Using the accumulated data of natural disasters from 1950 to 2011, we want to attempt to answer the following questions. 1. Across the United States, which types of events (as indicated in the π΄π πππΏπ΄ variable) are most harmful with respect to population health? 2. Across the United States, which types of events have the greatest economic consequences? For this study, population health is regarded as all casualties (fatalities and injuries), and economic consequences is the total cost of property damage and crops damage. Our analysis shows clearly that tornadoes have the most detrimental impact on both health and property costs, but additional health risks are heat-related casualties, floods, thunderstorms, and lightning. Property damage is also very high for flash floods, other floods, hail, thunderstorms, and wind storms.
## Unzip and read the data into R
StormData <- read.csv("repdata%2Fdata%2FStormData.csv.bz2", header=TRUE, stringsAsFactors = FALSE)
## Observe the data real quick, and see that the main information we need is in only a few columns
head(StormData)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6## Load the appropriate packages
library(ggvis)
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, unionTo determine the total health affects of natural disasters we will remove all columns from our data frame except the Event Type, Fatalities, and Injuries. We will then create a new column of total casualties, and then aggregate casualty totals by event.
## Health damage
SDhealth <- select(StormData, EVTYPE, FATALITIES, INJURIES) %>% # Filter DF to only relevant columns
mutate(CASUALTIES = FATALITIES+INJURIES) %>% # Total casualties
group_by(EVTYPE) %>% summarise(TOTAL_CASUALTIES=sum(CASUALTIES)) %>% # Aggregate totals by event type
arrange(desc(TOTAL_CASUALTIES)) # Sort descending
## We will just look at the top 20
SDhealth <- SDhealth[1:20,]
SDhealth %>% ggvis(~EVTYPE, ~TOTAL_CASUALTIES) %>% layer_bars() %>%
add_axis("x", properties = axis_props(labels=(list(angle=45, align = "top")))) %>%
add_axis("x", orient = "top", ticks = 0, title = "Fatalities and Injuries from Natural Disasters",
properties = axis_props(
axis = list(stroke = "white"),
labels = list(fontSize = 0)))Because tornadoes represent such a high disparity of casualties and it is difficult to see the actual relationship between other factors, we can remove that one element and re-plot the chart.
## Remove the top contender to balance out other factors
SDhealth2 <- SDhealth[2:20,]
SDhealth2 %>% ggvis(~EVTYPE, ~TOTAL_CASUALTIES) %>% layer_bars() %>%
add_axis("x", properties = axis_props(labels=(list(angle=45, align = "top")))) %>%
add_axis("x", orient = "top", ticks = 0, title = "Fatalities and Injuries (Minus Tornados)",
properties = axis_props(
axis = list(stroke = "white"),
labels = list(fontSize = 0)))Similar to health risk, for property risk we will filter out only Event Type, Property Damage value and Crop Damage values, then aggregate totals in a new column.
## Property Damage vs Crop Damage
SDdmg <- select(StormData, EVTYPE, PROPDMG, CROPDMG) %>% # Filter DF to only relevant columns
mutate(TOTALDMG = PROPDMG+CROPDMG) %>% # Total Damages
group_by(EVTYPE) %>% summarise(TOTAL_DAMAGE=sum(TOTALDMG)) %>% # Aggregate damages
arrange(desc(TOTAL_DAMAGE)) # Sort Descending
## Just look at the top 20
SDdmg <- SDdmg[1:20,]
SDdmg %>% ggvis(~EVTYPE, ~TOTAL_DAMAGE) %>% layer_bars() %>%
add_axis("x", properties = axis_props(labels=(list(angle=45, align = "top")))) %>%
add_axis("x", orient = "top", ticks = 0, title = "Damage from Natural Disasters",
properties = axis_props(
axis = list(stroke = "white"),
labels = list(fontSize = 0)))Based on this data we can clearly see that the most economic damage is caused by tornados, and after that flood events of various types and thunderstorms and windstorms.