Final Assignment - Reproducible Research
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.
INTRODUCTION
Here we have data which we need to makes sense of. Before any step first we should read the data in a suitable format. Later we will subset the data to take only limited data as maybe not the entire data will be useful to us ahead #### 1. Reading and loading the data
if (!file.exists("data.csv"))
{
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",'data.csv',mode="wb",method="curl")
}
if(!exists("rdata"))
{
rrdata <- read.csv("data.csv")
}
str(rrdata)## '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 ...Data Processing
Proecessing the raw data to make it into a meaningful data which can be used for processing. It is an important step because this is the step which later makes visualization of data easier.
2. Subsetting the data
Taking limited columns and making sense of PROPDMGEXP & CROPDMGEXP data by assigning it value as given in the document.
rrdata <- rrdata[,c(2,8,23,24,25,26,27,28)]
rrdata$PROPDMGVALUE <- 1
rrdata$PROPDMGVALUE[rrdata$PROPDMGEXP =="H"] <- 100
rrdata$PROPDMGVALUE[rrdata$PROPDMGEXP =="K"] <- 1000
rrdata$PROPDMGVALUE[rrdata$PROPDMGEXP =="M"] <- 1000000
rrdata$PROPDMGVALUE[rrdata$PROPDMGEXP =="B"] <- 1000000000
rrdata$CROPDMGVALUE <- 1
rrdata$CROPDMGVALUE[rrdata$CROPDMGEXP =="H"] <- 100
rrdata$CROPDMGVALUE[rrdata$CROPDMGEXP =="K"] <- 1000
rrdata$CROPDMGVALUE[rrdata$CROPDMGEXP =="M"] <- 1000000
rrdata$CROPDMGVALUE[rrdata$CROPDMGEXP =="B"] <- 1000000000
library(plyr)
library(dplyr)
library(ggplot2)
final_data <- ddply(.data = rrdata, .variables = .(EVTYPE), fatalities = sum(FATALITIES), injuries = sum(INJURIES), property_damage = sum(PROPDMG * PROPDMGVALUE), crop_damage = sum(CROPDMG * CROPDMGVALUE), summarize)3. Sorting the Data
Sorting the data according to two condition for which we have to answer later.
final_data_propdamage <- arrange(final_data, desc(property_damage+crop_damage))
final_data_lifedamage <- arrange(final_data, desc(injuries+fatalities))RESULTS
THe final results as presented using a bar graph below: #### TOP 10 Types of events having the greatest economic consequences !!
ggplot(data=head(final_data_propdamage, n=10), aes(x=factor(EVTYPE),property_damage, fill= EVTYPE))+geom_bar(stat="identity", width=0.5 )+ labs(x="Types of Events", y="Property Damage(in $)", title ="TOP 10 Types of events having the greatest economic consequences !!")
TOP 10 Types of events which are most harmful with respect to population health !!
ggplot(data=head(final_data_lifedamage, n=10), aes(x=factor(EVTYPE),fatalities, fill= EVTYPE))+geom_bar(stat="identity", width=0.5 )+labs(x="Types of Events", y="Fatalities", title ="TOP 10 Types of events which are most harmful with respect to population health !!")