Synopsis

This is a data analysis for the NOAA storm data from 1950-2011 presented as a document. The storm affected arees has been considered and I would try to analyse the effects . It focuses on two points:

Population casualties
Economic damage.

I will do some simple processing work on the data, and use the plot to present my result.

Introduction

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 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[47 mb].

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.

Data PRocessing

set the Working Directory.

library(dplyr)

## 
## 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

library(ggplot2)
library(knitr)

setwd("D:/DataScience")
repdata_data_StormData.csv <- read.csv("StormData.csv.bz2")
storm_damage <- select(repdata_data_StormData.csv, BGN_DATE,STATE, EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP,CROPDMG,CROPDMGEXP)

download the file and read into R.

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 Look at the data.

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

Transfer the EVTYPE,PROPDMGEXP and CROPDMGEXP to uppercase for aggregation. Transfer the BGN_DATE to date time format for future work.

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.

Sum the FATALITIES and INJURIES by EVTYPE, and get the top 10 Harmful types.

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)

Sum the PROPDMG by EVTYPE and PROPDMGEXP. Then calculate real property damage by accounting PROPDMGEXP. Last step, sum the new property damage data by EVTYPE ## Results

Print the head of the health frame.

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))

print(plot1)

NOAA Storm Data Analysis

Abhishek Sharma

21 September 2016