Major Weather Events in USA and Their Effects in Human Life and Economy

Sergio Vicente Simioni

Monday, April 20, 2015

  1. Synopsis
  2. Data Processing
    • Needed Library
    • Download CSV file
    • Data tidying
    • Data Consolidation
  3. Results
    • Data table print and bar graphs plot
  4. Summary

Synopsis

  • Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

  • This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage. Data

  • The data for this assignment come from the sources:
    • 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.

Data Processing

Loading the necessary Library from the R packages for the analisys

library(dplyr)
library(ggplot2)
library(knitr)

Loading the CSV file from the National Climatic Data center

repdata_data_StormData.csv <- read.csv("C:/Users/Sergio Simioni/Desktop/Data_Science/Reproducible_Research/Peer_Assessment_2/repdata_data_StormData.csv.bz2")
storm_damage <- select(repdata_data_StormData.csv, BGN_DATE,STATE, EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP,CROPDMG,CROPDMGEXP)

Data tidying

   * in order to eliminate the wrong inputs in PROPDMGEXP and CROPDMGEXP, it was created additional columns with zero and added values only for the letters H = Hundred, K = Thousands M = Millions and B  = Billions
storm_damage$PROP_US <- 0
storm_damage$CROP_US <- 0
storm_damage$damage  <- 0
storm_damage$health  <- 0
storm_damage$PROP_US <- ifelse(storm_damage$PROPDMGEXP =="H" | storm_damage$PROPDMGEXP =="h",
                               storm_damage$PROPDMG*0.0000001, storm_damage$PROP_US)
storm_damage$CROP_US <- ifelse(storm_damage$CROPDMGEXP =="H"|  storm_damage$CROPDMGEXP =="h",
                               storm_damage$CROPDMG*0.0000001, storm_damage$CROP_US)

storm_damage$PROP_US <- ifelse(storm_damage$PROPDMGEXP =="K"|  storm_damage$PROPDMGEXP =="k",
                               storm_damage$PROPDMG*0.000001,  storm_damage$PROP_US)
storm_damage$CROP_US <- ifelse(storm_damage$CROPDMGEXP =="K"|  storm_damage$CROPDMGEXP =="k",
                               storm_damage$CROPDMG*0.000001,  storm_damage$CROP_US)

storm_damage$PROP_US <- ifelse(storm_damage$PROPDMGEXP =="M"|  storm_damage$PROPDMGEXP =="m", 
                               storm_damage$PROPDMG*0.001,     storm_damage$PROP_US)
storm_damage$CROP_US <- ifelse(storm_damage$CROPDMGEXP =="M"|  storm_damage$CROPDMGEXP =="m", 
                               storm_damage$CROPDMG*0.001,     storm_damage$CROP_US)

storm_damage$PROP_US <- ifelse(storm_damage$PROPDMGEXP =="B"|  storm_damage$PROPDMGEXP =="b", 
                               storm_damage$PROPDMG*1,         storm_damage$PROP_US)
storm_damage$CROP_US <- ifelse(storm_damage$CROPDMGEXP =="B"|  storm_damage$CROPDMGEXP =="b",
                               storm_damage$CROPDMG*1,         storm_damage$CROP_US)

Data Consolidating: Fatalities/Injuries and Properties Damages/Crops Damages

storm_damage$health <- storm_damage$FATALITIES + storm_damage$INJURIES
storm_damage$damage <- storm_damage$PROP_US + storm_damage$CROP_US

Data Aggregating: Health/Type of Occurrence and Damage/Type of Occurrence.

health <- aggregate(storm_damage$health, by=list(storm_damage$EVTYPE), FUN = sum)
health <- arrange(health, desc(x))
health <- head(health,10)
health <- transform( health, Group.1 = reorder(Group.1, order(x, decreasing =TRUE)))
health <- select(health, Event_Type = Group.1, Number_of_Injuries= x)
storm_PDMG <- aggregate(storm_damage$damage, by=list(storm_damage$EVTYPE), FUN = sum)
storm_PDMG <- arrange(storm_PDMG, desc(x))
storm_PDMG <- head(storm_PDMG,10)
storm_PDMG <- transform( storm_PDMG, Group.1 = reorder(Group.1, order(x, decreasing =TRUE)))
storm_PDMG <- select(storm_PDMG, Event_Type = Group.1, Economic_Impact= x)

Results

Table of the 10 most important events for Health and Properties/Crops Damages.

head(health,10)
##           Event_Type Number_of_Injuries
## 1            TORNADO              96979
## 2     EXCESSIVE HEAT               8428
## 3          TSTM WIND               7461
## 4              FLOOD               7259
## 5          LIGHTNING               6046
## 6               HEAT               3037
## 7        FLASH FLOOD               2755
## 8          ICE STORM               2064
## 9  THUNDERSTORM WIND               1621
## 10      WINTER STORM               1527
head(storm_PDMG,10)
##           Event_Type Economic_Impact
## 1              FLOOD      150.319678
## 2  HURRICANE/TYPHOON       71.913713
## 3            TORNADO       57.352114
## 4        STORM SURGE       43.323541
## 5               HAIL       18.758222
## 6        FLASH FLOOD       17.562129
## 7            DROUGHT       15.018672
## 8          HURRICANE       14.610229
## 9        RIVER FLOOD       10.148404
## 10         ICE STORM        8.967041

Creating bar plots utilizing ggplot

g<- ggplot(health, aes(Event_Type, Number_of_Injuries)) + 
    labs(title="Total Fatalities & Injuries") +
    xlab("") + ylab("Number of injuries")
plot1<- g + geom_bar(colour="red", stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
g<- ggplot(storm_PDMG, aes(Event_Type, Economic_Impact)) + 
    labs(title="Total Properties & Crop Damages") +
    xlab("") + ylab("U$ Billions")
plot2<- g + geom_bar(colour="red", stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1))

Bar plot of the 10 most important events in United States

print(plot1)

print(plot2)

Summary

Tornadoes are the major weather event in US, impacting fatalities and injuries which sum 96.979 cases in these period of analisys, followed by Excessive Heat with 8.428 cases. Related to the Properties and Crops damages it is possible to see that Floods have the major economic impact U$ 150.3 Billions followed by Hurricanes/Typhoons with the expressive value of U$ 71.9 billions.







Revision April/21/2015