Exploring the United State NOAA storm database
Introduction
The project aims to explore the storm database of the US National Oceanic and Atmospheric Administration (NOAA).These data include some characteristics in terms of major storms and atmospheric phenomena that occur in the United States.Also the damages and severe consequences that occurred in terms of deaths, injuries and property damage.So in this project, we will focus on the reasons that have led to these types mentioned above.
Synopsis
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.This analysis present which types of events are most harmful with respect to population health and which have the greatest economic consequences.
first Load required libraries
# set warning=FALSE and message=FALSE to stop seeing messages and warning
library(readr)
library(tidyr)
library(magrittr)
library(dplyr)
library(ggplot2)
library(gridExtra)loading Data
Download it from internet first
link <- "http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url = link, destfile = "StormData")Unzip Data and Read file as table format
StormData <- read.csv(bzfile("StormData"),sep = ",",header=TRUE)Summary of Data
look at the data
summary(StormData)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295Data Processing
At once we will prepare data for 2 questions
#Select required variables only'effect of Events on health and economic' and drop the other.
StormDataSelected<-StormData[,c("BGN_DATE","EVTYPE","FATALITIES","INJURIES","PROPDMG","PROPDMGEXP","CROPDMG","CROPDMGEXP")]
#Subset Data '3columns ' that measures for Question 1
Health_harmful<-subset(StormDataSelected,!StormDataSelected$FATALITIES == 0 & !StormDataSelected$INJURIES ==
0, select = c(EVTYPE, FATALITIES, INJURIES))
#Subset Data '4columns ' that measures for Question 2
Economic_harmful<-subset(StormDataSelected, !StormDataSelected$PROPDMG == 0 & !StormDataSelected$CROPDMG ==
0, select = c(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))1-Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Data prpcessing 1. Now we will providing the required data to answer the question regarding the effect of events on health.
#for fatalities
Health_harmful_death<-Health_harmful %>%
group_by(EVTYPE) %>%
summarise(FATALITIES_COUNT=sum(FATALITIES))%>%
arrange(desc(FATALITIES_COUNT))
#select top 5
Health_harmful_death<-Health_harmful_death[1:5,]
#for Injury
Health_harmful_injur<-Health_harmful %>%
group_by(EVTYPE) %>%
summarise(INJURIES_COUNT=sum(INJURIES))%>%
arrange(desc(INJURIES_COUNT))
#select top 5
Health_harmful_injur<-Health_harmful_injur[1:5,]Result for first question
plot the count of fatalities and injuries
#use barplot from ggplot2 package
#plot top 5 events causing fatalities
Fatalities_plt<-ggplot(Health_harmful_death,
aes(x=EVTYPE,y=FATALITIES_COUNT,fill=EVTYPE))+
geom_bar(stat = "identity",show.legend = FALSE)+
xlab("Harmful Events")+ylab("num of Deaths")+
ggtitle("Top 5 Events Causing Fatalities")+
theme(axis.text.x = element_text(angle = 30))
#plot top 5 events causing Injuries
Injury_plt<-ggplot(Health_harmful_injur,
aes(x=EVTYPE,y=INJURIES_COUNT,fill=EVTYPE))+
geom_bar(stat="identity",show.legend = FALSE)+
xlab("Haemfull Events")+ylab("num of Injuries")+
ggtitle("Top 5 Events Causing Injuries")+
theme(axis.text.x = element_text(angle = 30))
#from "gridExtra" package we can divide two plots into 2col
grid.arrange(Fatalities_plt, Injury_plt, ncol = 2)
2-Across the United States, which types of events have the greatest economic consequences?
Data prpcessing 2. Now we will providing the required data to answer the question regarding the effect of events on Economic.
#to see count of each PROPDMGEXP and CROPDMGEXP levels
table(Economic_harmful$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5 6 7 8
## 5 0 0 0 4 0 0 1 0 2 0 0 0
## B h H K m M
## 15 0 0 14850 1 1364table(Economic_harmful$CROPDMGEXP)##
## ? 0 2 B k K m M
## 3 0 11 0 3 21 14911 1 1292#we hidding the result of data is being too big
#select only levels required (K,k,M,m,B)
selected<-c("K","k","M","m","B")
Economic_harmful[Economic_harmful$PROPDMGEXP %in% selected,]
Economic_harmful[Economic_harmful$CROPDMGEXP %in% selected,]
#we convert Values to numeric
#first replace pattern by numeric values by using gsub() function
Economic_harmful$PROPDMGEXP <- gsub("m", 1e+06, Economic_harmful$PROPDMGEXP, ignore.case = TRUE)
Economic_harmful$PROPDMGEXP <- gsub("k", 1000, Economic_harmful$PROPDMGEXP, ignore.case = TRUE)
Economic_harmful$PROPDMGEXP <- gsub("b", 1e+09, Economic_harmful$PROPDMGEXP, ignore.case = TRUE)
Economic_harmful$PROPDMGEXP <- as.numeric(Economic_harmful$PROPDMGEXP)
Economic_harmful$CROPDMGEXP <- gsub("m", 1e+06, Economic_harmful$CROPDMGEXP, ignore.case = TRUE)
Economic_harmful$CROPDMGEXP <- gsub("k", 1000, Economic_harmful$CROPDMGEXP, ignore.case = TRUE)
Economic_harmful$CROPDMGEXP <- gsub("b", 1e+09, Economic_harmful$CROPDMGEXP, ignore.case = TRUE)
#second convert them into numeric
Economic_harmful$CROPDMGEXP <- as.numeric(Economic_harmful$CROPDMGEXP)
Economic_harmful$PROPDMGEXP <- as.numeric(Economic_harmful$PROPDMGEXP)
#mutat column that contain the total damages
Economic_harmful$SumDam<-(Economic_harmful$PROPDMG * Economic_harmful$PROPDMGEXP)+
(Economic_harmful$CROPDMG * Economic_harmful$CROPDMGEXP)
#third count total damges by each event
Economic_harmful<-Economic_harmful %>%
group_by(EVTYPE) %>%
summarise(TotalDMG=sum(SumDam))%>%
arrange(desc(TotalDMG))
#select Top 5 only
Economic_harmful<-Economic_harmful[1:5,]Result for Second question
plot the count of Damages
ggplot(Economic_harmful,
aes(x=EVTYPE,y=TotalDMG,fill=EVTYPE))+
geom_bar(stat = "identity",show.legend = FALSE)+
xlab("Harmfull Events")+
ylab("sum of damages")+
ggtitle("Top 5 Events Causing Economics Damages")
Conclusion
After we make some analysis for this data to answer for both question ,we can found that the Tornado most harmfull affected to the population health , and the Flood have the greatest economic consequences.