Course Project 2: Reproducible Research
Manuel Diaz
Synopsis.
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. 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.
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events.
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?
There is some documentation of the database available. Here you will find how some of the variables are constructed or defined. We recommend you check it out. 1. National Weather Service Storm Data Documentation. 2. National Climatic Data Center Storm Events FAQ.
The packages used are:
library(knitr)
library(data.table)
library(ggplot2)
library(dplyr)
library(R.utils)
library(ggpubr)
library(ggthemes)
library(ggeasy)Data Processing.
The data come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the link.
- In the next codes, the data was downloaded and store in the local directory.
url_1 <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
setwd("C:/Users/aleja/Documents/Cursos/Coursera R pratices/RepData_PeerAssessment2")
ifelse(!dir.exists(file.path(getwd(), "Data")),
dir.create(file.path(getwd(), "Data")), FALSE)## [1] FALSEIf the result of the code above is TRUE, it means that there was not a file called “Data”, and is already created. If it’s FALSE, there was already a file called “Data”
download.file(url = url_1, destfile = file.path("./Data", "StormData.csv.bz2"),
method = "curl")
bunzip2("./Data/StormData.csv.bz2", "./Data/StormData.csv");list.files(path = "./Data")- We confirm the csv. file is in the specify path. Then, we need to confirm the data, the structure and the column names.
fread("./Data/StormData.csv")-> df
str(df); head(df)## Classes 'data.table' and '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 ...
## - attr(*, ".internal.selfref")=<externalptr>## 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 COUNTYENDN
## 1: TORNADO 0 0 NA
## 2: TORNADO 0 0 NA
## 3: TORNADO 0 0 NA
## 4: TORNADO 0 0 NA
## 5: TORNADO 0 0 NA
## 6: TORNADO 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 6We need to clean the data.table, due to the amount of NA’s that contains.
- We are going to sort and order data according to deads or fatal events in each extreme weather event.
df %>%
select(EVTYPE, FATALITIES) %>%
group_by(EVTYPE) %>%
summarise(total_fatalities = sum(FATALITIES)) %>%
arrange(-total_fatalities)-> Total_fatal_events; head(Total_fatal_events, 10)## # A tibble: 10 x 2
## EVTYPE total_fatalities
## <chr> <dbl>
## 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 224- Next, We are going to do the same with injured people.
df %>%
select(EVTYPE, INJURIES) %>%
group_by(EVTYPE) %>%
summarise(total_injure = sum(INJURIES)) %>%
arrange(-total_injure) -> Total_injure_events; head(Total_injure_events, 10)## # A tibble: 10 x 2
## EVTYPE total_injure
## <chr> <dbl>
## 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 1361- The same process to obtain the total cost prodiced by extreme weather.
The data has two types of economic impact, namely property damage (PROPDMG) and crop damage (CROPDMG). The damage cost is in $USD, and is coded as shown below:
H, h -> hundreds = x100 K, K -> kilos = x1,000 M, m -> millions = x1,000,000 B,b -> billions = x1,000,000,000 (+) -> x1 (-) -> x0 (?) -> x0 blank -> x0
To know more about the different variables, and the structure of the data, visit the page.
df %>% select(EVTYPE, PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)->df_damage
sort(unique(as.character(df_damage$PROPDMGEXP)))->Symbol
c(0,0,0,1,10,10,10,10,10,10,10,10,10,10^9,10^2,10^2,10^3,10^6,10^6)->Multiplier
data.frame(Symbol, Multiplier)->convert_Multiplier
df_damage$Prop_Multiplier <- convert_Multiplier$Multiplier[match(df_damage$PROPDMGEXP, convert_Multiplier$Symbol)]
df_damage$Crop_Multiplier <- convert_Multiplier$Multiplier[match(df_damage$CROPDMGEXP, convert_Multiplier$Symbol)]
df_d <- df_damage %>%
mutate(PROPDMG = PROPDMG*Prop_Multiplier) %>%
mutate(CROPDMG = CROPDMG*Crop_Multiplier) %>%
mutate(TOTAL_DMG = PROPDMG+CROPDMG)
df_d %>%
group_by(EVTYPE) %>%
summarize(Total_cost_Dmg = sum(TOTAL_DMG, na.rm=T),
Property_cost_Dmg = sum(PROPDMG, na.rm=T),
Crop_cost_Dmg = sum(CROPDMG, na.rm=T))%>%
arrange(-Property_cost_Dmg, -Crop_cost_Dmg, -Total_cost_Dmg)->df_Cost_Damage
head(df_Cost_Damage, 10)## # A tibble: 10 x 4
## EVTYPE Total_cost_Dmg Property_cost_Dmg Crop_cost_Dmg
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 150319678250 144657709800 5661968450
## 2 HURRICANE/TYPHOON 71913712800 69305840000 2607872800
## 3 TORNADO 57352117607 56937162897 414954710
## 4 STORM SURGE 43323541000 43323536000 5000
## 5 FLASH FLOOD 17562132111 16140815011 1421317100
## 6 HAIL 18757611527 15732269877 3025537650
## 7 HURRICANE 14610229010 11868319010 2741910000
## 8 TROPICAL STORM 8382236550 7703890550 678346000
## 9 WINTER STORM 6715441260 6688497260 26944000
## 10 HIGH WIND 5908617580 5270046280 638571300Result
- In the next plot, is shown the extreme weather event that produces the majority fatal events.
g1 <- ggplot(Total_fatal_events[1:10,],
aes(x=reorder(EVTYPE, -total_fatalities), y=total_fatalities) )
g1 +
geom_bar(stat="identity", fill="brown2")+
theme_foundation() +
theme(axis.text.x = element_text(angle=90, vjust=0.5))+
ggtitle("Top 10 Natural Disasters by Deads.") +
labs(x="Natural Disaster Event.", y = "Total Deads")+
theme(plot.title = element_text(hjust = 0.5))->g1g1
- In the next plot, is shown the extreme weather event that produces the majority injured people.
g2 <- ggplot(Total_injure_events[1:10,],
aes(x=reorder(EVTYPE, -total_injure), y=total_injure) )
g2 +
geom_bar(stat="identity", fill="brown2")+
theme_foundation() +
theme(axis.text.x = element_text(angle=90, vjust=0.5))+
ggtitle("Top 10 Natural Disasters by Injures.") +
labs(x="Natural Disaster Event.", y = "Total Injures")+
theme(plot.title = element_text(hjust = 0.5))->g2g2
- Next, we show the extreme weather events that produces the majority economic impact.
g3<- ggplot(df_Cost_Damage[1:10,],
aes(x=reorder(EVTYPE, -Total_cost_Dmg), y=Total_cost_Dmg) )
g3 +
geom_bar(stat="identity", fill="brown2")+
theme_foundation() +
theme(axis.text.x = element_text(angle=90, vjust=0.5))+
ggtitle("Top 10 Natural Disasters by Damage Costs.") +
labs(x="Natural Disaster Event.", y = "Total Deads")+
theme(plot.title = element_text(hjust = 0.5))->g3g3
- The scales from injured people and dead people is very different. So, we plot the graphs together to obtain a better understanding.
g11 <- ggplot(Total_fatal_events[1:10,],
aes(x=reorder(EVTYPE, -total_fatalities), y=total_fatalities) )
g11 +
geom_bar(stat="identity", fill="brown2")+
theme_tufte() +
theme(axis.text.x = element_text(angle=45, vjust=0.5))+
labs(x="Natural Disaster Event.", y = "Total Deads")+
ylim(0, 100000) ->g11
g22 <- ggplot(Total_injure_events[1:10,],
aes(x=reorder(EVTYPE, -total_injure), y=total_injure) )
g22 +
geom_bar(stat="identity", fill="brown2")+
theme_tufte() +
theme(axis.text.x = element_text(angle=45, vjust=0.5)) +
labs(x="Natural Disaster Event.", y = "Total Injures")+
ylim(0,100000) ->g22
g33<- ggplot(df_Cost_Damage[1:10,],
aes(x=reorder(EVTYPE, -Total_cost_Dmg), y=Total_cost_Dmg) )
g33 +
geom_bar(stat="identity", fill="brown2")+
theme_tufte() +
theme(axis.text.x = element_text(angle=45, vjust=0.5))+
ggtitle("Top 10 Natural Disasters by Damage Costs.") +
labs(x="Natural Disaster Event.", y = "Total Deads")+
theme(plot.title = element_text(hjust = 0.5))->g33
fig1<- ggarrange(g33,
ggarrange(g11,g22, ncol = 2, labels = c("Deads", "Injures"), hjust = -2),
nrow = 2, labels = "Total Costs")fig1
Which types of events are most harmful to population health?
The most harmful event to population is the tornados events, because is the event that kills and hurt more people, than the other weather events.
Which types of events have the greatest economic consequences?
The events that have the greatest economic consequences are Floods, Hurricanes, Tornados and Storm Surges respectivily.