ReproducibleProject2
Dan Nguyen
2/17/2020
Exploring the U.S. NOAA storm database
Introduction
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. 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 are the most dangerous weather event to the populations health. The second most dangerous event type is excessive heat. The economic impact of weather events was also analyzed. Flash floods and thunderstorm winds caused billions of dollars in property damages between 1950 and 2011. The largest damage to crops were caused by droughts, followed by floods and hailing.
Loading the data
Load required libraries
library("ggplot2")
library("gridExtra")
library("R.utils")
Begin to Read Data
if (!exists("stormData")) {
# Extract file if it is not already extracted
if (file.exists("repdata-data-StormData.csv.bz2")) {
if (!file.exists("repdata-data-StormData.csv")) {
bunzip2("repdata-data-StormData.csv.bz2", overwrite = F)
}
# Read data into the varirable called stormData
stormData <- read.csv("C:/ALib/training/coursera/reproducible/repdata-data-StormData.csv", sep = ",")
}
}
Display Summary of 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 : 569 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588294
Data Processing (for both questions)
Trim the data set to required columns only
stormEvent <- stormData[, 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
eventHealth <- subset(stormEvent, !stormEvent$FATALITIES == 0 & !stormEvent$INJURIES ==
0, select = c(EVTYPE, FATALITIES, INJURIES))
Select data for Property Damage and Crop Damage for Question 2
eventEconomic <- subset(stormEvent, !stormEvent$PROPDMG == 0 & !stormEvent$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 Processing
This will prepare requiured data to present most harmful events with respect to population health. # Create separate data set for Injury and Fatalities Fatalities eventHealth_Death <- aggregate(eventHealth\(FATALITIES, by = list(eventHealth\)EVTYPE), FUN = sum) # Give proper name for columns colnames(eventHealth_Death) <- c(“EVENTTYPE”, “FATALITIES”)
Injury
eventHealth_Inj <- aggregate(eventHealth\(INJURIES, by = list(eventHealth\)EVTYPE), FUN = sum) # Give column name colnames(eventHealth_Inj) <- c(“EVENTTYPE”, “INJURIES”)
Let’s reorder 2 dataset and filter top 5 events for both dataset
eventHealth_Death <- eventHealth_Death[order(eventHealth_Death$FATALITIES, decreasing = TRUE), ][1:5, ]
eventHealth_Inj <- eventHealth_Inj[order(eventHealth_Inj$INJURIES, decreasing = TRUE), ][1:5, ]
Results
Populate the top 5 major cause of Both fatalities and injuriees # plot top 5 events for fatalities and injuries
Plot Fatalities and store at Death_plot
Death_plot <- ggplot() + geom_bar(data = eventHealth_Death, aes(x = EVENTTYPE, y = FATALITIES, fill = interaction(FATALITIES, EVENTTYPE)), stat = “identity”, show.legend = F) + theme(axis.text.x = element_text(angle = 30, hjust = 1)) + xlab(“Harmful Events”) + ylab(“No. of fatailities”) + ggtitle(“Top 5 weather events causing fatalities”) + theme(axis.text.x = element_text(angle = 30, hjust = 1))
Plot injuries and store at variable Inj_plot
Inj_plot <- ggplot() + geom_bar(data = eventHealth_Inj, aes(x = EVENTTYPE, y = INJURIES, fill = interaction(INJURIES, EVENTTYPE)), stat = “identity”, show.legend = F) + theme(axis.text.x = element_text(angle = 30, hjust = 1)) + xlab(“Harmful Events”) + ylab(“No. of Injuries”) + ggtitle(“Top 5 weather events causing Injuries”) + theme(axis.text.x = element_text(angle = 30, hjust = 1))
Draw two plots generated above dividing space in two columns
grid.arrange(Death_plot, Inj_plot, ncol = 2)
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
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Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.