Reproducible Research Course Project 2: Severe Weather Impact on Population Health and Economic Consequences in the U.S. 1950-2011
Andrea Gustafsen
12/05/2021
Synopsis
This project analyses the storm database from the U.S. National Oceanic and Atmospheric Administration (NOAA) to find which type of weather events cause most damage to population health and which has the largest economic consequesnses in the Unites States between 1950-2011. Damage to the population health is measured by number of fatalities and injuries. Economic consequences are measured by damages to properties and crops. Tornades causes most fatalities and injuries and floods caused the largest economic consequences.
Load relevant packages
First we load the necessary packages into our R session.
library(plyr)
library(tidyverse)Data Processing
The data was downloaded from the course website:
Storm Data [47MB]
The documentation for the database is available at the below links:
- National Weather Servisr Storm Data Documentation
- National Climatic Data Center Storm Events FAQ
The databse covers events from 1950 to 2011. Fewer events were generallt recorded in earlier years due to lack of good records. Recent years should be more complete.
Loading data into R
The data was downloaded from the above mentioned wesite and then loaded into R.
data <- read.csv("Storm Data.bzip2")Inspecting the data
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 6str(data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...sum(is.na(df))## Warning in is.na(df): is.na() applied to non-(list or vector) of type 'closure'## [1] 0Subsetting the data
We select only the columns needed in this analysis.
# Select tthe variables we need in this analysis
df <- data %>%
select(EVTYPE,
FATALITIES,
INJURIES,
PROPDMG,
PROPDMGEXP,
CROPDMG,
CROPDMGEXP)Data transformation
Damages are stored in two columns with the base in one column and the exponent in another column. This makes it impractical for analysis so we will transform the two base/exp columns into one with the numerical value for both damages to property and crops.
# convert exp columns into numbers: K = 1000, M = 1000000 etc
unique(df$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"df$PROPDMGEXP <- mapvalues(df$PROPDMGEXP,
from = c( "K", "M", "" , "B", "m", "+", "0", "5", "6", "?", "4", "2", "3", "h", "7", "H", "-", "1", "8"),
to = c(10^3, 10^6, 1, 10^9, 10^6, 0, 1, 10^5, 10^6, 0, 10^4, 10^2, 10^3, 10^2, 10^7, 10^2, 0, 10, 10^8))
df$PROPDMGEXP <- as.numeric(df$PROPDMGEXP)
unique(df$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"df$CROPDMGEXP <- mapvalues(df$CROPDMGEXP,
from = c("", "M", "K", "m", "B", "?", "0", "k", "2"),
to = c(0, 10^6, 10^3, 10^6, 10^9, 10^0, 10^0, 10^3, 10^2))
df$CROPDMGEXP <- as.numeric(df$CROPDMGEXP)
# create new columns PROPMDGTOTAL and CROPDMGTOTAL with actual values
df$PROPDMGTOTAL <- df$PROPDMG * df$PROPDMGEXP
df$CROPDMGTOTAL <- df$CROPDMG * df$CROPDMGEXPSince fatalities and injuries together measure the harmful effects on population health we will add these together and store in one column HEALTH_TOT. We will do the same for damages on property and crops and store the total value in one column ECONOMIC_TOT.
# create one column for health impact and one for economic impact
df_totals <- df %>%
mutate(HEALTH_TOT = FATALITIES + INJURIES,
ECONOMIC_TOT = PROPDMGTOTAL + CROPDMGTOTAL)Results
Weather events most harmful to population health
# Across the United States, which types of events are most harmful
# with respect to population health?
agg_health <- with(df_totals, aggregate(HEALTH_TOT ~ EVTYPE, FUN = sum))
health_impact <- agg_health[order(-agg_health$HEALTH_TOT), ][1:10,]
ggplot(health_impact, aes(x = reorder(EVTYPE, -HEALTH_TOT), y = HEALTH_TOT, fill = EVTYPE)) +
geom_bar(stat = "identity") +
labs(title = "Total Number of Fatalities and Injuries Due to Severe Weather Events in the US 1950-2011",
y = "Fatalities and injuries",
x = "") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Weather events with greatest economic consequences
agg_economic <- with(df_totals, aggregate(ECONOMIC_TOT ~ EVTYPE, FUN = sum))
economic_impact <- agg_economic[order(-agg_economic$ECONOMIC_TOT), ][1:10,]
ggplot(economic_impact, aes(x = reorder(EVTYPE, -ECONOMIC_TOT), y = ECONOMIC_TOT, fill = EVTYPE)) +
geom_bar(stat = "identity") +
labs(title = "Total Economic Damages Due to Severe Weather Events in the US 1950-2011",
y = "total cost for damage on property and crop",
x = "") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
1-3 plots