storm_data_analysis
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.
Two research questions are: 1. Across the United States, which types of events are most harmful with respect to population health? 2. Across the United States, which types of events have the greatest economic consequences?
Analysis results show that Tornado is the most harmful event in terms of human fatalites and injuries;Floods have the greatest economic consequences.
Loading and Processing the Raw Data
The data are downloaded from NOAA Storm Database
if (!file.exists('./storm_data')) {dir.create('./storm_data') }
if (!file.exists("storm_data/repdata-data-StormData.csv.bz2")) {
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile="storm_data/repdata-data-StormData.csv.bz2", mode = "wb", method = "curl")
}
if (!exists('storm_data')) {
storm_data <- read.csv("storm_data/repdata-data-StormData.csv.bz2")
}summary of data and its structure
str(storm_data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels ""," Christiansburg",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels ""," CANTON"," TULIA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","%SD",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 436781 levels "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(storm_data)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295data processing and cleaning, creating subset including relevant columns (variables) and rows (data)
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, unionstorm_subset=select(storm_data, EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)
# subset data for Question 1
eventHealth <- subset(storm_subset, !storm_subset$FATALITIES == 0 & !storm_subset$INJURIES ==
0, select = c(EVTYPE, FATALITIES, INJURIES))
# subset data for Question 2
eventEconomic <- subset(storm_subset, !storm_subset$PROPDMG == 0 & !storm_subset$CROPDMG ==
0, select = c(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))RQ1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
First, I calculate total number of FATALITIES and INJURIES frequency grouped by event types; then I select the top 5 events causing death and injuries; finally plot the top 5 major cause for fatalities and injuriees respectively. The resuts revealed that tornadoe is the most dangerous weather event to the populations health.
library(dplyr)
library(ggplot2)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine# calculate sum of FATALITIES and INJURIES grouped by EVTYPE
event_death=eventHealth %>% group_by(EVTYPE) %>%
summarise(sum_death = sum(FATALITIES, na.rm = TRUE))
colnames(event_death) <- c("EVENTTYPE", "FATALITIES")
event_inj=eventHealth %>% group_by(EVTYPE) %>%
summarise(sum_inj = sum(INJURIES, na.rm = TRUE))
colnames(event_inj) = c("EVENTTYPE", "INJURIES")
#reorder dataset and filter top 5 events
event_death <- event_death[order(event_death$FATALITIES, decreasing = TRUE), ][1:5, ]
event_death## # A tibble: 5 × 2
## EVENTTYPE FATALITIES
## <fctr> <dbl>
## 1 TORNADO 5227
## 2 EXCESSIVE HEAT 402
## 3 LIGHTNING 283
## 4 TSTM WIND 199
## 5 FLASH FLOOD 171event_inj <- event_inj[order(event_inj$INJURIES, decreasing = TRUE), ][1:5, ]
event_inj## # A tibble: 5 × 2
## EVENTTYPE INJURIES
## <fctr> <dbl>
## 1 TORNADO 60187
## 2 EXCESSIVE HEAT 4791
## 3 FLOOD 2679
## 4 ICE STORM 1720
## 5 HEAT 1420# plot top 5 major cause for fatalities and injuriees respectively
death_plot=ggplot(data = event_death, aes(x = factor(EVENTTYPE), y = event_death$FATALITIES, fill = EVENTTYPE)) + geom_bar(stat="identity") + coord_flip() + labs(y = "Number of Death", x = "Event type", title = "Top 5 weather events causing fatalities")
injury_plot=ggplot(data = event_inj, aes(x = factor(EVENTTYPE), y = event_inj$INJURIES, fill = EVENTTYPE)) + geom_bar(stat="identity") + coord_flip() + labs(y = "Number of Injuries", x = "Event type", title = "Top 5 weather events causing injuries")
grid.arrange(death_plot, injury_plot, nrow = 2)
RQ2. Across the United States, which types of events have the greatest economic consequences?
I first did data Processing, then plot the dataset out. Results showed floods have the greatest economic consequences.
library(stats)
library(ggplot2)
eventEconomic$PROPMULT<-1
eventEconomic$PROPMULT[eventEconomic$PROPDMGEXP =="H"] <- 100
eventEconomic$PROPMULT[eventEconomic$PROPDMGEXP =="K"] <- 1000
eventEconomic$PROPMULT[eventEconomic$PROPDMGEXP =="M"] <- 1000000
eventEconomic$PROPMULT[eventEconomic$PROPDMGEXP =="B"] <- 1000000000
eventEconomic$CROPMULT<-1
eventEconomic$CROPMULT[eventEconomic$CROPDMGEXP =="H"] <- 100
eventEconomic$CROPMULT[eventEconomic$CROPDMGEXP =="K"] <- 1000
eventEconomic$CROPMULT[eventEconomic$CROPDMGEXP =="M"] <- 1000000
eventEconomic$CROPMULT[eventEconomic$CROPDMGEXP =="B"] <- 1000000000
str(eventEconomic)## 'data.frame': 16242 obs. of 7 variables:
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 409 786 405 408 408 786 786 834 834 812 ...
## $ PROPDMG : num 0.1 5 25 48 20 50 500 500 500 5 ...
## $ PROPDMGEXP: Factor w/ 19 levels "","-","?","+",..: 14 19 19 19 18 17 17 17 17 17 ...
## $ CROPDMG : num 10 500 1 4 10 50 50 5 50 15 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 9 7 9 9 8 7 7 7 7 7 ...
## $ PROPMULT : num 1e+09 1e+06 1e+06 1e+06 1e+00 1e+03 1e+03 1e+03 1e+03 1e+03 ...
## $ CROPMULT : num 1e+06 1e+03 1e+06 1e+06 1e+00 1e+03 1e+03 1e+03 1e+03 1e+03 ...eventEconomic$PRODMG1 <- eventEconomic$PROPDMG * eventEconomic$PROPMULT
eventEconomic$CROPDMG1 <- eventEconomic$CROPDMG * eventEconomic$CROPMULT
eventEconomic <- aggregate(cbind(PRODMG1, CROPDMG1) ~ EVTYPE, data=eventEconomic, FUN=sum)
eventEconomic$TOTALDMG <- eventEconomic$PRODMG1 + eventEconomic$CROPDMG1
eventEconomic <- aggregate(eventEconomic$TOTALDMG, by = list(eventEconomic$EVTYPE),
FUN = sum)
colnames(eventEconomic) <- c("EVTYPE", "TOTALDMG")
# Rank the event type by highest damage cost and take top 5 columns
eventEconomic <- eventEconomic[order(eventEconomic$TOTALDMG, decreasing = TRUE),
]
eventEconomic <- eventEconomic[1:5, ]
#plot
ggplot(data = eventEconomic, aes(x = factor(EVTYPE), y = TOTALDMG, fill =EVTYPE)) +
geom_bar(stat = "identity") + coord_flip() + xlab("Event Type") + ylab("Total Damage")
Results
- Across the United States, which types of events are most harmful with respect to population health? Analysis results show that Tornado is the most harmful event in terms of human fatalites and injuries
- Across the United States, which types of events have the greatest economic consequences? Analysis results show floods have the greatest economic consequences.