Course Project 2_Peer Graded
Nemil Panchamia
17/08/2020
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.
ASSIGNMENT
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.
QUESTIONS
Your data analysis must address the following questions:
- Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
SYNOPSIS
This assignment 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. We assed two questions:1) which types of events are most harmful with respect to population health and 2) hich types of events have the greatest economic consequences. We first process, summarizes, transform and create new data variables when necessary to respond the questions above. In order to answer the questions, we subset the dataset by selecting key variables. The selected variables include: EVent type (EVTYPE), FATALITIES, INJURIES and cost related varibles including propery and crop damages with their respecive data unit (i.e millions, thousands, billions). The result show that accross the USA, Tornado is the most fatal events and main cause of injuries. On the other hand, Flood related events lead others in terms of major economic consequences.
DATA PROCESSING
The data is downloaded from the Storm Data course website and saved as ‘StormData.csv’
DOWNLOADING OF DATA
if(!file.exists("StormData.csv.bz2")) {
URL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(URL, destfile="StormData.csv.bz2")
}LOADING DATA
Aftre the data are downloaded and saved in my working directory, the next step is to load data into R.
myData <- read.csv("StormData.csv.bz2")
head (myData, 3)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3EXPLORING THE DATA
str(myData)## '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/ 436781 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 ...TRANSFORMING DATE
myData$BGN_DATE <- as.Date(myData$BGN_DATE, "%m/%d/%Y %H:%M:%S")
myData$END_DATE <- as.Date(myData$END_DATE, "%m/%d/%Y %H:%M:%S")RESULT
1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Let us subset a database that would respond to the above questions. In the dataset, the variables that would indicate harmful effects of events with respect to population health are: FATALITIES and INJURIES.
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, unionsubData <- myData %>%
select (EVTYPE, FATALITIES, INJURIES) %>%
group_by(EVTYPE) %>%
summarize(TotalFatalities = sum(FATALITIES),TotalInjuries = sum(INJURIES), .groups = 'drop') We then subset fatalities and injuries separately. First let’s start with the worst events.
Fatalities <- subData %>% select(EVTYPE, TotalFatalities) %>% arrange(desc(TotalFatalities))
topFatalities <- head(Fatalities,10)
topFatalities## # A tibble: 10 x 2
## EVTYPE TotalFatalities
## <fct> <dbl>
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224Second, we take the top five events in terms of number of injuries to people.
Injuries <- subData %>% select(EVTYPE, TotalInjuries) %>% arrange(desc(TotalInjuries))
topInjuries <- head(Injuries,10)
topInjuries## # A tibble: 10 x 2
## EVTYPE TotalInjuries
## <fct> <dbl>
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361THE PLOT
par(mfrow = c(1,2))
#barplot of the top ten events to cause injuries in USA
with(topInjuries, barplot(TotalInjuries,
main = "Top ten leading events of injuries in USA",
ylab = "# of injuries in thousands",
names = topInjuries$EVTYPE,
col = "pink",
border = "pink",
las = 2,
cex.names = 0.6,
cex.main = 1,
cex.axis = 0.8,
cex.lab = 0.8))
#bar plot of the top ten fatal events in USA
with(topFatalities, barplot(TotalFatalities,
main = "Top ten fatal events in USA",
ylab = "# of deaths in thousands",
names = topFatalities$EVTYPE,
col = "black",
las = 2,
cex.names = .6,
cex.main = 1,
cex.axis = 0.8,
cex.lab = 0.8))
2. Across the United States, which types of events have the greatest economic consequences?
We first find out the variables that would show the economic impact of event. The PROPDMG, CROPDMG and their unit as reported in PROPDMGEXP and CROPDMGEXP, respectively.
So, we subset the datasets with respect to the identified variables
subEconomic <- myData %>% select(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)We then create a new variable, TotalDamage that add together the damages on property and crop. We also sort the TotalDamage column to get events with the greatest economic consequences.
#Let's first group the dataset by events and sort them to get the event with greatest economic consequences
grouped_EcoDmg <- subEconomic %>% group_by(EVTYPE) %>% mutate(Total = PROPDMG + CROPDMG) %>%
summarise(TotalDamage= sum(Total), .groups = "drop") %>%
arrange(desc(TotalDamage))
# Let us just collect top 10 events.
top_economicDamage <- head(grouped_EcoDmg, 10)
top_economicDamage## # A tibble: 10 x 2
## EVTYPE TotalDamage
## <fct> <dbl>
## 1 TORNADO 3312277.
## 2 FLASH FLOOD 1599325.
## 3 TSTM WIND 1445168.
## 4 HAIL 1268290.
## 5 FLOOD 1067976.
## 6 THUNDERSTORM WIND 943636.
## 7 LIGHTNING 606932.
## 8 THUNDERSTORM WINDS 464978.
## 9 HIGH WIND 342015.
## 10 WINTER STORM 134700.We then plot the bar chart of the above dataset
par(mai = c(0.9, 2, 0.5, 0.8))
with(top_economicDamage, barplot(TotalDamage/1000000,
main = "Top 10 events with the greatest economic consequences",
ylab = "$ million",
names.arg = EVTYPE,
las = 2,
cex.names = 0.6,
col = "blue",
border = "blue"))