The effect of bad weather on the quality of life in the United Stated of America
Pascal Spijkerman
07/12/2019
Ingroduction
This report is written for the Data Science Course of Coursera in the coursetrack of Course 5, Reproducible Research.In this report, we explore the NOAA Storm Database and analyse the effect of extreme weather effects in the United States.
The report answers 2 questions: 1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
Data processing
We first start importing necessary libraries and the Storm data. Then we identify variables in the storm data.
The data is downloaded from the Coursera project website.
library(knitr)
library(ggplot2)
library(dplyr)
data <- read.csv("repdata_data_StormData.csv")Then we take a look at the available variables in the dataset.
head(data)## 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 6Then we aggregate the data and have a glance at the results before actually visualise the results in nice tables.
fatalities <- aggregate(FATALITIES ~EVTYPE, data = data, sum, na.rm=TRUE)
fatalities <- arrange(fatalities, desc(FATALITIES))
fatalities<- fatalities[1:10,]
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 224injuries <- aggregate(INJURIES ~EVTYPE, data = data, sum, na.rm=TRUE)
injuries <- arrange(injuries, desc(INJURIES))
injuries <- injuries[1:10,]
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 1361propdmg <- aggregate(PROPDMG~EVTYPE, data = data, sum, na.rm=TRUE)
propdmg <- arrange(propdmg, desc(PROPDMG))
propdmg <- propdmg[1:10,]
propdmg## EVTYPE PROPDMG
## 1 TORNADO 3212258.2
## 2 FLASH FLOOD 1420124.6
## 3 TSTM WIND 1335965.6
## 4 FLOOD 899938.5
## 5 THUNDERSTORM WIND 876844.2
## 6 HAIL 688693.4
## 7 LIGHTNING 603351.8
## 8 THUNDERSTORM WINDS 446293.2
## 9 HIGH WIND 324731.6
## 10 WINTER STORM 132720.6cropdmg <- aggregate(CROPDMG~EVTYPE, data = data, sum, na.rm=TRUE)
cropdmg <- arrange(cropdmg, desc(CROPDMG))
cropdmg <- cropdmg[1:10,]
cropdmg## EVTYPE CROPDMG
## 1 HAIL 579596.28
## 2 FLASH FLOOD 179200.46
## 3 FLOOD 168037.88
## 4 TSTM WIND 109202.60
## 5 TORNADO 100018.52
## 6 THUNDERSTORM WIND 66791.45
## 7 DROUGHT 33898.62
## 8 THUNDERSTORM WINDS 18684.93
## 9 HIGH WIND 17283.21
## 10 HEAVY RAIN 11122.80Results
#1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
We analyse the effect on fatalities and injuries. Tables show Tornados are the most harmful for both fatalities and injuries.
par(mfrow = c(1, 2), mar = c(15, 8, 4, 4), mgp = c(3, 1, 0), cex = 0.7)
barplot(fatalities$FATALITIES, names.arg = fatalities$EVTYPE, las = 3, main = "Weather events with highest number of fatalities", ylab = "number of fatalities", col = "lightblue")
barplot(injuries$INJURIES, names.arg = injuries$EVTYPE, las = 3, main = "Weather events with highest number of injuries", ylab = "number of injuries", col = "lightblue")
#2. Across the United States, which types of events have the greatest economic consequences?
For this question, we focus on the variables PROPDMG (Propery Damage) and CROPDMG (Crop Damage). Results show Tornados has the highes property damage and Hails have the highest crop damage.
par(mfrow = c(1, 2), mar = c(15, 8, 4, 4), mgp = c(3, 1, 0), cex = 0.7)
barplot(propdmg$PROPDMG, names.arg = propdmg$EVTYPE, las = 3, main = "Weather events and property damage", ylab = "Propert Damage in USD", col = "lightblue")
barplot(cropdmg$CROPDMG, names.arg = cropdmg$EVTYPE, las = 3, main = "Weather events and crop damage", ylab = "Crop Damage in USD", col = "lightblue")