HOW PUBLIC HEALTH AND THE ECONOMY ARE IMPACTED BY SEVERE WEATHER EVENTS IN THE UNITED STATES
CHARLES WESTBY
September 22, 2015
SYNOPSIS
In this report, we will be exploring how extreme weather events effected public health and the economy during the history of the United States. These extreme weather events include tornados, floods, lightning and other inclement weather. In order to determine the public health consequences of each weather event, we will see how many injuries and fatalities were caused. When considering the immediate economic impact, we will see how much crop and property damage was caused by these events.
PROCESSING DATA
Setting Working Directory
The working directory is where you work when you are coding in R. Currently there is nothing in it. But when we start working, it is the place where we will save the downloaded data. All other work that is saved during the course of this report will also be saved in this directory.
setwd("~/GitHub/Reproducible_Research_Assignment_2")Opening Packages to Be Used
These are the packages that will be used. The library command calls up the packages in the working directory. dplyr is used to manipulate the data. While ggplot2 will be used to graph the data. Finally, gridExtra will be used so that multiple graphs can be put together.
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(ggplot2)
library(gridExtra)Downloading and Storing Storm Data
Here, the data on Severe Weather Events in the United States is downloaded from the following URL. Once it is downloaded, it is saved in the working directory. Next, it is stored in the variable storm_data. From this point on in the report, storm_data will represent the Severe Weather data.
if (!file.exists("StormData.csv.bz2")) {
fileURL <- 'https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2'
download.file(fileURL, destfile="StormData.csv.bz2", method = "curl")
}
storm_data <- read.csv("StormData.csv.bz2")Storm Data Summary
We check the summary of storm_data to get an idea of some of the parameters of the data.
summary(storm_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) :588295Storm Data Heading
After that we check the heading of the data, in order to see how the data is listed in the columns and rows.
head(storm_data)## 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 6Summary and List of Storm Event Types
Finally, we take a look at the different kinds of weather events that are listed in storm_data. Although there are plenty listed here, this report focuses on the events that caused the most economic and public health damage.
summary(storm_data$EVTYPE)## HAIL TSTM WIND THUNDERSTORM WIND
## 288661 219940 82563
## TORNADO FLASH FLOOD FLOOD
## 60652 54277 25326
## THUNDERSTORM WINDS HIGH WIND LIGHTNING
## 20843 20212 15754
## HEAVY SNOW HEAVY RAIN WINTER STORM
## 15708 11723 11433
## WINTER WEATHER FUNNEL CLOUD MARINE TSTM WIND
## 7026 6839 6175
## MARINE THUNDERSTORM WIND WATERSPOUT STRONG WIND
## 5812 3796 3566
## URBAN/SML STREAM FLD WILDFIRE BLIZZARD
## 3392 2761 2719
## DROUGHT ICE STORM EXCESSIVE HEAT
## 2488 2006 1678
## HIGH WINDS WILD/FOREST FIRE FROST/FREEZE
## 1533 1457 1342
## DENSE FOG WINTER WEATHER/MIX TSTM WIND/HAIL
## 1293 1104 1028
## EXTREME COLD/WIND CHILL HEAT HIGH SURF
## 1002 767 725
## TROPICAL STORM FLASH FLOODING EXTREME COLD
## 690 682 655
## COASTAL FLOOD LAKE-EFFECT SNOW FLOOD/FLASH FLOOD
## 650 636 624
## LANDSLIDE SNOW COLD/WIND CHILL
## 600 587 539
## FOG RIP CURRENT MARINE HAIL
## 538 470 442
## DUST STORM AVALANCHE WIND
## 427 386 340
## RIP CURRENTS STORM SURGE FREEZING RAIN
## 304 261 250
## URBAN FLOOD HEAVY SURF/HIGH SURF EXTREME WINDCHILL
## 249 228 204
## STRONG WINDS DRY MICROBURST ASTRONOMICAL LOW TIDE
## 196 186 174
## HURRICANE RIVER FLOOD LIGHT SNOW
## 174 173 154
## STORM SURGE/TIDE RECORD WARMTH COASTAL FLOODING
## 148 146 143
## DUST DEVIL MARINE HIGH WIND UNSEASONABLY WARM
## 141 135 126
## FLOODING ASTRONOMICAL HIGH TIDE MODERATE SNOWFALL
## 120 103 101
## URBAN FLOODING WINTRY MIX HURRICANE/TYPHOON
## 98 90 88
## FUNNEL CLOUDS HEAVY SURF RECORD HEAT
## 87 84 81
## FREEZE HEAT WAVE COLD
## 74 74 72
## RECORD COLD ICE THUNDERSTORM WINDS HAIL
## 64 61 61
## TROPICAL DEPRESSION SLEET UNSEASONABLY DRY
## 60 59 56
## FROST GUSTY WINDS THUNDERSTORM WINDSS
## 53 53 51
## MARINE STRONG WIND OTHER SMALL HAIL
## 48 48 47
## FUNNEL FREEZING FOG THUNDERSTORM
## 46 45 45
## Temperature record TSTM WIND (G45) Coastal Flooding
## 43 39 38
## WATERSPOUTS MONTHLY PRECIPITATION WINDS
## 37 36 36
## (Other)
## 2940Assigning Variables
In this section we will be assigning variables to be used in the R Coding. This step makes the data easy to use. Also it is a great way to demonstrate the steps used to build our plots. #### Event The different weather events from storm_data are stored in the variable event.
event <- storm_data$EVTYPEFatalities
Here the fatalities from each weather event in storm_data are stored in the variable fatalities. Next the variable sum_fatalities is created. sum_fatalities is the sum of all the fatalities caused by each event. Finally, we create top_10_fatalities, which is a variable where the top ten fatalities are ordered and listed.
fatalities <- storm_data$FATALITIES
sum_fatalities <- aggregate(fatalities~event, storm_data, sum, na.rm=TRUE)
top_10_fatalities <- sum_fatalities[order(-sum_fatalities$fatalities), ][1:10, ]Injuries
The same process used to create fatalities, sum_fatalities, and top_10_fatalities are used to create the variables injuries, sum_injuries, and top_10_injuries.
These two sets of variables are used to determine the cost to public health from each weather event.
injuries <- storm_data$INJURIES
sum_injuries <- aggregate(injuries~event, storm_data, sum, na.rm=TRUE)
top_10_injuries <- sum_injuries[order(-sum_injuries$injuries), ][1:10, ]Crop Exponent
Our next step is to figure out the economic damage. So we will look at the cost of property damage and crop damage caused by each weather event. From the data in storm_data we see that crop damage and property damage are listed with an accompanying exponent. These exponents are “H”, “K”, “M”, and “B”, which respectively denote hundreds, thousands, millions, and billions of dollars.
So we create the variable crop_exponent, which is a list of all the crop damage exponents listed in storm_data. Next we tell R to read crop_exponent as a character. After that we tell R to change “H” to “2”, “K” to “3”, “M” to “6”, and “B” to “9”. These numbers correspond to the exponential power represented by each letter. The next step in our code tells R to return any data not listed as an “H”, “K”, “M”, or “B” in crop_exponent as a “0”. The final code tells R to read crop_exponent as a numeric.
crop_exponent <- storm_data$CROPDMGEXP
crop_exponent <- as.character(crop_exponent)
crop_exponent[toupper(crop_exponent) == "H"] <- "2"
crop_exponent[toupper(crop_exponent) == "K"] <- "3"
crop_exponent[toupper(crop_exponent) == "M"] <- "6"
crop_exponent[toupper(crop_exponent) == "B"] <- "9"
crop_exponent[is.na(crop_exponent)] <- "0"
crop_exponent <- as.numeric(crop_exponent)## Warning: NAs introduced by coercionCrop Damage
This next section is a follow up to the previous section. All crop damage listed in storm_data is listed with a crop damage number value and a crop damage exponent. The crop damage exponents were all manipulated and stored in the variable crop_exponent in the previous section.
Here we manipulate the data listed in the column crop damage and store it in the variable crop_damage. Next we multiply the values stored in crop_damage by 10 raised to the exponent stored in the variable crop_exponent. This gives us the total value of the crop damage listed in storm_data. This value is stored in the variable crop_damage, which replaces its previous value. Next we add up all the crop_damage listed in storm_data by weather event. This value is stored in the variable sum_crop_damage. After this we tell R to list the top 10 causes of crop_damage caused by weather event and store it in the variable top_10_crop.
crop_damage <- storm_data$CROPDMG
crop_damage <- crop_damage * 10^crop_exponent
sum_crop_damage <- aggregate(crop_damage~event, storm_data, sum, na.rm=TRUE)
top_10_crop <- sum_crop_damage[order(-sum_crop_damage$crop_damage), ][1:10, ]Property Exponent
The same process used to create the variable crop_exponent is used to create the variable property_exponent.
property_exponent <- storm_data$PROPDMGEXP
property_exponent <- as.character(property_exponent)
property_exponent[toupper(property_exponent) == "H"] <- "2"
property_exponent[toupper(property_exponent) == "K"] <- "3"
property_exponent[toupper(property_exponent) == "M"] <- "6"
property_exponent[toupper(property_exponent) == "B"] <- "9"
property_exponent[is.na(property_exponent)] <- "0"
property_exponent <- as.numeric(property_exponent)## Warning: NAs introduced by coercionProperty Damage
The same process used to create the variables crop_damage, sum_crop_damage and top_10_crop are used to create the variables property_damage, sum_property_damage and top_10_property. This gives us the top 10 causes of property damage for weather events in storm_data
property_damage <- storm_data$PROPDMG
property_damage <- property_damage * 10^property_exponent
sum_property_damage <- aggregate(property_damage~event, storm_data, sum, na.rm=TRUE)
top_10_property <- sum_property_damage[order(-sum_property_damage$property_damage), ][1:10, ]RESULTS
Weather Events with Highest Health Consequences (Fatalities and Injuries)
In this section we will look at the results of the data analyis of storm_data. By the end, we will see the top 10 weather events that cause fatalities, injuries, crop_damage and property_damage. This analysis will show us the impact on public health and immediate economic consequences caused by each weather event.
Fatalities from Weather Events
Here we tell R to display the variable top_10_fatalities, which tells us which weather events caused the most fatalities.
top_10_fatalities## event fatalities
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 224Injuries from Weather Events
Next we tell R to show the variable top_10_injuries, which shows which weather events caused the most injuries.
top_10_injuries## event injuries
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100
## 427 ICE STORM 1975
## 153 FLASH FLOOD 1777
## 760 THUNDERSTORM WIND 1488
## 244 HAIL 1361Graphs for Fatalities and Injuries from Weather Events
Next we use the ggplot command in R to graph the data stored in top_10_fatalities and top_10_injuries as bar graphs. The first one shows how many fatalities were caused by each weather event. The next one shows the amount of injuries caused by each weather event. The code shows how each graph was labeled. It also shows how the weather events listed in the x-axis are made to be at a 90 degree angle. These graphs are stored in the variables fatalities_graph and injuries_graph. Finally the graphs stored in the variables fatalities_graph and injuries_graph are used as arguments in the command grid.arrange along with an argument ncol=2, which tell the command grid.arrange to use two columns. These arguments in the command grid.arrange make the two graphs be put together side by side.
fatalities_graph <- ggplot(top_10_fatalities, aes(event, fatalities)) + geom_bar(stat="identity") + labs(title="Fatalities", x="Weather Event", y="Fatalities") + theme(axis.text.x=element_text(angle=90))
injuries_graph <- ggplot(top_10_injuries, aes(event, injuries)) + geom_bar(stat="identity") + labs(title="Injuries", x="Weather Event", y="Injuries") + theme(axis.text.x=element_text(angle=90))
grid.arrange(fatalities_graph, injuries_graph, ncol=2)
Weather Events with Largest Economic Consequences (Crop and Property Damage)
Crop Damages from Weather Events
Here we display the variable top_10_crop which shows us which weather events caused the most crop damage.
top_10_crop## event crop_damage
## 16 DROUGHT 13972566000
## 34 FLOOD 5661968450
## 98 RIVER FLOOD 5029459000
## 85 ICE STORM 5022113500
## 52 HAIL 3025954470
## 77 HURRICANE 2741910000
## 82 HURRICANE/TYPHOON 2607872800
## 30 FLASH FLOOD 1421317100
## 26 EXTREME COLD 1292973000
## 46 FROST/FREEZE 1094086000Property Damages from Weather Events
Then we display top_10_property, which shows which weather events caused the most property damage.
top_10_property## event property_damage
## 62 FLOOD 144657709800
## 179 HURRICANE/TYPHOON 69305840000
## 333 TORNADO 56947380614
## 281 STORM SURGE 43323536000
## 50 FLASH FLOOD 16822673772
## 103 HAIL 15735267456
## 171 HURRICANE 11868319010
## 341 TROPICAL STORM 7703890550
## 400 WINTER STORM 6688497251
## 156 HIGH WIND 5270046260Graphs for Crop Damages and Property Damages from Weather Events
In this section we use the process used to put together the graphs of the weather events that caused the most fatalites and injuries to make graphs that show which weather events caused the most crop and property damage.
crop_graph <- ggplot(top_10_crop, aes(event, crop_damage)) + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle=90)) + labs(title="Crop Damage", x="Weather Event", y="Crop Damage")
property_graph <- ggplot(top_10_property, aes(event, property_damage)) + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle=90)) + labs(title="Property Damage", x="Weather Event", y="Property Damage")
grid.arrange(crop_graph, property_graph, ncol=2)
CONCLUSION
From the data that was stored in storm_data we can see which weather events had the most immediate economic and public health consequences. We see that tornados caused the most fatalities and injuries. Not only did they cause the most damage to public health, but they beat out the next weather events by far. From the results we see that droughts cause the most crop damage and floods cause the most property damage. Therefore, the weather event that has the biggest immediate economic consequences differs with respect to crop damage and property damage.