Reproducible Research Project 2
Rony Morales
7/11/2020
1: Synopsis
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This project involves exploring 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.
In this assesment we analise the data and identify which types of severe weather events are most harmful on:
- Health (injuries and fatalities)
- Property and crops (economic consequences)
Information on the Data: Documentation
Download the raw data file and extract the data into a dataframe.Then convert to a data.table
library("data.table")
library("ggplot2")
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, destfile = paste0("C:/Users/Moral/Documents/Data Science Specialisation Coursera/Reproducible Research/Reproducible Research Course Project 2", '/repdata%2Fdata%2FStormData.csv.bz2'))
stormDF <- read.csv("C:/Users/Moral/Documents/Data Science Specialisation Coursera/Reproducible Research/Reproducible Research Course Project 2/repdata%2Fdata%2FStormData.csv.bz2")
stormDT <- as.data.table(stormDF)2.2: Identifing column names
colnames(stormDT)## [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"2.3: Data Subsetting
Subset the dataset on the parameters of interest. Basically, we remove the columns we don’t need for clarity.
# Finding columns to remove
cols2Remove <- colnames(stormDT[, !c("EVTYPE"
, "FATALITIES"
, "INJURIES"
, "PROPDMG"
, "PROPDMGEXP"
, "CROPDMG"
, "CROPDMGEXP")])
# Removing columns
stormDT[, c(cols2Remove) := NULL]
# Only use data where fatalities or injuries occurred.
stormDT <- stormDT[(EVTYPE != "?" &
(INJURIES > 0 | FATALITIES > 0 | PROPDMG > 0 | CROPDMG > 0)), c("EVTYPE"
, "FATALITIES"
, "INJURIES"
, "PROPDMG"
, "PROPDMGEXP"
, "CROPDMG"
, "CROPDMGEXP") ]2.4: Converting Exponent Columns into Actual Exponents instead of (-,+, H, K, etc)
Making the PROPDMGEXP and CROPDMGEXP columns cleaner so they can be used to calculate property and crop cost.
# Change all damage exponents to uppercase.
cols <- c("PROPDMGEXP", "CROPDMGEXP")
stormDT[, (cols) := c(lapply(.SD, toupper)), .SDcols = cols]
# Map property damage alphanumeric exponents to numeric values.
propDmgKey <- c("\"\"" = 10^0,
"-" = 10^0,
"+" = 10^0,
"0" = 10^0,
"1" = 10^1,
"2" = 10^2,
"3" = 10^3,
"4" = 10^4,
"5" = 10^5,
"6" = 10^6,
"7" = 10^7,
"8" = 10^8,
"9" = 10^9,
"H" = 10^2,
"K" = 10^3,
"M" = 10^6,
"B" = 10^9)
# Map crop damage alphanumeric exponents to numeric values
cropDmgKey <- c("\"\"" = 10^0,
"?" = 10^0,
"0" = 10^0,
"K" = 10^3,
"M" = 10^6,
"B" = 10^9)
stormDT[, PROPDMGEXP := propDmgKey[as.character(stormDT[,PROPDMGEXP])]]
stormDT[is.na(PROPDMGEXP), PROPDMGEXP := 10^0 ]
stormDT[, CROPDMGEXP := cropDmgKey[as.character(stormDT[,CROPDMGEXP])] ]
stormDT[is.na(CROPDMGEXP), CROPDMGEXP := 10^0 ]2.5: Making Economic Cost Columns
stormDT <- stormDT[, .(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, propCost = PROPDMG * PROPDMGEXP, CROPDMG, CROPDMGEXP, cropCost = CROPDMG * CROPDMGEXP)]2.6: Calcuating Total Property and Crop Cost
totalCostDT <- stormDT[, .(propCost = sum(propCost), cropCost = sum(cropCost), Total_Cost = sum(propCost) + sum(cropCost)), by = .(EVTYPE)]
totalCostDT <- totalCostDT[order(-Total_Cost), ]
totalCostDT <- totalCostDT[1:10, ]
head(totalCostDT, 5)## EVTYPE propCost cropCost Total_Cost
## 1: FLOOD 144657709807 5661968450 150319678257
## 2: HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3: TORNADO 56947380677 414953270 57362333947
## 4: STORM SURGE 43323536000 5000 43323541000
## 5: HAIL 15735267513 3025954473 187612219862.7: Calcuating Total Fatalities and Injuries
totalInjuriesDT <- stormDT[, .(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES), totals = sum(FATALITIES) + sum(INJURIES)), by = .(EVTYPE)]
totalInjuriesDT <- totalInjuriesDT[order(-FATALITIES), ]
totalInjuriesDT <- totalInjuriesDT[1:10, ]
head(totalInjuriesDT, 5)## EVTYPE FATALITIES INJURIES totals
## 1: TORNADO 5633 91346 96979
## 2: EXCESSIVE HEAT 1903 6525 8428
## 3: FLASH FLOOD 978 1777 2755
## 4: HEAT 937 2100 3037
## 5: LIGHTNING 816 5230 60463: Results
3.1: Events that are Most Harmful to Population Health
consequences <- melt(totalInjuriesDT, id.vars="EVTYPE", variable.name = "victims")
head(consequences, 5)## EVTYPE victims value
## 1: TORNADO FATALITIES 5633
## 2: EXCESSIVE HEAT FATALITIES 1903
## 3: FLASH FLOOD FATALITIES 978
## 4: HEAT FATALITIES 937
## 5: LIGHTNING FATALITIES 816# Create chart
healthChart <- ggplot(totalInjuriesDT, aes(x=reorder(EVTYPE,-totals),y=totals))
# Plot data as bar chart
healthChart = healthChart + geom_bar(stat="identity", position="dodge")
# Format y-axis scale and set y-axis label
healthChart = healthChart + ylab("Frequency Count")
# Set x-axis label
healthChart = healthChart + xlab("Event Type")
# Rotate x-axis tick labels
healthChart = healthChart + theme(axis.text.x = element_text(angle=45, hjust=1)) + scale_fill_gradient(low="blue", high="red")
# Set chart title and center it
healthChart = healthChart + ggtitle("Top 10 US More harmful event") + theme(plot.title = element_text(hjust = 0.5))
healthChart
3.2: Events that have the Greatest Economic Consequences
Melting data.table so that it is easier to put in bar graph format
econ_consequences <- melt(totalCostDT, id.vars="EVTYPE", variable.name = "Damage_Type")
head(econ_consequences, 5)## EVTYPE Damage_Type value
## 1: FLOOD propCost 144657709807
## 2: HURRICANE/TYPHOON propCost 69305840000
## 3: TORNADO propCost 56947380677
## 4: STORM SURGE propCost 43323536000
## 5: HAIL propCost 15735267513# Create chart
econChart <- ggplot(totalCostDT, aes(x=reorder(EVTYPE,-Total_Cost),y=Total_Cost))
# Plot data as bar chart
econChart = econChart + geom_bar(stat="identity", position="dodge" , color= "darkblue")
# Format y-axis scale and set y-axis label
econChart = econChart + ylab("Cost Billions of dollars") + scale_y_continuous(labels = scales::unit_format(unit = "B", scale = 1e-9, prefix = "$"))
# Set x-axis label
econChart = econChart + xlab("Event Type")
# Rotate x-axis tick labels
econChart = econChart + theme(axis.text.x = element_text(angle=45, hjust=1))
# Set chart title and center it
econChart = econChart + ggtitle("Top 10 US Storm Events causing Economic Consequences") + theme(plot.title = element_text(hjust = 0.5))
econChart