StormData - Reproducible Research Peer Assessment 2
Richard Ellison
Tuesday, June 16, 2015
U.S. National Oceanic and Atmospheric Administration (NOAA) storm database 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 data analysis addresses the following questions:
Across the United States, which types of events are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?Data Processing
Read the Storm Data from NOAA
download.file(url="http://d396qusza40orc.cloudfront.net/repdata/data/StormData.csv.bz2",
destfile="C:/Users/rellison/Documents/Coursera/StormData/StormData.csv.bz2")
StormData <- read.csv(bzfile("C:/Users/rellison/Documents/Coursera/StormData/StormData.csv.bz2"))investigate the data
head(StormData)## 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
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6summary(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 : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295Subset the data to retain only the fields needed for the analysis
FieldNames <- c("STATE","EVTYPE","FATALITIES","INJURIES","PROPDMG","PROPDMGEXP","CROPDMG","CROPDMGEXP")
WorkData1 <- StormData[FieldNames]
#rm(StormData)Filter the records to include only valid states in the US
WorkData2 <- WorkData1[WorkData1$STATE %in% state.abb,]
#rm(WorkdData1)Handle the property damage exponent values and calculate dollar values
unique(WorkData2$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m MWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "K"] <- 1000## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "K", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "M"] <- 1e+06## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "M", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == ""] <- 1
WorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "B"] <- 1e+09## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "B", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "m"] <- 1e+06## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "m", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "+"] <- 0
WorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "0"] <- 1
WorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "5"] <- 1e+05## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "5", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "6"] <- 1e+06## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "6", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "?"] <- 0
WorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "4"] <- 10000## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "4", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "2"] <- 100## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "2", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "3"] <- 1000## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "3", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "h"] <- 100## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "h", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "7"] <- 1e+07## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "7", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "H"] <- 100## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "H", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "-"] <- 0
WorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "1"] <- 10## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "1", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "8"] <- 1e+08## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "8", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "M"] <- 1e+06## Warning in `[<-.factor`(`*tmp*`, WorkData2$PROPDMGEXP == "M", value =
## structure(c(NA, : invalid factor level, NA generatedWorkData2$PROPDMGEXP[WorkData2$PROPDMGEXP == "NA"] <- 1
WorkData2$PROPDMGEXP[is.na(WorkData2$PROPDMGEXP)] <- 1
WorkData2$PROPDMGEXP <- as.numeric(WorkData2$PROPDMGEXP)
WorkData2$PROPDMGAMT <- WorkData2$PROPDMG * WorkData2$PROPDMGEXPHandle the crop damage exponent values and calculate dollar values
unique(WorkData2$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "?"] <- 0
WorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "0"] <- 1## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "0", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "2"] <- 100## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "2", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "B"] <- 1e+09## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "B", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "k"] <- 1000## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "k", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "K"] <- 1000## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "K", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "m"] <- 1e+06## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "m", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "M"] <- 1e+06## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "M", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[WorkData2$CROPDMGEXP == "NA"] <- 1## Warning in `[<-.factor`(`*tmp*`, WorkData2$CROPDMGEXP == "NA", value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP[is.na(WorkData2$CROPDMGEXP)] <- 1## Warning in `[<-.factor`(`*tmp*`, is.na(WorkData2$CROPDMGEXP), value =
## structure(c(1L, : invalid factor level, NA generatedWorkData2$CROPDMGEXP <- as.numeric(WorkData2$CROPDMGEXP)
WorkData2$CROPDMGAMT <- WorkData2$CROPDMG * WorkData2$CROPDMGEXP
# Create combined fields for aggregation
WorkData2$FATALPLUSINJURY <-WorkData2$FATALITIES + WorkData2$INJURIES
WorkData2$CRPPRPDMGAMT <- WorkData2$PROPDMGAMT + WorkData2$CROPDMGAMT** Aggregate the data for health and economic damage**
HealthAgg <- aggregate(FATALPLUSINJURY ~ EVTYPE, data=WorkData2, FUN=sum)
EconomicAgg <- aggregate(CRPPRPDMGAMT ~ EVTYPE, data=WorkData2, FUN=sum)** Get the top 10 each of health and economic risks**
Health10 <- HealthAgg[order(HealthAgg$FATALPLUSINJURY, decreasing=TRUE),][1:10,]
Economic10 <- EconomicAgg[order(EconomicAgg$CRPPRPDMGAMT, decreasing=TRUE),][1:10,]RESULTS
Question 1: Across the United States, which types of events are most harmful with respect to population health?
** Plot the top 10 causes of fatalities and injuries**
options("scipen"=999)
par(mar=c(12,4,3,2))
barplot(Health10$FATALPLUSINJURY, las=3, names.arg=Health10$EVTYPE,
main="Top 10 Events with Greatest Health Damages",
ylab="Number of Fatalities and Injuries",
col="green")
By far, the most important cause of fatalities and injuries are Tornadoes. These are followed at much lower totals by Excessive Heat, Thunderstorm Wind, Flood and Lightning.
Question 2: Across the United States, which types of events have the greatest economic consequences?
** Plot the top 10 events of property/crop damage**
par(mar=c(12,4,3,2))
barplot(Economic10$CRPPRPDMGAMT, las=3, names.arg=Economic10$EVTYPE,
main="Top 10 Events with Greatest Economic Impacts",
ylab="Economic impacts ($)",
col="green")