Reproducible Research - Course Project 2
Shounak Shastri
02/05/2020
Analysis of severe weather events and their effect on the health and economic stability of the US population
Synopsis
This project uses the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database to analyse the impact of severe weather events on the health and economic stability of the American population.
This analysis concentrates mainly on the number of fatalities and injuries due to severe weather conditions for analysing the health impact and the damage to property and crops to analyse the economic impact.
The results show that the fatalities and injuries are the highest when there is an occurance of a Tornado, Floods are the most damaging to property and draughts for the agriculture.
Downloading and Reading the Data
setwd("C:\\Users\\Intel\\Documents\\Datasets\\Storm Data")
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
#download.file(url, "StormData.csv.bz2")
if(!exists("stormData")) {
stormData <- read.csv("StormData.csv",header = TRUE)
}Basic Exploratory Analysis
Here, we check the summary of the data. We can the see the column names which would be required and plan our analysis accordingly.
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 : 569 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588294The data contains two columns, FATALATIES and INJURIES, which describe the damage to human health.
PART 1 - Processing and Analysing of data for damages to health
The fatalaties and injuries are extracted from the data and combined according to the Event using the tapply function.
deaths <- tapply(stormData$FATALITIES,
stormData$EVTYPE,
sum)
injuries <- tapply(stormData$INJURIES,
stormData$EVTYPE,
sum)These operations produce two tables representing the sum of fatalaties and injuries all over the country according to the event type
Import the required libraries
library(reshape2)## Warning: package 'reshape2' was built under R version 3.6.3library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.6.3Plot the top 10 weather events which caused the most deaths.
# Sort the fatalities data into descending order
deaths <- deaths[order(deaths, decreasing = TRUE)]
deaths <- deaths[1:10]
deaths <- melt(deaths, "Row.names")
deaths$Row.names <- as.factor(deaths$Row.names)Results for damages to health
# Plot the fatalities with respect to the event type
deathPlot <- ggplot(deaths, aes(x = Row.names, y = value)) +
geom_bar(stat = "identity") +
ylim(0, 6000) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle=90, vjust=0.5, hjust=1)) +
ggtitle("Events with Most Fatalities") +
labs(x = "Event", y = "Number of Deaths")
print(deathPlot)
From the figure, it can be seen that the highest number of deaths were recorded due to Tornados. On some further examination, it can be noted that the number of fatalities is higher than the other events by a hugh margin.
Plot the top 10 weather events which caused the most Injuries.
Here, we use the same method as above
# Sort the Injuries data into descending order
injuries <- injuries[order(injuries, decreasing = TRUE)]
injuries <- injuries[1:10]
injuries <- melt(injuries, "Event")
injuries$Event <- as.factor(injuries$Event)
# Plot the injuries data
injuriesplot <- ggplot(injuries, aes(x = Event, y = value)) +
geom_bar(stat = "identity") +
ylim(0, 100000) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle=90, vjust=0.5, hjust=1)) +
ggtitle("Events with Most Injuries") +
labs(x = "Event", y = "Number of Injuries")
print(injuriesplot)
The figure for Injuries shows that Tornados cause the most injuries among all the events.
PART 2 - Processing and Analysing the data for economic damages
The data uses four variables to depict the economic damages -PROPDMG: The overall damage to property -PROPDMGEXP: multiplier for property damage -CROPDMG: Overall damage to crops -CROPDMGEXP: multiplier for crop damage
Analysing and modifying the EXP columns
We first check the number of unique multipliers used for both tyoes of damages using the unique() function
unique(stormData$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Munique(stormData$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MMultipliers used in the data: - “?”: 0 USD - " “: 1 USD -”0“: 10^0 USD -”1“: 10^1 USD -”2“: 10^2 USD -”3“: 10^3 USD -”4“: 10^4 USD -”5“: 10^5 USD -”6“: 10^6 USD -”7“: 10^7 USD -”8“: 10^8 USD -”h" or “H”: 10^2 USD - “k” or “K”: 10^3 USD - “m” or “M”: 10^6 USD - “B”: 10^9 USD
Converting the multipliers for property damage into proper numbers and finding the real damage to property in USD
library(plyr)## Warning: package 'plyr' was built under R version 3.6.3stormData$PROPDMGEXP <- mapvalues(warn_missing = F,
stormData$PROPDMGEXP,
from = c("K", "M","", "B", "m", "+",
"0", "5", "6", "?", "4", "2", "3", "h",
"7", "H", "-", "1", "8"),
to = c(10^3, 10^6, 1, 10^9, 10^6, 0,
1,10^5, 10^6, 0, 10^4, 10^2, 10^3,
10^2, 10^7, 10^2, 0, 10, 10^8))
stormData$PROPDMGEXP <- as.numeric(as.character(stormData$PROPDMGEXP))
propertyDamage <- data.frame(stormData$EVTYPE,
(stormData$PROPDMG * stormData$PROPDMGEXP)/10^9)
colnames(propertyDamage) <- c("Event", "Damages")Aggregating the damage to property according to the event type
propDamageAgg <- tapply(propertyDamage$Damages,
propertyDamage$Event,
sum)
propDamageAgg <- propDamageAgg[order(propDamageAgg, decreasing = TRUE)]
propDamageAgg <- propDamageAgg[1:10]
propDamageAgg <- melt(propDamageAgg, "Event")
propDamageAgg$Event <- as.factor(propDamageAgg$Event)Converting the multipliers for crop damage into proper numbers and finding the real damage to crops in USD
stormData$CROPDMGEXP <- mapvalues(warn_missing = F,
stormData$CROPDMGEXP,
from = c("","M", "K", "m", "B",
"?", "0", "k","2"),
to = c(1,10^6, 10^3, 10^6, 10^9,
0, 1, 10^3, 10^2))
stormData$CROPDMGEXP <- as.numeric(as.character(stormData$CROPDMGEXP))
cropDamage <- data.frame(stormData$EVTYPE,
(stormData$CROPDMG * stormData$CROPDMGEXP)/10^9)
colnames(cropDamage) <- c("Event", "Damages")Aggregating the damage to crops according to the event type
cropDamageAgg <- tapply(cropDamage$Damages,
cropDamage$Event,
sum)
cropDamageAgg <- cropDamageAgg[order(cropDamageAgg, decreasing = TRUE)]
cropDamageAgg <- cropDamageAgg[1:10]
cropDamageAgg <- melt(cropDamageAgg, "Event")
cropDamageAgg$Event <- as.factor(cropDamageAgg$Event)Results for Damages to the economy
Plotting the damage to property with respect to the event type
propDamagePlot <- ggplot(propDamageAgg, aes(x = Event, y = value)) +
geom_bar(stat = "identity") +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle=90, vjust=0.5, hjust=1)) +
ggtitle("Events with Most Property Damage") +
labs(x = "Event", y = "Property Damage in Billion USD")
print(propDamagePlot)
It can be seen from the figure that floods cause the most damage to property
Plotting the damage to crops with respect to the event type
cropDamagePlot <- ggplot(cropDamageAgg, aes(x = Event, y = value)) +
geom_bar(stat = "identity") +
ylim(0, 15) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle=90, vjust=0.5, hjust=1)) +
ggtitle("Events with Most Crop Damage") +
labs(x = "Event", y = "Crop Damage in Billion USD")
print(cropDamagePlot)
It can be seen from the figure that Droughts cause the most damage to crops