Analysis of Storm data
QW
3/18/2022
Explore Population health and economic impacts from U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database (1950-2011)
Synopsis
In this report we aim to explore the most harmful events to population health and the events having the greatest economic consequences in U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database between the years 1950 and 2011.From these data, we found that Tornado is the most harmful weather event to population health, and the flood weather has the greatest economic consequences in terms of property and crop.
Data Processing
Read the Storm Data. The original data come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size.
if(!exists("storm.data")) {
storm.data <- read.csv(bzfile("repdata_data_StormData.csv.bz2"),header = TRUE)
}
#head(storm.data)
#summary(storm.data)
#str(storm.data)Create our analysis dataset by including the interested variables from storm.data.
analysis_data <- storm.data[,c(8,23:28)]
head(analysis_data)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0### Create a new variable (health_impact), which is the total number of deaths + injuries.
analysis_data$health_impact <- analysis_data[,2] + analysis_data[,3]
#str(analysis_data)
#summary(analysis_data)
#View(table(analysis_data$PROPDMGEXP))There’re total 985 types of weather event. Due to the page limitation,
we only report the top 10 most harmful events to population health based on the numbers of deaths plus injuries.
library(dplyr)##
## 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#View(table(analysis_data$EVTYPE))
health_impact <- analysis_data %>% select(health_impact, EVTYPE) %>%
group_by(EVTYPE) %>%
summarise(health_sum = sum(health_impact)) %>%
slice_max(health_sum, n=10)
#View(health_impact)
table(analysis_data$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5 6
## 465934 1 8 5 216 25 13 4 4 28 4
## 7 8 B h H K m M
## 5 1 40 1 6 424665 7 11330head(analysis_data)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0
## health_impact
## 1 15
## 2 0
## 3 2
## 4 2
## 5 2
## 6 6#In order to correctly calculate the the economic damage from property and crop, we have to convert the character unit into numbers.
PROPDMGEXP_1 <- rep(0, length(analysis_data$PROPDMGEXP))
PROPDMGEXP_1[which(analysis_data$PROPDMGEXP %in% c("K","k"))] = 10^3
PROPDMGEXP_1[which(analysis_data$PROPDMGEXP %in% c("M","m"))] = 10^6
PROPDMGEXP_1[which(analysis_data$PROPDMGEXP %in% c("B","b"))] = 10^9
table(analysis_data$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994CROPDMGEXP_1 <- rep(0, length(analysis_data$CROPDMGEXP))
CROPDMGEXP_1[which(analysis_data$CROPDMGEXP %in% c("K","k"))] = 10^3
CROPDMGEXP_1[which(analysis_data$CROPDMGEXP %in% c("M","m"))] = 10^6
CROPDMGEXP_1[which(analysis_data$CROPDMGEXP %in% c("B","b"))] = 10^9
### Create another new variable (economy_impact), which is the total economic loss of property + crop.
analysis_data$economy_impact <- analysis_data$PROPDMG*PROPDMGEXP_1 + analysis_data$CROPDMG*CROPDMGEXP_1
#View(analysis_data)
economy_impact <- analysis_data %>% select(economy_impact, EVTYPE) %>%
group_by(EVTYPE) %>%
summarise(economy_sum = sum(economy_impact)) %>%
slice_max(economy_sum, n=10)
#View(economy_impact)Results
In the figure below, We could find that the most harmful weather event TORNARDO in terms of the total number of death and injuries.
library(ggplot2)
plot1 <- ggplot(health_impact, aes(x = EVTYPE, y= health_sum, fill=EVTYPE)) +
geom_bar(stat = "identity") +
xlab("Weather type of event") +
ylab("Total number of deaths plus injuries")+
ggtitle("The population health imapct by the weather type in America \n (1950-2011)")+
ggeasy::easy_center_title()+
theme(axis.text.x = element_text(angle = 15, size = 10, vjust = 1.0, hjust=1),
legend.title = element_blank(),
legend.text = element_text(size = 9),
legend.position = c(0.20, 0.65))
plot1
In the figure below, We could find that the flood weather event has the greated economic consequences in terms of property and crop.
plot2 <- ggplot(economy_impact, aes(x = EVTYPE, y= economy_sum, fill=EVTYPE)) +
geom_bar(stat = "identity") +
xlab("Weather type of event") +
ylab("Total amount of economic damage on property and crop ($)")+
ggtitle("Total economic consequence by the weather type in America \n (1950-2011)")+
ggeasy::easy_center_title()+
theme(axis.text.x = element_text(angle = 15, size = 10, vjust = 1.0, hjust=1),
legend.title = element_blank(),
legend.text = element_text(size = 9),
legend.position = c(0.75, 0.70))
plot2