Course Project 2
Tanmay Sahasrabudhe
2024-04-16
Reproducible Research Course Project 2 - Analysis of impact of storms and other weather events on human population
Synopsis
Storms can cause problems to public and economic health and may result in fatalities, injuries, property damage, etc. Hence, preventing such occurrences is a key concern. The goal of this report is to identify the most hazardous weather events based on data collected by the United States NOAA (national Oceanic and Atmospheric Administration).
The storm database includes data on such events from 1950 to 2011 for estimates on economic costs, fatalities, etc. The estimates of fatalities and injuries was used to determine weather events that have the most harmful effect on human population.
Environment Setup
The R packages required for this analysis are ggplot2,
dplyr, and xtable.
library(ggplot2)
library(dplyr, warn.conflicts = FALSE)## Warning: package 'dplyr' was built under R version 4.3.3library(xtable, warn.conflicts = FALSE)Load Data
The data is downloaded from the given URL for the project and is
loaded into R using the read.csv() method.
stormData <- read.csv("C:\\Users\\tan20\\PE Notes\\R Files\\repdata_data_StormData.csv\\repdata_data_StormData.csv",sep = ",", header = TRUE)The summary of the dataset is as follows:
names(stormData)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"str(stormData)## '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 ...head(stormData)## 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 6Data Processing
Creating subset of data
Performance of the analysis can be improved. The following variables
(described below) with value > 0 are used for
analysis.
| Variable | Description |
|---|---|
| EVTYPE | Event type (Flood, Heat, Hurricane, Tornado, …) |
| FATALITIES | Number of fatalities resulting from event |
| INJURIES | Number of injuries resulting from event |
| PROPDMG | Property damage in USD |
| PROPDMGEXP | Unit multiplier for property damage (K, M, or B) |
| CROPDMG | Crop damage in USD |
| CROPDMGEXP | Unit multiplier for property damage (K, M, or B) |
| BGN_DATE | Begin date of the event |
| END_DATE | End date of the event |
| STATE | State where the event occurred |
stormDataTidy <- subset(stormData, EVTYPE != "?"
&
(FATALITIES > 0 | INJURIES > 0 | PROPDMG > 0 | CROPDMG > 0),
select = c("EVTYPE",
"FATALITIES",
"INJURIES",
"PROPDMG",
"PROPDMGEXP",
"CROPDMG",
"CROPDMGEXP",
"BGN_DATE",
"END_DATE",
"STATE"))
dim(stormDataTidy)## [1] 254632 10sum(is.na(stormDataTidy))## [1] 0The working (tidy) dataset contains 254632 observations, 10 variables and no missing values.
Clean Event Type Data
There are a total of 487 unique Event Type values in the current tidy dataset.
length(unique(stormDataTidy$EVTYPE))## [1] 487For the variable EVTYPE, the values entered were same,
but the case (uppercase or lowercase) used was different. To normalize
this discrepancy, each value was converted to upper case.
stormDataTidy$EVTYPE <- toupper(stormDataTidy$EVTYPE)# AVALANCHE
stormDataTidy$EVTYPE <- gsub('.*AVALANCE.*', 'AVALANCHE', stormDataTidy$EVTYPE)
# BLIZZARD
stormDataTidy$EVTYPE <- gsub('.*BLIZZARD.*', 'BLIZZARD', stormDataTidy$EVTYPE)
# CLOUD
stormDataTidy$EVTYPE <- gsub('.*CLOUD.*', 'CLOUD', stormDataTidy$EVTYPE)
# COLD
stormDataTidy$EVTYPE <- gsub('.*COLD.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*FREEZ.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*FROST.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*ICE.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LOW TEMPERATURE RECORD.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LO.*TEMP.*', 'COLD', stormDataTidy$EVTYPE)
# DRY
stormDataTidy$EVTYPE <- gsub('.*DRY.*', 'DRY', stormDataTidy$EVTYPE)
# DUST
stormDataTidy$EVTYPE <- gsub('.*DUST.*', 'DUST', stormDataTidy$EVTYPE)
# FIRE
stormDataTidy$EVTYPE <- gsub('.*FIRE.*', 'FIRE', stormDataTidy$EVTYPE)
# FLOOD
stormDataTidy$EVTYPE <- gsub('.*FLOOD.*', 'FLOOD', stormDataTidy$EVTYPE)
# FOG
stormDataTidy$EVTYPE <- gsub('.*FOG.*', 'FOG', stormDataTidy$EVTYPE)
# HAIL
stormDataTidy$EVTYPE <- gsub('.*HAIL.*', 'HAIL', stormDataTidy$EVTYPE)
# HEAT
stormDataTidy$EVTYPE <- gsub('.*HEAT.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WARM.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*HIGH.*TEMP.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*RECORD HIGH TEMPERATURES.*', 'HEAT', stormDataTidy$EVTYPE)
# HYPOTHERMIA/EXPOSURE
stormDataTidy$EVTYPE <- gsub('.*HYPOTHERMIA.*', 'HYPOTHERMIA/EXPOSURE', stormDataTidy$EVTYPE)
# LANDSLIDE
stormDataTidy$EVTYPE <- gsub('.*LANDSLIDE.*', 'LANDSLIDE', stormDataTidy$EVTYPE)
# LIGHTNING
stormDataTidy$EVTYPE <- gsub('^LIGHTNING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('^LIGNTNING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('^LIGHTING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
# MICROBURST
stormDataTidy$EVTYPE <- gsub('.*MICROBURST.*', 'MICROBURST', stormDataTidy$EVTYPE)
# MUDSLIDE
stormDataTidy$EVTYPE <- gsub('.*MUDSLIDE.*', 'MUDSLIDE', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*MUD SLIDE.*', 'MUDSLIDE', stormDataTidy$EVTYPE)
# RAIN
stormDataTidy$EVTYPE <- gsub('.*RAIN.*', 'RAIN', stormDataTidy$EVTYPE)
# RIP CURRENT
stormDataTidy$EVTYPE <- gsub('.*RIP CURRENT.*', 'RIP CURRENT', stormDataTidy$EVTYPE)
# STORM
stormDataTidy$EVTYPE <- gsub('.*STORM.*', 'STORM', stormDataTidy$EVTYPE)
# SUMMARY
stormDataTidy$EVTYPE <- gsub('.*SUMMARY.*', 'SUMMARY', stormDataTidy$EVTYPE)
# TORNADO
stormDataTidy$EVTYPE <- gsub('.*TORNADO.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*TORNDAO.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LANDSPOUT.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WATERSPOUT.*', 'TORNADO', stormDataTidy$EVTYPE)
# SURF
stormDataTidy$EVTYPE <- gsub('.*SURF.*', 'SURF', stormDataTidy$EVTYPE)
# VOLCANIC
stormDataTidy$EVTYPE <- gsub('.*VOLCANIC.*', 'VOLCANIC', stormDataTidy$EVTYPE)
# WET
stormDataTidy$EVTYPE <- gsub('.*WET.*', 'WET', stormDataTidy$EVTYPE)
# WIND
stormDataTidy$EVTYPE <- gsub('.*WIND.*', 'WIND', stormDataTidy$EVTYPE)
# WINTER
stormDataTidy$EVTYPE <- gsub('.*WINTER.*', 'WINTER', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WINTRY.*', 'WINTER', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*SNOW.*', 'WINTER', stormDataTidy$EVTYPE)Tidying the dataset reduced the number of values in the
`EVTYPE variable to 81.
length(unique(stormDataTidy$EVTYPE))## [1] 81Clean Date Data
Format date variables for any type of optional reporting or further analysis.
In the raw dataset, the BNG_START and
END_DATE variables are stored as factors which should be
made available as actual date types that can be manipulated and
reported on. For now, time variables will be ignored.
Create four new variables based on date variables in the tidy dataset:
| Variable | Description |
|---|---|
| DATE_START | Begin date of the event stored as a date type |
| DATE_END | End date of the event stored as a date type |
| YEAR | Year the event started |
| DURATION | Duration (in hours) of the event |
stormDataTidy$DATE_START <- as.Date(stormDataTidy$BGN_DATE, format = "%m/%d/%Y")
stormDataTidy$DATE_END <- as.Date(stormDataTidy$END_DATE, format = "%m/%d/%Y")
stormDataTidy$YEAR <- as.integer(format(stormDataTidy$DATE_START, "%Y"))
stormDataTidy$DURATION <- as.numeric(stormDataTidy$DATE_END - stormDataTidy$DATE_START)/3600Clean Economic Data
The information about Property Damage is logged using two variables:
PROPDMG and PROPDMGEXP. PROPDMG
is the mantissa (the significand) rounded to three significant digits
and PROPDMGEXP is the exponent (the multiplier). The same
approach is used for Crop Damage where the CROPDMG variable
is encoded by the CROPDMGEXP variable.
The documentation also specifies that the PROPDMGEXP and
CROPDMGEXP are supposed to contain an alphabetical
character used to signify magnitude and logs “K” for thousands, “M” for
millions, and “B” for billions. A quick review of the data, however,
shows that there are several other characters being logged.
table(toupper(stormDataTidy$PROPDMGEXP))##
## - + 0 2 3 4 5 6 7 B
## 11585 1 5 210 1 1 4 18 3 3 40
## H K M
## 7 231427 11327table(toupper(stormDataTidy$CROPDMGEXP))##
## ? 0 B K M
## 152663 6 17 7 99953 1986In order to calculate costs, the PROPDMGEXP and
CROPDMGEXP variables are mapped to a multiplier factor
which will then be used to calculate the actual costs for both property
and crop damage. Two new variables will be created to store damage
costs:
- PROP_COST
- CROP_COST
# function to get multiplier factor
getMultiplier <- function(exp) {
exp <- toupper(exp);
if (exp == "") return (10^0);
if (exp == "-") return (10^0);
if (exp == "?") return (10^0);
if (exp == "+") return (10^0);
if (exp == "0") return (10^0);
if (exp == "1") return (10^1);
if (exp == "2") return (10^2);
if (exp == "3") return (10^3);
if (exp == "4") return (10^4);
if (exp == "5") return (10^5);
if (exp == "6") return (10^6);
if (exp == "7") return (10^7);
if (exp == "8") return (10^8);
if (exp == "9") return (10^9);
if (exp == "H") return (10^2);
if (exp == "K") return (10^3);
if (exp == "M") return (10^6);
if (exp == "B") return (10^9);
return (NA);
}
# calculate property damage and crop damage costs (in billions)
stormDataTidy$PROP_COST <- with(stormDataTidy, as.numeric(PROPDMG) * sapply(PROPDMGEXP, getMultiplier))/10^9
stormDataTidy$CROP_COST <- with(stormDataTidy, as.numeric(CROPDMG) * sapply(CROPDMGEXP, getMultiplier))/10^9Summarize Data
A summary of the data is generated and arranged in descending order of health impact.
healthImpactData <- aggregate(x = list(HEALTH_IMPACT = stormDataTidy$FATALITIES + stormDataTidy$INJURIES),
by = list(EVENT_TYPE = stormDataTidy$EVTYPE),
FUN = sum,
na.rm = TRUE)
healthImpactData <- healthImpactData[order(healthImpactData$HEALTH_IMPACT, decreasing = TRUE),]In the same summary, the data is now arranged in descending order of cost impact.
damageCostImpactData <- aggregate(x = list(DAMAGE_IMPACT = stormDataTidy$PROP_COST + stormDataTidy$CROP_COST),
by = list(EVENT_TYPE = stormDataTidy$EVTYPE),
FUN = sum,
na.rm = TRUE)
damageCostImpactData <- damageCostImpactData[order(damageCostImpactData$DAMAGE_IMPACT, decreasing = TRUE),]Results
Event Types Most Harmful to Population Health
Fatalities and injuries have the most harmful impact on population health. The results below display the 10 most harmful weather events in terms of population health in the U.S.
print(xtable(head(healthImpactData, 10),
caption = "Top 10 Weather Events Most Harmful to Population Health"),
caption.placement = 'top',
type = "html",
include.rownames = FALSE,
html.table.attributes='class="table-bordered", width="100%"')| EVENT_TYPE | HEALTH_IMPACT |
|---|---|
| TORNADO | 97075.00 |
| HEAT | 12392.00 |
| FLOOD | 10127.00 |
| WIND | 9893.00 |
| LIGHTNING | 6049.00 |
| STORM | 4780.00 |
| COLD | 3100.00 |
| WINTER | 1924.00 |
| FIRE | 1698.00 |
| HAIL | 1512.00 |
healthImpactChart <- ggplot(head(healthImpactData, 10),
aes(x = reorder(EVENT_TYPE, HEALTH_IMPACT), y = HEALTH_IMPACT, fill = EVENT_TYPE)) +
coord_flip() +
geom_bar(stat = "identity") +
xlab("Event Type") +
ylab("Total Fatalities and Injures") +
theme(plot.title = element_text(size = 14, hjust = 0.5)) +
ggtitle("Top 10 Weather Events Most Harmful to\nPopulation Health")
print(healthImpactChart)
Event Types with Greatest Economic Consequences
Property and crop damage have the most harmful impact on the economy. The results below display the 10 most harmful weather events in terms economic consequences in the U.S.
print(xtable(head(damageCostImpactData, 10),
caption = "Top 10 Weather Events with Greatest Economic Consequences"),
caption.placement = 'top',
type = "html",
include.rownames = FALSE,
html.table.attributes='class="table-bordered", width="100%"')| EVENT_TYPE | DAMAGE_IMPACT |
|---|---|
| FLOOD | 180.58 |
| HURRICANE/TYPHOON | 71.91 |
| STORM | 70.45 |
| TORNADO | 57.43 |
| HAIL | 20.74 |
| DROUGHT | 15.02 |
| HURRICANE | 14.61 |
| COLD | 12.70 |
| WIND | 12.01 |
| FIRE | 8.90 |
damageCostImpactChart <- ggplot(head(damageCostImpactData, 10),
aes(x = reorder(EVENT_TYPE, DAMAGE_IMPACT), y = DAMAGE_IMPACT, fill = EVENT_TYPE)) +
coord_flip() +
geom_bar(stat = "identity") +
xlab("Event Type") +
ylab("Total Property / Crop Damage Cost\n(in Billions)") +
theme(plot.title = element_text(size = 14, hjust = 0.5)) +
ggtitle("Top 10 Weather Events with\nGreatest Economic Consequences")
print(damageCostImpactChart)
Conclusion
Based on the analysis, the following conclusions can be drawn.
Which types of weather events are most harmful to population health?
Tornadoes are responsible for the greatest number of fatalities and injuries.
Which types of weather events have the greatest economic consequences?
Floods are responsible for causing the most property damage and crop damage costs.