE)] <- "FIRE"
StormData$EVTYPE[grepl("thunder", StormData$EVTYPE,ignore.case = TRUE)] <- "Thunder Storm"
StormData$EVTYPE[grepl("slide|eros", StormData$EVTYPE,ignore.case = TRUE)] <- "Erosion"
StormData$EVTYPE[grepl("rain", StormData$EVTYPE,ignore.case = TRUE)] <- "Rain"
StormData$EVTYPE[grepl("freez|cold|snow|chill|winter", StormData$EVTYPE,ignore.case = TRUE)] <- "Cold Weather"
StormData$EVTYPE[grepl("TROPICAL.STORM", StormData$EVTYPE,ignore.case = TRUE)] <- "TROPICAL STORM"
StormData$EVTYPE[grepl("heat", StormData$EVTYPE,ignore.case = TRUE)] <- "Heat"
StormData$EVTYPE[grepl("(hurri|opal)", StormData$EVTYPE,ignore.case = TRUE)] <- "Hurricane"

Specific Data for Question

health <- StormData[,(c(8,23:24))]
property<-StormData[,c(8,25:28)]

table(property$PROPDMGEXP)

## 
##             -      ?      +      0      1      2      3      4      5      6 
## 465934      1      8      5    216     25     13      4      4     28      4 
##      7      8      B      h      H      K      m      M 
##      5      1     40      1      6 424665      7  11330

table(property$CROPDMGEXP)

## 
##             ?      0      2      B      k      K      m      M 
## 618413      7     19      1      9     21 281832      1   1994

property$PROPDMGEXP<-factor(property$PROPDMGEXP,levels=c("H","K","M","B","h","m","O"))
property$PROPDMGEXP[is.na(property$PROPDMGEXP)] <- "O"

property$CROPDMGEXP<-factor(property$CROPDMGEXP,levels=c("K","M","B","k","m","O"))
property$CROPDMGEXP[is.na(property$CROPDMGEXP)] <- "O"

property$PROPDMGEXP <- as.character(property$PROPDMGEXP)
property$CROPDMGEXP <- as.character(property$CROPDMGEXP)

property$PROPDMGMLT <- 0
property$CROPDMGMLT <- 0

property$PROPDMGMLT[grepl("h", property$PROPDMGEXP,ignore.case = TRUE)]<-100
property$PROPDMGMLT[grepl("k", property$PROPDMGEXP,ignore.case = TRUE)]<-1000
property$PROPDMGMLT[grepl("m", property$PROPDMGEXP,ignore.case = TRUE)]<-1000000
property$PROPDMGMLT[grepl("b", property$PROPDMGEXP,ignore.case = TRUE)]<-1000000000
property$PROPDMGMLT[grepl("o", property$PROPDMGEXP,ignore.case = TRUE)]<-1

property$CROPDMGMLT[grepl("k", property$CROPDMGEXP,ignore.case = TRUE)]<-1000
property$CROPDMGMLT[grepl("m", property$CROPDMGEXP,ignore.case = TRUE)]<-1000000
property$CROPDMGMLT[grepl("b", property$CROPDMGEXP,ignore.case = TRUE)]<-1000000000
property$CROPDMGMLT[grepl("o", property$CROPDMGEXP,ignore.case = TRUE)]<-1


property$PROPDMG <- property$PROPDMG * property$PROPDMGMLT
property$CROPDMG <- property$CROPDMG * property$CROPDMGMLT
property$total <- property$PROPDMG + property$CROPDMG

Results

Population Health Question

Health Totals

health.totals <- aggregate(cbind(FATALITIES,INJURIES) ~ EVTYPE, data = health, sum, na.rm=TRUE)
health.totals$TOTAL <- health.totals$FATALITIES + health.totals$INJURIES
health.totals <- health.totals[order(-health.totals$TOTAL), ]
health.totals <- health.totals[1:25,]


plot(health.totals$EVTYPE,
     as.matrix(t(health.totals[,c(-1,-4)])),
     colors = c("dark blue","red"),
     columns = c("Fatalities","Injuries"),
     main = "Most Harmful Event Type in the United States")

Population Health Assessment

Although it looks like tornadoes are the worst, it is seen that the averages of these give a different perspective. Now, these are not seen as so destructive.

Economic Impact

economic.total <- aggregate(cbind(PROPDMG,CROPDMG, total) ~ EVTYPE, data = property, sum, na.rm=TRUE)
economic.crop <- economic.total[order(-economic.total$CROPDMG), ]
economic.crop <- economic.crop[1:25,]

economic.prop <- economic.total[order(-economic.total$PROPDMG), ]
economic.prop <- economic.prop[1:25,]

plot(economic.prop$EVTYPE,
     as.matrix(t(economic.prop[,c(-1,-3,-4)])),
     colors = c("dark blue","red"),
     columns = c("Property Damage"),
     main = "Economic Impact of Weather on Propery Damage")

plot(economic.crop$EVTYPE,
     as.matrix(t(economic.crop[,c(-1,-2,-4)])),
     colors = c("dark blue","red"),
     columns = c("Crop Damage"),
     main = "Economic Impact of Weather on Crop Damage")

Economic Impact Assessment

Flooding produces the most economic impact.

Storm Data Analysis

Chandrasekhar Gudipati