Impact of weather events on public health and economic damage in the US
Enrique Estrada
May 29, 2018
Assignment
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events.
Synopsis
The current report focuses on damage to the public health (injuries and fatalities), and the economy of the affected communities. Based On the analysis of this report, preventative measures aimed at reducing casualties and damage during tornadoes and floods will have the greatest economic and health impacts.
Load libraries & Session Information
library(ggplot2) # for plots
library(R.utils) # for bunzip2
library(gridExtra) # for arranging grids
sessionInfo()## R version 3.4.1 (2017-06-30)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17134)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] gridExtra_2.3 R.utils_2.6.0 R.oo_1.22.0 R.methodsS3_1.7.1
## [5] ggplot2_2.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.14 knitr_1.20 magrittr_1.5 munsell_0.4.3
## [5] colorspace_1.3-2 rlang_0.1.6 stringr_1.2.0 plyr_1.8.4
## [9] tools_3.4.1 grid_3.4.1 gtable_0.2.0 htmltools_0.3.6
## [13] yaml_2.1.16 lazyeval_0.2.1 rprojroot_1.3-2 digest_0.6.13
## [17] tibble_1.4.1 evaluate_0.10.1 rmarkdown_1.9 stringi_1.1.6
## [21] compiler_3.4.1 pillar_1.0.1 scales_0.5.0 backports_1.1.2Retrieve Data
Data was retrieved via Coursera’s Cloudfont Link and unzipped into the working directory. Please reference the code below to download and unzip the data. The file will download and save to current working directory.
Data Download
## Data Download
URL <- "http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
destfile <- "StormData.csv.bz2"
download.file(URL, destfile)
stormData <- read.csv(bzfile("StormData.csv.bz2"), strip.white = TRUE)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 : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295In context, the columns needed for the process are:
EVTYPE: Event typeFATALITIES: Number of fatalitiesINJURIES: Number of injuriesPROPDMG: Property damage (numeric value)PROPDMGEXP: Property damage (exponential indicator affecting the numeric value)CROPDMG: Crop damage (numeric value)CROPDMGEXP: Crop damage (exponential indicator affecting the numeric value)
Data Processing
# 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?
# 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 10 events for both dataset
eventHealth_Death <- eventHealth_Death[order(eventHealth_Death$FATALITIES, decreasing = TRUE),
][1:10, ]
eventHealth_Inj <- eventHealth_Inj[order(eventHealth_Inj$INJURIES, decreasing = TRUE),
][1:10, ]Results
# plot top 10 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 10 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 10 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)
Figure 1. The most harmful events to public health are TORNADO followed by EXCESSIVE HEAT.
2. Across the United States, which types of events have the greatest economic consequences?
# select required entries for economy
eventEconomic <- subset(eventEconomic, eventEconomic$PROPDMGEXP == "K" | eventEconomic$PROPDMGEXP ==
"k" | eventEconomic$PROPDMGEXP == "M" | eventEconomic$PROPDMGEXP == "m" |
eventEconomic$PROPDMGEXP == "B" | eventEconomic$PROPDMGEXP == "b")
eventEconomic <- subset(eventEconomic, eventEconomic$CROPDMGEXP == "K" | eventEconomic$CROPDMGEXP ==
"k" | eventEconomic$CROPDMGEXP == "M" | eventEconomic$CROPDMGEXP == "m" |
eventEconomic$CROPDMGEXP == "B" | eventEconomic$CROPDMGEXP == "b")
# Convert ecnomic values to number
eventEconomic$PROPDMGEXP <- gsub("m", 1e+06, eventEconomic$PROPDMGEXP, ignore.case = TRUE)
eventEconomic$PROPDMGEXP <- gsub("k", 1000, eventEconomic$PROPDMGEXP, ignore.case = TRUE)
eventEconomic$PROPDMGEXP <- gsub("b", 1e+09, eventEconomic$PROPDMGEXP, ignore.case = TRUE)
eventEconomic$PROPDMGEXP <- as.numeric(eventEconomic$PROPDMGEXP)
eventEconomic$CROPDMGEXP <- gsub("m", 1e+06, eventEconomic$CROPDMGEXP, ignore.case = TRUE)
eventEconomic$CROPDMGEXP <- gsub("k", 1000, eventEconomic$CROPDMGEXP, ignore.case = TRUE)
eventEconomic$CROPDMGEXP <- gsub("b", 1e+09, eventEconomic$CROPDMGEXP, ignore.case = TRUE)
eventEconomic$CROPDMGEXP <- as.numeric(eventEconomic$CROPDMGEXP)
eventEconomic$PROPDMGEXP <- as.numeric(eventEconomic$PROPDMGEXP)
# then sum the damages by each event type
eventEconomic$TOTALDMG <- (eventEconomic$CROPDMG * eventEconomic$CROPDMGEXP) +
(eventEconomic$PROPDMG * eventEconomic$PROPDMGEXP)
eventEconomic <- aggregate(eventEconomic$TOTALDMG, by = list(eventEconomic$EVTYPE),
FUN = sum)
colnames(eventEconomic) <- c("EVTYPE", "TOTALDMG")
# Rank the event type by highest damage cost and take top 5 columns
eventEconomic <- eventEconomic[order(eventEconomic$TOTALDMG, decreasing = TRUE),
]
eventEconomic <- eventEconomic[1:10, ]Results
# Now plot the graph
ggplot() + geom_bar(data = eventEconomic, 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") 
Figure 2.The most harmful events to the economy are FLOOD, followed by HURRICANE/TYPHOON and TORNADO.