Reproducible Research: Peer Assessment 2
Elena Fedorova
15.07.2018
Analysis of the adverse impacts caused by storms in the U.S.
1. Synopsis
The goal of the assignment is to study the effects of severe weather events on both the U.S. population and the economy. To carry out the task, the National Oceanic and Atmospheric Administration’s (NOAA) storm database is going to be explored. 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.
Thus, key questions addressed by the report are as follows:
Across the United States, which types of events (EVTYPE variable) are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?
RStudio is going be used to answer these questions.
2. Loading and reading the data into R
First, we’ll download and read the data into R.
# Loading the data (the file has preliminarily been downloaded))
StormData <- read.csv("repdata%2Fdata%2FStormData.csv")
# A quick glance to the structure of this data set
summary(StormData)## 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 : 569 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :5882943. Subset the data required for the analysis
In the second step, we’ll summarise and filter the required data.
3.1. Selection of the human population damage data: fatalities and injuries (variables “FATALITIES”, “INJURIES”, “INCIDENTS”), removal of the small outliers.
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, unionpopulation_damage <- StormData %>%
group_by(EVTYPE) %>%
mutate(INCIDENTS=max(INJURIES, FATALITIES)) %>%
summarise(injur = max(INJURIES),
fatal = max(FATALITIES),
incid = max(INCIDENTS)) %>%
arrange(incid) %>%
filter(incid > 500)
health_type <- order(population_damage$incid, decreasing=TRUE)3.2. Selection of the property damage data to estimate impact on the ecomomy: (variable “CROPDMG”, “PROPDMG”), removal of the small outliers.
economy_damage <- StormData %>%
group_by(EVTYPE) %>%
summarise(crops = max(CROPDMG),
properties = max (PROPDMG)) %>%
mutate(total_damage=crops+properties) %>%
filter(total_damage > 3000) %>%
arrange(total_damage)
damage_type <- order(economy_damage$total_damage, decreasing=TRUE)4. Visual presentation of the results
4.1. Creating a barplot for the human population damage:
population_damage$names <- as.character(population_damage$EVTYPE)
for (i in 1:length(population_damage$names)) {
lsname <- strsplit(population_damage$names[i], split=" ")
population_damage$names[i] = paste(unlist(lsname), collapse="\n")
}
barplot(names.arg=tolower(as.character(factor(population_damage$names[health_type]) )),
height=t(as.matrix(population_damage[health_type,2:3])),
xlab="Event",
ylab="Number of incidents",
main="U.S. human population damage", col = c("blue", "red"), beside=TRUE)
legend("topright", lty= c(1,1), lwd = c(3,3), c("Injuries", "Fatalities"), col=c("blue", "red"))
Arrange the most harmful events by the number of maximum number of incidents
head(arrange(population_damage, desc(incid)), 6)## # A tibble: 6 x 5
## EVTYPE injur fatal incid names
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TORNADO 1700 158 1700 TORNADO
## 2 ICE STORM 1568 6 1568 "ICE\nSTORM"
## 3 FLOOD 800 15 800 FLOOD
## 4 HURRICANE/TYPHOON 780 15 780 HURRICANE/TYPHOON
## 5 HEAT 230 583 583 HEAT
## 6 EXCESSIVE HEAT 519 99 519 "EXCESSIVE\nHEAT"4.2. Creating a barplot presenting the damage for economy (crops and property):
economy_damage$names <- as.character(economy_damage$EVTYPE)
for (i in 1:length(economy_damage$names)) {
lsname <- strsplit(economy_damage$names[i], split=" ")
economy_damage$names[i] = paste(unlist(lsname), collapse="\n")
}
barplot(names.arg=tolower(as.character(factor(economy_damage$names[damage_type]) )),
height=t(as.matrix(economy_damage[damage_type,2:3])),
xlab="Event",
ylab="Damage USD",
main="Damage to the economy", col = c("yellow", "green"))
legend("topright", lty= c(1,1), lwd = c(3,3), c("Property damage", "Crop damage"), col=c("yellow", "green"))
Arrange the most harmful events by the most financial impact on the economy^:
head(arrange(economy_damage, desc(total_damage)), 6)## # A tibble: 6 x 5
## EVTYPE crops properties total_damage names
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 FLASH FLOOD 950 5000 5950 "FLASH\nFLOOD"
## 2 THUNDERSTORM WIND 800 5000 5800 "THUNDERSTORM\nWIND"
## 3 TORNADO 900 4410 5310 TORNADO
## 4 WATERSPOUT 0 5000 5000 WATERSPOUT
## 5 LANDSLIDE 20 4800 4820 LANDSLIDE
## 6 FLOOD 978 3000 3978 FLOOD5. Conclusion
According to the National Oceanic and Atmospheric Administration’s (NOAA) storm database we can conlcude that tornadoes had the most harmful effect with respect to population health (injuries), and so had heat to deaths.
According to the National Oceanic and Atmospheric Administration’s (NOAA) storm database we can conlcude that flash floows had the most harmful effect with respect to the economy.