The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events.
Sypnosis
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 in order to answer two questions. 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.
1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Data Processing
First, we load in the data from the csv file included in the zip archive and check the dimension and first few rows
knitr::opts_chunk$set(echo = TRUE)
#WEEK 4 Code
# Reading data
Raw_data <- read.csv("repdata_data_StormData.csv.bz2")
# subsetting by date
Main_data <- Raw_data
Main_data$BGN_DATE <- strptime(Raw_data$BGN_DATE, "%m/%d/%Y %H:%M:%S")
Main_data <- subset(Main_data, BGN_DATE > "1995-12-31")
# subsetting to needed columns
Main_data <- subset(Main_data, select = c(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))
#cleaning event types names
Main_data$EVTYPE <- toupper(Main_data$EVTYPE)
# eliminating zero data
Main_data <- Main_data[Main_data$FATALITIES !=0 |
Main_data$INJURIES !=0 |
Main_data$PROPDMG !=0 |
Main_data$CROPDMG !=0, ]
RESULT
Population health data processing
Health_data <- aggregate(cbind(FATALITIES, INJURIES) ~ EVTYPE, data = Main_data, FUN=sum)
Health_data$PEOPLE_LOSS <- Health_data$FATALITIES + Health_data$INJURIES
Health_data <- Health_data[order(Health_data$PEOPLE_LOSS, decreasing = TRUE), ]
Top10_events_people <- Health_data[1:10,]
print(Top10_events_people)
## EVTYPE FATALITIES INJURIES PEOPLE_LOSS
## 149 TORNADO 1511 20667 22178
## 39 EXCESSIVE HEAT 1797 6391 8188
## 48 FLOOD 414 6758 7172
## 107 LIGHTNING 651 4141 4792
## 153 TSTM WIND 241 3629 3870
## 46 FLASH FLOOD 887 1674 2561
## 146 THUNDERSTORM WIND 130 1400 1530
## 182 WINTER STORM 191 1292 1483
## 69 HEAT 237 1222 1459
## 88 HURRICANE/TYPHOON 64 1275 1339
Economic consequences data processing
Creating total damage values
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.5.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
Main_data <- mutate(Main_data,
PROPDMGTOTAL = PROPDMG * (10 ^ PROPDMGEXP),
CROPDMGTOTAL = CROPDMG * (10 ^ CROPDMGEXP))
## Warning: package 'bindrcpp' was built under R version 3.5.2
#analyzing
Economic_data <- aggregate(cbind(PROPDMGTOTAL, CROPDMGTOTAL) ~ EVTYPE, data = Main_data, FUN=sum)
Economic_data$ECONOMIC_LOSS <- Economic_data$PROPDMGTOTAL + Economic_data$CROPDMGTOTAL
Economic_data <- Economic_data[order(Economic_data$ECONOMIC_LOSS, decreasing = TRUE), ]
Top10_events_economy <- Economic_data[1:10,]
print(Top10_events_economy)
## EVTYPE PROPDMGTOTAL CROPDMGTOTAL ECONOMIC_LOSS
## 48 FLOOD 143944833550 4974778400 148919611950
## 88 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 141 STORM SURGE 43193536000 5000 43193541000
## 149 TORNADO 24616945710 283425010 24900370720
## 66 HAIL 14595143420 2476029450 17071172870
## 46 FLASH FLOOD 15222203910 1334901700 16557105610
## 86 HURRICANE 11812819010 2741410000 14554229010
## 32 DROUGHT 1046101000 13367566000 14413667000
## 152 TROPICAL STORM 7642475550 677711000 8320186550
## 83 HIGH WIND 5247860360 633561300 5881421660
Plotting health loss
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.2
g <- ggplot(data = Top10_events_people, aes(x = reorder(EVTYPE, PEOPLE_LOSS), y = PEOPLE_LOSS))
g <- g + geom_bar(stat = "identity", colour = "black")
g <- g + labs(title = "Total people loss in USA by weather events in 1996-2011")
g <- g + theme(plot.title = element_text(hjust = 0.5))
g <- g + labs(y = "Number of fatalities and injuries", x = "Event Type")
g <- g + coord_flip()
print(g)
Plotting economic loss
g <- ggplot(data = Top10_events_economy, aes(x = reorder(EVTYPE, ECONOMIC_LOSS), y = ECONOMIC_LOSS))
g <- g + geom_bar(stat = "identity", colour = "black")
g <- g + labs(title = "Total economic loss in USA by weather events in 1996-2011")
g <- g + theme(plot.title = element_text(hjust = 0.5))
g <- g + labs(y = "Size of property and crop loss", x = "Event Type")
g <- g + coord_flip()
print(g)
