Analysis of the Impact of Weather Events on Public Health and Economy in the United States.
Author: Javier Samir Rey Date : Saturday, June 20, 2015
Synopsis
This search aims to identify weather events that cause more harm to the health of people, on the information based on the records provided by the National Weather Service for the years 1950-2011 , this research assumes that both deaths injuries are harmful for the health of people considering that many deaths can bring health problems.
Data Processing
#Fix for publishing in rpubs:
#https://support.rstudio.com/hc/en-us/articles/205002917-SSL-certificate-problem-when-publishing-to-RPubs
library(readr)
library(R.utils)## Loading required package: R.oo
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## cat, commandArgs, getOption, inherits, isOpen, parse, warningsrm(list = ls())
bunzip2("repdata-data-StormData.csv.bz2", overwrite=T, remove=F)
stormData <- suppressWarnings(read_csv("repdata-data-StormData.csv"))##
|================================================================================| 100% 535 MBdim(stormData)## [1] 902297 37str(stormData)## Classes 'tbl_df', 'tbl' 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 : int 130 145 1600 900 1500 2000 100 900 2000 2000 ...
## $ 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 : logi NA NA NA NA NA NA ...
## $ BGN_LOCATI: logi NA NA NA NA NA NA ...
## $ END_DATE : logi NA NA NA NA NA NA ...
## $ END_TIME : logi NA NA NA NA NA NA ...
## $ 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 : logi NA NA NA NA NA NA ...
## $ END_LOCATI: logi NA NA NA NA NA NA ...
## $ 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: logi NA NA NA NA NA NA ...
## $ WFO : logi NA NA NA NA NA NA ...
## $ STATEOFFIC: logi NA NA NA NA NA NA ...
## $ ZONENAMES : logi NA NA NA NA NA NA ...
## $ 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 : logi NA NA NA NA NA NA ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "problems")=Classes 'tbl_df', 'tbl' and 'data.frame': 6139392 obs. of 4 variables:
## ..$ row : int 1671 1673 1674 1675 1678 1679 1680 1681 1682 1683 ...
## ..$ col : int 29 29 29 29 29 29 29 29 29 29 ...
## ..$ expected: chr "T/F/TRUE/FALSE" "T/F/TRUE/FALSE" "T/F/TRUE/FALSE" "T/F/TRUE/FALSE" ...
## ..$ actual : chr "NG" "NG" "NG" "NG" ...head(stormData)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 900 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 NA NA NA NA 0
## 2 TORNADO 0 NA NA NA NA 0
## 3 TORNADO 0 NA NA NA NA 0
## 4 TORNADO 0 NA NA NA NA 0
## 5 TORNADO 0 NA NA NA NA 0
## 6 TORNADO 0 NA NA NA NA 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 NA NA 14.0 100 3 0 0
## 2 NA 0 NA NA 2.0 150 2 0 0
## 3 NA 0 NA NA 0.1 123 2 0 0
## 4 NA 0 NA NA 0.0 100 2 0 0
## 5 NA 0 NA NA 0.0 150 2 0 0
## 6 NA 0 NA NA 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0 NA NA NA NA
## 2 0 2.5 K 0 NA NA NA NA
## 3 2 25.0 K 0 NA NA NA NA
## 4 2 2.5 K 0 NA NA NA NA
## 5 2 2.5 K 0 NA NA NA NA
## 6 6 2.5 K 0 NA NA NA NA
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 NA 1
## 2 3042 8755 0 0 NA 2
## 3 3340 8742 0 0 NA 3
## 4 3458 8626 0 0 NA 4
## 5 3412 8642 0 0 NA 5
## 6 3450 8748 0 0 NA 6summary(stormData[,c(8,23,24,25,27)])## EVTYPE FATALITIES INJURIES
## Length:902297 Min. : 0.0000 Min. : 0.0000
## Class :character 1st Qu.: 0.0000 1st Qu.: 0.0000
## Mode :character Median : 0.0000 Median : 0.0000
## Mean : 0.0168 Mean : 0.1557
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :583.0000 Max. :1700.0000
## PROPDMG CROPDMG
## Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00 Median : 0.000
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000colnames(stormData)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"#Normalize names
colnames(stormData)[8] <- "evtype"
colnames(stormData)[23] <- "fatalities"
colnames(stormData)[24] <- "injuries"
colnames(stormData)[25] <- "propdmg"
colnames(stormData)[27] <- "cropdmg"Results - Impact in Population Health
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Prepare data for analysis it does the following:
- Cleaning some data
- Grouping by event type and calculate the sum of injuries together fatalities.
- Filter out events with no fatalities and injuries.
- Sort the table with items with the highest fatalities and injuries in top.
- Selected top 20.
library(dplyr)##
## Attaching package: 'dplyr'
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
stormData$evtype[stormData$evtype == "THUNDERSTORM WINDS"] <- "THUNDERSTORM WIND"
stormByEvent <- stormData %>%
group_by(evtype) %>%
summarise(harmful = sum(fatalities + injuries)) %>%
filter(harmful > 0) %>%
arrange(desc(harmful))
stormByEventGraph <- stormByEvent[1:20,]
head(stormByEventGraph)## Source: local data frame [6 x 2]
##
## evtype harmful
## 1 TORNADO 96979
## 2 EXCESSIVE HEAT 8428
## 3 TSTM WIND 7461
## 4 FLOOD 7259
## 5 LIGHTNING 6046
## 6 HEAT 3037g <- ggplot(stormByEventGraph, aes(x=reorder(evtype, harmful), y = harmful))
g <- g + geom_bar(stat="identity", colour = "#43e8d8", fill = "#43e8d8")
g <- g + labs(title = "Top Event Types (Population Health)", x = "Event Type", y = "Fatalities and Injuires")
g <- g + theme(plot.title = element_text(color="#666666", face="bold", size=19))
g <- g + theme(axis.title = element_text(color="#666666", face="bold", size=19))
g <- g + coord_flip()
g
Results - Impact in Economic
Across the United States, which types of events have the greatest economic consequences?
Prepare data for analysis it does the following:
- Grouping by event type and calculate the sum of propdmg together cropdmg.
- Filter out events with no propdmg and cropdmg.
- Sort the table with items with the highest fatalities and injuries in top.
- Selected top 20.
stormByEvent <- stormData %>%
group_by(evtype) %>%
summarise(economic = sum(propdmg + cropdmg)) %>%
filter(economic > 0) %>%
arrange(desc(economic))
stormByEventGraph <- stormByEvent[1:20,]
head(stormByEventGraph)## Source: local data frame [6 x 2]
##
## evtype economic
## 1 TORNADO 3312277
## 2 FLASH FLOOD 1599325
## 3 TSTM WIND 1445168
## 4 THUNDERSTORM WIND 1408614
## 5 HAIL 1268290
## 6 FLOOD 1067976g <- ggplot(stormByEventGraph, aes(x=reorder(evtype, economic), y = economic))
g <- g + geom_bar(stat="identity", colour = "#43e8d8", fill = "#43e8d8")
g <- g + labs(title = "Top Event Types (Greatest Economic Consequences)", x = "Event Type", y = "Economy Damage")
g <- g + theme(plot.title = element_text(color="#666666", face="bold", size=19))
g <- g + theme(axis.title = element_text(color="#666666", face="bold", size=19))
g <- g + coord_flip()
g
Conclusion
From these data, we found that excessive heat and tornado are most harmful with respect to population health, while tornado and flash flood have the greatest economic consequences.