U.S. NOAA Analysis
Peter Hagen
May 19, 2016
Introduction
This project explores the U.S.NOAA storm data. The goal is to investigate two questions. First, what envinomental events cause the greatest population damage. Second, what environmental events cause the most economical damage.
Synopsis
The analysis revealed that tornados overwhelmingly cause the most population damage. On the other hand economic damage was a much closer race. Floods cause the most property damage while droughts caused the most damage to crops. When taking both propert and crop damage into account, Floods caused the most economic damage.
Data Processing
Reading, Investigating, Processing Data
First I load the data and set up the environment then do some initial investigation. Loading is done with read.csv. I then prepare a trimmed dataset using only the factors that will be required for the rest of the analysis.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(scales)
library(ggplot2)
library(ggthemes)
library(RColorBrewer)
library(reshape2)
library(dplyr)
storm <- read.csv(bzfile("repdata-data-StormData.csv.bz2"))
head(storm)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6str(storm)## '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 ""," Christiansburg",..: 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 ""," CANTON"," TULIA",..: 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","%SD",..: 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 "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(storm)## 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) :588295storm.trim <- storm[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
pop.damage <- subset(storm.trim, !storm.trim$FATALITIES == 0 & !storm.trim$INJURIES == 0, select = c(EVTYPE, FATALITIES, INJURIES))
pop.damage <- summarise(group_by(storm.trim, EVTYPE), fatalities = sum(FATALITIES), injuries = sum(INJURIES))Additional data processing
More data processing required for the econ plots. I am simply replacing the levels for the exponents with numerical values so they can be used.
storm.econ.trim <- storm[, c("EVTYPE","PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
levels(storm.trim$PROPDMGEXP)## [1] "" "-" "?" "+" "0" "1" "2" "3" "4" "5" "6" "7" "8" "B" "h" "H" "K"
## [18] "m" "M"storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "K"] <- 1000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "m"] <- 1000000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "M"] <- 1000000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "h"] <- 100
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "H"] <- 100
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "B"] <- 1000000000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "8"] <- 100000000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "7"] <- 10000000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "6"] <- 1000000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "5"] <- 100000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "4"] <- 10000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "3"] <- 1000
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "2"] <- 100
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "1"] <- 10
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "0"] <- 1
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "+"] <- 0
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "?"] <- 0
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == "-"] <- 0
storm.trim$propdmgexp[storm.trim$PROPDMGEXP == ""] <- 0
storm.trim$propdmg <- storm.trim$PROPDMG * storm.trim$propdmgexp
levels(storm.trim$CROPDMGEXP)## [1] "" "?" "0" "2" "B" "k" "K" "m" "M"storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "K"] <- 1000
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "m"] <- 1000000
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "M"] <- 1000000
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "B"] <- 1000000000
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "8"] <- 100000000
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "2"] <- 100
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "0"] <- 1
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == "?"] <- 0
storm.trim$cropdmgexp[storm.trim$CROPDMGEXP == ""] <- 0
storm.trim$cropdmg <- storm.trim$CROPDMG * storm.trim$cropdmgexp
econ.impact <- summarise(group_by(storm.trim, EVTYPE), total.prop.damage = sum(propdmg), total.crop.damage = sum(cropdmg))
econ.impact$total.impact <- econ.impact$total.prop.damage + econ.impact$total.crop.damageResults
1. Plotting weather activity that causes injuries or fatalities
fatalities.data <- arrange(pop.damage, desc(fatalities))[1:20, c(1, 2)]
ggplot() +
geom_bar(data = fatalities.data, aes(x= EVTYPE, y=fatalities),fill= "firebrick", stat = "identity") +
theme(axis.text.x = element_text(angle = 270,
hjust = 0.01,
vjust = 0.5,
size = 7)) +
ggtitle("Top 20 Environmental Events Causing Fatalities")+
xlab("Event Type") +
ylab("Number of Fatalities")
injuries.data <- arrange(pop.damage, desc(injuries))[1:20, c(1, 3)]
ggplot() +
geom_bar(data = injuries.data, aes(x= EVTYPE, y=injuries),fill= "firebrick", stat = "identity") +
theme(axis.text.x = element_text(angle = 270,
hjust = 0.01,
vjust = 0.5,
size = 7)) +
ggtitle("Top 20 Environmental Events Causing Injuries")+
xlab("Event Type") +
ylab("Number of Injuries")
Same plot but without tornados (for clarity)
pop.no.twist <- subset(storm.trim, !storm.trim$FATALITIES == 0 & !storm.trim$INJURIES == 0 & !storm.trim$EVTYPE == "TORNADO", select = c(EVTYPE, FATALITIES, INJURIES))
pop.no.twist <- summarise(group_by(pop.no.twist, EVTYPE), fatalities = sum(FATALITIES), injuries = sum(INJURIES))
fatalities.no.twist <- arrange(pop.no.twist, desc(fatalities))[1:20, c(1, 2)]
ggplot() +
geom_bar(data = fatalities.no.twist, aes(x= EVTYPE, y=fatalities),fill= "firebrick", stat = "identity") +
theme(axis.text.x = element_text(angle = 270,
hjust = 0.01,
vjust = 0.5,
size = 7)) +
ggtitle("Top 20 Environmental Events Causing Fatalities(Excluding Tornados)")+
xlab("Event Type") +
ylab("Number of Fatalities")
injuries.no.twist <- arrange(pop.no.twist, desc(injuries))[1:20, c(1, 3)]
ggplot() +
geom_bar(data = injuries.no.twist, aes(x= EVTYPE, y=injuries),fill= "firebrick", stat = "identity") +
theme(axis.text.x = element_text(angle = 270,
hjust = 0.01,
vjust = 0.5,
size = 7)) +
ggtitle("Top 20 Environmental Events Causing Injuries(Excluding Tornados)")+
xlab("Event Type") +
ylab("Number of Injuries")
2. Economic Data Plots
I plot the top 10 environmental events that cause property and crop damage. I then make a concluding plot to take them both into account.
econ.impact.prop <- arrange(econ.impact, desc(total.prop.damage))[1:10, c(1, 2)]
ggplot() +
geom_bar(data = econ.impact.prop, aes(x= EVTYPE, y=total.prop.damage),fill= "firebrick", stat = "identity") +
theme(axis.text.x = element_text(angle = 270,
hjust = 0.01,
vjust = 0.5,
size = 7)) +
ggtitle("Top 10 Environmental Events Causing Property Damage")+
xlab("Event Type") +
ylab("Property Damage (USD)")
econ.impact.crop <- arrange(econ.impact, desc(total.crop.damage))[1:10, c(1, 3)]
ggplot() +
geom_bar(data = econ.impact.crop, aes(x= EVTYPE, y=total.crop.damage),fill= "firebrick", stat = "identity") +
theme(axis.text.x = element_text(angle = 270,
hjust = 0.01,
vjust = 0.5,
size = 7)) +
ggtitle("Top 10 Environmental Events Causing Crop Damage")+
xlab("Event Type") +
ylab("Crop Damage (USD)")
econ.impact.total <- arrange(econ.impact, desc(total.impact))[1:10, c(1 :4)]
econ.melt <- melt(econ.impact.total)## Using EVTYPE as id variablesecon.melt <- subset(econ.melt, !econ.melt$variable == "total.impact", select = c(EVTYPE, variable, value))
ggplot() +
geom_bar(data = econ.melt, aes(x= EVTYPE, y=value, fill= variable), stat = "identity") +
theme(axis.text.x = element_text(angle = 270,
hjust = 0.01,
vjust = 0.5,
size = 10)) +
ggtitle("Top 10 Environmental Events Total Economical Damage")+
xlab("Event Type") +
ylab("Damage (USD)")
Summary
Tornados cause the most population damage by far. Floods cause the most economic damage but it is a much closer race. Droughts cause the most damage directly to crops.