Johns Hopkins University - Coursera - Reproducible Research - Week 4 - Course Project 2
Ken Yang
May 4, 2021
Title : Data Analysis of Adverse Weather Impacts on U.S. Population and Economy
1. Synopsis
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events have resulted in fatalities, injuries, and property damage. Preventing such outcomes to the extent possible is a key concern.
This project uses 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 goal of this project is to explore the NOAA storm database and answer some basic questions about severe weather events. Database are used to answer those questions and all code is shown for the entire analysis. The analysis consists of tables, figures, or other summaries. Various R packages are also used to support the analysis.
2. Key Questions to be Addressed in the Data Analysis
Question 1 : Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Question 2 : Across the United States, which types of events have the greatest economic consequences?
3. Data Processing
3.1 Data
The data for this project come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. The file is downloaded from the web site:
file name : Storm Data [47Mb]
website link : https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
There is also some documentation of the database available. You will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation (website link : https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf)
National Climatic Data Center Storm Events FAQ (website link : https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf)
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
3.2 Data Preparation
Step 3.2.1 : load libraries and set global option to block warning message
library(rmarkdown)
library(knitr)
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(grid)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(plyr)## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarizeknitr::opts_chunk$set(warning=FALSE)Step 3.2.2 : Download data file to current working directory and read the data
## Perform the file download
if(!file.exists("./stormData.csv.bz2")){
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile="./stormData.csv.bz2")
}
## read the data file
storm <- read.csv("stormData.csv.bz2", header=TRUE, sep=",")
## look at data
head(storm)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 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 : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(storm)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:19.0 Class :character Class :character Class :character
## Median :30.0 Mode :character Mode :character Mode :character
## Mean :31.2
## 3rd Qu.:45.0
## Max. :95.0
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## 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 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## Step 3.2.3 : Select relevant data for this Analysis
storm_subset <- storm[ , c("EVTYPE", "BGN_DATE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]Step 3.2.4 : transform data for this analysis
## Transform Raw Date to Date:
storm_subset$BGN_DATE <- as.POSIXct(storm_subset$BGN_DATE,format="%m/%d/%Y %H:%M:%S")
## Look at data
head(storm_subset)## EVTYPE BGN_DATE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 1950-04-18 0 15 25.0 K 0
## 2 TORNADO 1950-04-18 0 0 2.5 K 0
## 3 TORNADO 1951-02-20 0 2 25.0 K 0
## 4 TORNADO 1951-06-08 0 2 2.5 K 0
## 5 TORNADO 1951-11-15 0 2 2.5 K 0
## 6 TORNADO 1951-11-15 0 6 2.5 K 0str(storm_subset)## 'data.frame': 902297 obs. of 8 variables:
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_DATE : POSIXct, format: "1950-04-18" "1950-04-18" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...summary(storm_subset)## EVTYPE BGN_DATE FATALITIES
## Length:902297 Min. :1950-01-03 00:00:00 Min. : 0.0000
## Class :character 1st Qu.:1995-04-20 00:00:00 1st Qu.: 0.0000
## Mode :character Median :2002-03-19 00:00:00 Median : 0.0000
## Mean :1998-12-30 13:50:20 Mean : 0.0168
## 3rd Qu.:2007-07-28 00:00:00 3rd Qu.: 0.0000
## Max. :2011-11-30 00:00:00 Max. :583.0000
## NA's :219
## INJURIES PROPDMG PROPDMGEXP CROPDMG
## Min. : 0.0000 Min. : 0.00 Length:902297 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 0.00 Class :character 1st Qu.: 0.000
## Median : 0.0000 Median : 0.00 Mode :character Median : 0.000
## Mean : 0.1557 Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.0000 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :1700.0000 Max. :5000.00 Max. :990.000
##
## CROPDMGEXP
## Length:902297
## Class :character
## Mode :character
##
##
##
## 3.3 Exploratory Data Analysis
QUESTION 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health ?
Step 3.3.1 : Data pre-processing (Question 1)
## Aggregate fatalities and injuries by Event Type
fatalities <- aggregate(FATALITIES ~ EVTYPE, data=storm_subset, sum)
injuries <- aggregate(INJURIES ~ EVTYPE, data=storm_subset, sum)
## Arrange fatalities and injuries in descending order by Event Type
## and find out the top 10
## (note: using arrange function is required to load library dplyr)
top10_fatalities <- arrange(fatalities,desc(FATALITIES),EVTYPE)[1:10,]
top10_injuries <- arrange(injuries,desc(INJURIES),EVTYPE)[1:10,]
## convert event type variable to factor for analysis
top10_fatalities$EVTYPE <- factor(top10_fatalities$EVTYPE, levels=top10_fatalities$EVTYPE)
top10_injuries$EVTYPE <- factor(top10_injuries$EVTYPE, levels=top10_injuries$EVTYPE)
## look at the fatalities by top 10 weather Event Type
top10_fatalities## EVTYPE FATALITIES
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224## look at the injuries by top 10 weather Event Type
top10_injuries## EVTYPE INJURIES
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361Step 3.3.2 : Plots of fatalities and injuries by top 10 weather event type (Question 1)
# plot of fatalities by Event Type
top10_fatalities_plot <- ggplot(top10_fatalities, aes(x = EVTYPE, y = FATALITIES)) +
geom_bar(stat = "identity", fill = "blue", width = NULL) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Fatalities")
# plot of injuries by Event Type
top10_injuries_plot <- ggplot(top10_injuries, aes(x = EVTYPE, y = INJURIES)) +
geom_bar(stat = "identity", fill = "blue", width = NULL) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Injuries")
# load libraries grid and gridExtra for grid.arrange function
grid.arrange(top10_fatalities_plot, top10_injuries_plot, ncol=2, nrow=1,
top = textGrob("Fatalities & Injuries for top 10 Weather Events",gp=gpar(fontsize=14,font=3)))
Question 2 : Across the United States, which types of events have the greatest economic consequences?
With respect to Economic consequences, damages caused by weather events include properties and crops.
In the database, the property damage (“PROPDMG” column) and crop damage (“CROPDMG” column) are related to another 2 columns titled ‘exponents’ (i.e “PROPDMGEXP” column and “CROPDMGEXP” column).
Step 3.3.3 : Data pre-processing (Question 2)
# convert event damage amount (property and crop) to integer format
# (note: load library plyr for mapvalues function)
unique(storm_subset$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"unique(storm_subset$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"tmp_PROPDMG <- mapvalues(storm_subset$PROPDMGEXP,
c("K", "M", "", "B", "m", "+", "0", "5", "6", "?", "4",
"2", "3", "h", "7", "H", "-", "1", "8"),
c(1e3, 1e6, 1, 1e9,1e6, 1, 1,1e5,1e6, 1,1e4,1e2,1e3,
1,1e7,1e2, 1, 10,1e8)
)
tmp_CROPDMG <- mapvalues(storm_subset$CROPDMGEXP,
c("", "M", "K", "m", "B", "?", "0", "k", "2"),
c(1, 1e6, 1e3, 1e6, 1e9, 1, 1, 1e3, 1e2)
)Step 3.3.4 : Calculate the damage amount (Question 2)
# calculate damage (property and crop)
storm_subset$TOTAL_PROPDMG <- as.numeric(tmp_PROPDMG) * storm_subset$PROPDMG
storm_subset$TOTAL_CROPDMG <- as.numeric(tmp_CROPDMG) * storm_subset$CROPDMG
# Total Damage Amount = Property Damage Amount + Crop Damage Amount
storm_subset$TOTAL_DMG <- storm_subset$TOTAL_PROPDMG + storm_subset$TOTAL_CROPDMG
# sum the total for (1) property damage, (2) corp damage, and
# (3) damage of property and corp
propdamage_sum <- aggregate(TOTAL_PROPDMG ~ EVTYPE, data=storm_subset, sum)
cropdamage_sum <- aggregate(TOTAL_CROPDMG ~ EVTYPE, data=storm_subset, sum)
totaldamage_sum <- aggregate(TOTAL_DMG ~ EVTYPE, data=storm_subset, sum)
# Arrange in descending order for damages of property and crop by Weather Event Type (EVTYPE)
# (Top 10 Events)
propdamage_sum_desc <- arrange(propdamage_sum, desc(propdamage_sum$TOTAL_PROPDMG),EVTYPE)[1:10,]
cropdamage_sum_desc <- arrange(cropdamage_sum, desc(cropdamage_sum$TOTAL_CROPDMG),EVTYPE)[1:10,]
totaldamage_sum_desc <- arrange(totaldamage_sum, desc(totaldamage_sum$TOTAL_DMG),EVTYPE)[1:10,]
# Show table for total of PROPERTY DAMAGE by Weather Event Type (in descending order)
propdamage_sum_desc## EVTYPE TOTAL_PROPDMG
## 1 FLOOD 144657709807
## 2 HURRICANE/TYPHOON 69305840000
## 3 TORNADO 56947380677
## 4 STORM SURGE 43323536000
## 5 FLASH FLOOD 16822673979
## 6 HAIL 15735267513
## 7 HURRICANE 11868319010
## 8 TROPICAL STORM 7703890550
## 9 WINTER STORM 6688497251
## 10 HIGH WIND 5270046295# Show table for total of CROP DAMAGE by Weather Event Type (in descending order)
cropdamage_sum_desc## EVTYPE TOTAL_CROPDMG
## 1 DROUGHT 13972566000
## 2 FLOOD 5661968450
## 3 RIVER FLOOD 5029459000
## 4 ICE STORM 5022113500
## 5 HAIL 3025954473
## 6 HURRICANE 2741910000
## 7 HURRICANE/TYPHOON 2607872800
## 8 FLASH FLOOD 1421317100
## 9 EXTREME COLD 1292973000
## 10 FROST/FREEZE 1094086000# Show table for total of PROPERTY DAMAGE and CROP DAMAGE by Weather Event Type (in descending order)
totaldamage_sum_desc## EVTYPE TOTAL_DMG
## 1 FLOOD 150319678257
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57362333947
## 4 STORM SURGE 43323541000
## 5 HAIL 18761221986
## 6 FLASH FLOOD 18243991079
## 7 DROUGHT 15018672000
## 8 HURRICANE 14610229010
## 9 RIVER FLOOD 10148404500
## 10 ICE STORM 8967041360Step 3.3.5 : plot graphs for damage amounts (Question 2)
# show plot for TOTAL PROPERTY DAMAGE by Event Type (with setting ENVTYPE as factor)
propdamage_sum_desc$EVTYPE <- factor(propdamage_sum_desc$EVTYPE, levels=propdamage_sum_desc$EVTYPE)
propdamage_ggplot <- ggplot(propdamage_sum_desc, aes(x=EVTYPE, y=TOTAL_PROPDMG)) +
geom_bar(stat="identity", fill="red") +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
xlab("Event Type") + ylab("Property Damages ($)")
# show plot for TOTAL PROPERTY DAMAGE by Event Type (with setting ENVTYPE as factor)
cropdamage_sum_desc$EVTYPE <- factor(cropdamage_sum_desc$EVTYPE, levels=cropdamage_sum_desc$EVTYPE)
cropdamage_ggplot <- ggplot(cropdamage_sum_desc, aes(x=EVTYPE, y=TOTAL_CROPDMG)) +
geom_bar(stat="identity", fill="green") +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
xlab("Event Type") + ylab("Crop Damages ($)")
# show plot for TOTAL DAMAGE by Event Type (with setting ENVTYPE as factor)
totaldamage_sum_desc$EVTYPE <- factor(totaldamage_sum_desc$EVTYPE, levels = totaldamage_sum_desc$EVTYPE)
totaldamage_ggplot <- ggplot(totaldamage_sum_desc, aes(x=EVTYPE, y=TOTAL_DMG)) +
geom_bar(stat="identity", fill="blue") +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
xlab("Event Type") + ylab("Total Prop & Crop Damages ($)")
# plot the final graph
grid.arrange(propdamage_ggplot,cropdamage_ggplot, totaldamage_ggplot,
ncol=3, nrow=1, top=textGrob("Impacts of Damages on Property, Crop, & Overall from the top 10 Weather Events", gp=gpar(fontsize=14,font=3)
)
)
4. Results
Findings from the 2 graphs for Question 1:
Tornado causes the most fatalities and injuries and is considered as having the most signifcant harmful impact on public health.
Excessive heat and flash flooding are the second and third leading causes of fatalities respectively.
TSTM wind and flood are the second and third leading causes of injuries respectively.
Findings from the 3 graphs for Question 2:
Flood causes the most significant total damages on property.
Drought causes the most significant total damages on crop.
Typhoon and tornado cause the second and third significant total damages on property respectively.
Flood and river flood cause the second and third significant total damages on crop respectively.
Flooding, typhoon and tornado are the 3 leading causes of damages on property and crop.