Reproducible Research Course Project 2 Exploring the US NOAA storm database
Ashish Veera
9/23/2017
Introduction
This course project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm DB. This DB tracks characteristics of major storms and weather events in the US, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage. The analysis below will analyze the major storm events causing injuries and fatalities. Similarly, we will also examine the major Storm Event causing highest property damage.
Synopsis
The analysis on the storm event database revealed that tornadoes have the highest impact to the populations health. The second most dangerous event type is excessive heat. The economic impact of weather events was also analyzed. Floods had the highest impact in terms of economic consequences.
Loading required libraries
library("ggplot2")
library("R.utils")
library("gridExtra")Loading data
if (!file.exists("data.csv.bz2"))
{
data<- download.file('https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2', 'data.csv.bz2')
}
bunzip2("data.csv.bz2", overwrite = T)
stormData1 <- read.csv("data.csv")Summary of data
summary(stormData1)## 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 Preprocessing
# Trim the data set to required columns only
stormData2 <- stormData1[, c("BGN_DATE", "EVTYPE", "FATALITIES", "INJURIES",
"PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
# Create subset for Question 1 and Question 2
# Select data for Fatalities and injuries for Question 1
popHealth<- subset(stormData2, !stormData2$FATALITIES == 0 & !stormData2$INJURIES ==
0, select = c(EVTYPE, FATALITIES, INJURIES))
# Select data for Property Damage and Crop Damage for Question 2
ecoCons <- subset(stormData2, !stormData2$PROPDMG == 0 & !stormData2$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?
fatalities <- aggregate(popHealth$FATALITIES, by = list(popHealth$EVTYPE), FUN = sum)
colnames(fatalities) <- c("EVENTTYPE", "FATALITIES")
injury <- aggregate(popHealth$INJURIES, by = list(popHealth$EVTYPE), FUN = sum)
colnames(injury) <- c("EVENTTYPE", "INJURY")
fatalities <- fatalities[order(fatalities$FATALITIES, decreasing = TRUE),][1:5,]
injury <- injury[order(injury$INJURY, decreasing = TRUE),][1:5,]Results for question 1
fatalities_plot <- ggplot() + geom_bar(data = fatalities, aes(x= EVENTTYPE, y= FATALITIES, fill = interaction(FATALITIES, EVENTTYPE)), stat = "identity", show.legend = F)+
xlab("Events") + ylab("Number of Fatalities") + ggtitle("Top 5 events causing fatalities")
injury_plot <- ggplot() + geom_bar(data = injury, aes(x= EVENTTYPE, y= INJURY, fill = interaction(INJURY, EVENTTYPE)), stat = "identity", show.legend = F) +
xlab("Events") + ylab("Number of Injuries") + ggtitle("Top 5 events causing Injuries")
grid.arrange(fatalities_plot, injury_plot, nrow = 2)
As observed in the above graph, the population health is most affected by Tornadoes. The total number of Fatalities due to Tornados are 5227 and the total number of Injuries due to Tornadoes are 60187.
2.Across the United States, which types of events have the greatest economic consequences?
Data preprocessing
# select required entries for economy
ecoCons <- subset(ecoCons, ecoCons$PROPDMGEXP == "K" | ecoCons$PROPDMGEXP ==
"k" | ecoCons$PROPDMGEXP == "M" | ecoCons$PROPDMGEXP == "m" |
ecoCons$PROPDMGEXP == "B" | ecoCons$PROPDMGEXP == "b")
ecoCons <- subset(ecoCons, ecoCons$CROPDMGEXP == "K" | ecoCons$CROPDMGEXP ==
"k" | ecoCons$CROPDMGEXP == "M" | ecoCons$CROPDMGEXP == "m" |
ecoCons$CROPDMGEXP == "B" | ecoCons$CROPDMGEXP == "b")
# Convert economic values to number
ecoCons$PROPDMGEXP <- gsub("m", 1e+06, ecoCons$PROPDMGEXP, ignore.case = TRUE)
ecoCons$PROPDMGEXP <- gsub("k", 1000, ecoCons$PROPDMGEXP, ignore.case = TRUE)
ecoCons$PROPDMGEXP <- gsub("b", 1e+09, ecoCons$PROPDMGEXP, ignore.case = TRUE)
ecoCons$PROPDMGEXP <- as.numeric(ecoCons$PROPDMGEXP)
ecoCons$CROPDMGEXP <- gsub("m", 1e+06, ecoCons$CROPDMGEXP, ignore.case = TRUE)
ecoCons$CROPDMGEXP <- gsub("k", 1000, ecoCons$CROPDMGEXP, ignore.case = TRUE)
ecoCons$CROPDMGEXP <- gsub("b", 1e+09, ecoCons$CROPDMGEXP, ignore.case = TRUE)
ecoCons$CROPDMGEXP <- as.numeric(ecoCons$CROPDMGEXP)
ecoCons$PROPDMGEXP <- as.numeric(ecoCons$PROPDMGEXP)
# then sum the damages by each event type
ecoCons$TOTALDMG <- (ecoCons$CROPDMG * ecoCons$CROPDMGEXP) +
(ecoCons$PROPDMG * ecoCons$PROPDMGEXP)
ecoCons <- aggregate(ecoCons$TOTALDMG, by = list(ecoCons$EVTYPE),
FUN = sum)
colnames(ecoCons) <- c("EVTYPE", "TOTALDMG")
# Rank the event type by highest damage cost and take top 5 columns
ecoCons<- ecoCons[order(ecoCons$TOTALDMG, decreasing = TRUE), ] [1:5,]Results for question 2
ggplot() + geom_bar(data = ecoCons, aes(x = EVTYPE, y = TOTALDMG, fill = interaction(TOTALDMG,
EVTYPE)), stat = "identity", show.legend = F) + theme(axis.text.x = element_text(angle = 30,
hjust = 1)) + xlab("Event Type") + ylab("Total Damage")
As observed from the above graph, Flood has the highest impact in terms of economic consequences
Summary
From the results of the above analysis, we can conclude that Tornado has the most impact in terms of popultion health and Flood has the most impact in terms of economic consequences