NOAA Storm Database Analysis
Baiaman Urmatbek
February 29, 2016
Synopsis
The goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. This data analysis address’ the following two 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?
The orginal data can be found from here: Storm Data
Data Processing
# downloads and saves NOAA Storm Data in a variable called 'initial.data'
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, destfile = "repdata%2Fdata%2FStormData.csv.bz2")
# creates a master data
initial.data <- read.csv(bzfile("repdata%2Fdata%2FStormData.csv.bz2"))See the summary of the data
summary(initial.data)## 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) :588295Results
Question #1
Across the United States, which types of events (as indicated in the 𝙴𝚅𝚃𝚈𝙿𝙴 variable) are most harmful with respect to population health? To solve this problem we will need the following columns from our data: * EVTYPE: The type of the environmental disasters (Tornado, High Wind, Snow, etc.) * FATALITIES: Both direct and indirect fatalities caused by the environmental disaster. * INJURIES: Both direct and indirect injuries caused by the environmental disaster.
# subsets the initial data
health <- initial.data[,c("EVTYPE", "FATALITIES", "INJURIES")]
# shows unique disasters
length(unique(health[,"EVTYPE"]))## [1] 985# takes sum of each disaster type for both 'Injuries' and 'Fatalities' and adds them
agg.health <- aggregate(health$FATALITIES + health$INJURIES ~ health$EVTYPE , FUN=sum, na.rm=TRUE)# sorts from the highest to the lowest and saves top five in "TopH"
sorted.health <- agg.health[order(-agg.health$`health$FATALITIES + health$INJURIES`),]
TopH <- head(sorted.health)Answer to Question #1
NOAA Storm Data shows us that ‘TORNADO’ is the most harmful environmental disaster in United states with respect to population health. See the graph below to find the full information.
barplot (height = TopH$`health$FATALITIES + health$INJURIES`,
names.arg = TopH$`health$EVTYPE`, las = 2, cex.names= 0.7,
col = rainbow (30, start=.1, end=0.5))
title ("Top 5 Disasters: Injuries + Fatalities", line=-4)
Question #2
Across the United States, which types of events have the greatest economic consequences? To answer this question we will need all the columns which are related to economics the following columns from the master data: * EVTYPE: The type of the environmental disasters (Tornado, High Wind, Snow, etc.) * PROPDMG: The total property damage rounded to three significant digits used in conjunction with PROPDMGEXP to determine the appropriate size multiplier. * PROPDMGEXP: A letter code indicating the magnitude of the PROPDMG dollar amount {“K”,“M”,“B”} for “thousands”, “millions” and “billions” respectively. * CROPDMG: The total crop damage rounded to three significant digits used in conjunction with CROPDMGEXP to determine the the appropriate size multiplier. * CROPDMGEXP: A letter code indicating the magnitude of the CROPDMG dollar amount {“K”,“M”,“B”} for “thousands”, “millions” and “billions” respectively.
# subsets the initial dataset to choose only the columns we need to answer the second question
economic <- initial.data[,c("EVTYPE", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
# how does the dataset look like?
head(economic)## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 25.0 K 0
## 2 TORNADO 2.5 K 0
## 3 TORNADO 25.0 K 0
## 4 TORNADO 2.5 K 0
## 5 TORNADO 2.5 K 0
## 6 TORNADO 2.5 K 0# multiplies PROPDMG by PROPDMGEXP
economic[economic$PROPDMGEXP == "K", ]$PROPDMG <- economic[economic$PROPDMGEXP == "K", ]$PROPDMG * 1000
economic[economic$PROPDMGEXP == "K", ]$PROPDMG <- economic[economic$PROPDMGEXP == "K", ]$PROPDMG * 1000
economic[economic$PROPDMGEXP == "M", ]$PROPDMG <- economic[economic$PROPDMGEXP == "M", ]$PROPDMG * 1000000
economic[economic$PROPDMGEXP == "m", ]$PROPDMG <- economic[economic$PROPDMGEXP == "m", ]$PROPDMG * 1000000
economic[economic$PROPDMGEXP == "B", ]$PROPDMG <- economic[economic$PROPDMGEXP == "B", ]$PROPDMG * 1000000000
# multiplies CROPDMG by CROPDMGEXP
economic[economic$CROPDMGEXP == "K", ]$CROPDMG <- economic[economic$CROPDMGEXP == "K", ]$CROPDMG * 1000
economic[economic$CROPDMGEXP == "k", ]$CROPDMG <- economic[economic$CROPDMGEXP == "k", ]$CROPDMG * 1000
economic[economic$CROPDMGEXP == "M", ]$CROPDMG <- economic[economic$CROPDMGEXP == "M", ]$CROPDMG * 1000000
economic[economic$CROPDMGEXP == "m", ]$CROPDMG <- economic[economic$CROPDMGEXP == "m", ]$CROPDMG * 1000000
economic[economic$CROPDMGEXP == "B", ]$CROPDMG <- economic[economic$CROPDMGEXP == "B", ]$CROPDMG * 1000000000# aggregates cost of damages from crops and properties based on type of disasters
agg.economic <- aggregate(economic$CROPDMG + economic$PROPDMG ~ economic$EVTYPE , FUN=sum, na.rm=TRUE)# puts the cost of damages in an descending order
sorted.economic <- agg.economic[order(-agg.economic$`economic$CROPDMG + economic$PROPDMG`),]
# saves top five in a varibale called "TopE"
TopE <- head(sorted.economic)Answer to Question #2
Across the United States, ‘Flood’ has the greatest economic consequences followed by ‘HURRICANE/TYPHOON’ and ‘STORM SURGE’. See the graph below to find the full information.
barplot (height = TopE$`economic$CROPDMG + economic$PROPDMG`,
names.arg = TopE$`economic$EVTYPE`, las = 2, cex.names= 0.7,
col = rainbow (30, start=.1, end=0.5))
title ("Top 5 Economical Damages: Crop + Property", line=-4)