US Weather Events with Severe Health and Financial Impact
Michael Kamfonas
October 22, 2015
Synopsis
This report is a requirement for the Coursera Data Science specialization course: Reproducible Research. The verbosity is intentional, to demonstrate the complete sequence of steps used to produce the results.
The two questions addressed are:
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?
The event types were consolidated to broader groupings. In terms of human fatalities and injuries, tornadoes rank the highest. In terms of property damage, hurricanes and tropical storms rank the highest with over three quarters of the cost attributed to them.
The data was filtered to years 1995-2011 since prior to 1995 reporting appeared incomplete based on event counts.
Data Processing
Storm data was downloaded from https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2, and documentation about this data can be downloaded from here: https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf.
The environment used is:
- Windows 10 Pro 64-bit with 16GB RAM on an Intel(R) Core(TM) i7-2860QM CPU at 2.5 GHz.
- R version 3.2.2 (2015-08-14)
- RStudio Version 0.99.486
#Sys.info()
#R.Version()
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(ggplot2)## Loading required package: ggplot2require(grid) # to use unit() in ggplot## Loading required package: gridThe dataset was uncompressed into a file named repdata_data_StormData.csv using the winZip utility in a 64-bit windows-10 environment, and placed in a project directory. The following steps were used to load a data frame called rawData and perform basic exploratory analysis.
rawData<-read.csv("repdata_data_StormData.csv")
object.size(rawData)## 429322384 bytesnames(rawData)## [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"sapply(rawData[1,],class)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME
## "numeric" "factor" "factor" "factor" "numeric" "factor"
## STATE EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## "factor" "factor" "numeric" "factor" "factor" "factor"
## END_TIME COUNTY_END COUNTYENDN END_RANGE END_AZI END_LOCATI
## "factor" "numeric" "logical" "numeric" "factor" "factor"
## LENGTH WIDTH F MAG FATALITIES INJURIES
## "numeric" "numeric" "integer" "numeric" "numeric" "numeric"
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC
## "numeric" "factor" "numeric" "factor" "factor" "factor"
## ZONENAMES LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## "factor" "numeric" "numeric" "numeric" "numeric" "factor"
## REFNUM
## "numeric"str(rawData)## '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 "","- 1 N Albion",..: 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 "","- .5 NNW",..: 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","$AC",..: 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/ 436774 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(rawData)## 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) :588294An event date (date class) was created based on the beginning date of each event. Also damage estimates were normalized based on the exponent. For numeric exponents we assume a power of 10. Otherwise 0 was used for invalid characters such as “?” “+” etc., 3 for K, 6 for M and 9 for B. By multiplying the amount value to 10 raised to the power of the normalized exponent, the respective damage amounts were derived ready to be summarized.
rawData$evdate<-as.Date(rawData$BGN_DATE,"%m/%d/%Y %H:%M:%S")
rawData$propdmgexp <- mapvalues(toupper(rawData$PROPDMGEXP),
c("K","M","","B","+","?","H","-"),
c( 3 , 6 , 0, 9 , 0 , 0 , 2 , 0 ))
rawData$cropdmgexp <- mapvalues(toupper(rawData$PROPDMGEXP),
c("K","M","","B","+","?","H","-"),
c( 3 , 6 , 0, 9 , 0 , 0 , 2 , 0 ))We generate a summary by event by year and verify that summary events carry no damage, fatality or injury values, so there is no concern of double-counting.
event.agg <- rawData %>%
group_by(EVTYPE,year=format(evdate,"%Y")) %>%
summarize(
evcount = sum(!is.na(EVTYPE)),
propdmg.M=sum(PROPDMG*10^(as.numeric(propdmgexp)-6)),
cropdmg.M=sum(CROPDMG*10^(as.numeric(cropdmgexp)-6)),
fatalities=sum(FATALITIES),
injuries=sum(INJURIES)) %>%
mutate(is.summary = grepl(".*summ.*",EVTYPE,ignore.case=TRUE))
# check if summary events carry counts and amounts
event.agg %>%
group_by(is.summary) %>%
summarize(
propdmg=sum(propdmg.M),
cropdmg=sum(cropdmg.M),
injuries=sum(injuries),
fatalities=sum(fatalities)
)## Source: local data frame [2 x 5]
##
## is.summary propdmg cropdmg injuries fatalities
## (lgl) (dbl) (dbl) (dbl) (dbl)
## 1 FALSE 428224.9 1775227 140528 15145
## 2 TRUE 0.0 0 0 0# Look at the yearly summary of event data collected
sum.yearly.nonsummary <- event.agg %>%
filter(!is.summary) %>%
group_by(year) %>%
summarize(
evcount=sum(evcount),
propdmg=sum(propdmg.M),
cropdmg=sum(cropdmg.M),
injuries=sum(injuries),
fatalities=sum(fatalities)
)
ggplot(data=sum.yearly.nonsummary) +
geom_bar(aes(x=year,y=evcount),fill="blue",stat="identity") +
scale_x_discrete(breaks=seq(1950,2010,5)) +
ggtitle("Weather Events Recorded by Year")
# exclude summary events and years prior to 1994
sum.evtypes <- event.agg %>%
filter(!is.summary) %>%
filter(year > 1994 ) %>%
group_by(EVTYPE) %>%
summarize(
evcount=sum(evcount),
propdmg=sum(propdmg.M),
cropdmg=sum(cropdmg.M),
injuries=sum(injuries),
fatalities=sum(fatalities)
)The completeness of events reported has improved in recent years, so we limit our estimates to the range of years 1995-2011. As evidenced by the plethora and apparent redundancy of event types (EVTYPE) a better organization of the events is required. As an example, hail is broken down based on its size and similar classifications exist for thunderstorms, tornadoes and many other events. We organize these events using a classifier we call supertype so that we can more reliably summarize and compare the data.
We follow the following process:
- All event type comparisons ignore case
- We create an event super-type with a normalized name and populate it for the most significant of the 732 events, based on certain keywords (e.g. HAIL, THUNDER, TORNADO, WINDS etc.) that appear in the EVTYPEs of observations of interest.
- Made subtle adjustments to the rules were made to ensure the top event categories of interest are fairly accurately classified.
evTypes<-toupper(sum.evtypes$EVTYPE)
length(evTypes)## [1] 732sum.evsupertypes <- sum.evtypes %>%
mutate(evsupertype = ifelse(grepl(".*(BLIZZ|SNOW|ICE).*",EVTYPE,ignore.case = TRUE)
,"BLIZARD/SNOW/ICE",NA)) %>%
mutate(evsupertype = ifelse(grepl(".*TORNAD.*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"TORNADO",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*WATERSP.*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"WATERSPOUT",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*THUNDERS.*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"THUNDERSTORM",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*HAIL.*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"HAIL",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*WIND.*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"WIND",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*FLOOD.*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"FLOOD",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(RECORD|UNSEASON|ABNORMA).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"EXTREME CONDITIONS",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(VOLCAN).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"VOLCANIC EVENT",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(FLOOD).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"FLOOD",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(LIGHTN).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"LIGHTNING",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(FIRE).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"FIRE",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(FREEZIN).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"FREEZING RAIN/FOG",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(WAVE|SURF).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"WAVE/SURF",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(HURRIC|TROPIC|STORM).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"STORM/HURRICANE",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*( RIP |CURRENT).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"RIP CURRENT",evsupertype )) %>%
mutate(evsupertype = ifelse(grepl(".*(FREEZE|COLD|ICY|HYPOTHERM).*",EVTYPE,ignore.case = TRUE)
& is.na(evsupertype)
,"FREEZE/COLD",evsupertype ))
S0<- sum.evsupertypes %>%
mutate(evsupertype = ifelse(is.na(evsupertype)
,as.character(EVTYPE),evsupertype )) %>%
summarize(
dmg.B.USD=sum(propdmg+cropdmg)/1000,
injuries=sum(injuries),
fatalities=sum(fatalities),
casualties=sum(fatalities+injuries)
)
S1<- sum.evsupertypes %>%
mutate(evsupertype = ifelse(is.na(evsupertype)
,as.character(EVTYPE),evsupertype )) %>%
group_by(evsupertype) %>%
summarize(
# evcount=sum(evcount),
# propdmg=sum(propdmg),
# cropdmg=sum(cropdmg),
dmg.B.USD=sum(propdmg+cropdmg)/1000,
injuries=sum(injuries),
fatalities=sum(fatalities),
casualties=sum(fatalities+injuries)
) %>%
arrange(desc(casualties)) %>%
# slice(1:15)
top_n(10,casualties)
S10 <- S1 %>%
summarize(
dmg.B.USD=sum(dmg.B.USD),
injuries=sum(injuries),
fatalities=sum(fatalities),
casualties=sum(casualties)
)
S10<- S0-S10
S10$evsupertype = "ALL OTHER"
S1 <- rbind(S1,S10)S2<- sum.evsupertypes %>%
mutate(evsupertype = ifelse(is.na(evsupertype)
,as.character(EVTYPE),evsupertype )) %>%
group_by(evsupertype) %>%
summarize(
# evcount=sum(evcount),
# propdmg=sum(propdmg),
# cropdmg=sum(cropdmg),
dmg.B.USD=sum(propdmg+cropdmg)/1000,
injuries=sum(injuries),
fatalities=sum(fatalities),
casualties=sum(fatalities+injuries)
) %>%
arrange(desc(dmg.B.USD)) %>%
# slice(1:15)
top_n(4,dmg.B.USD)
S20 <- S2 %>%
summarize(
dmg.B.USD=sum(dmg.B.USD),
injuries=sum(injuries),
fatalities=sum(fatalities),
casualties=sum(fatalities+injuries)
)
S20<- S0-S20
S20$evsupertype = "ALL OTHER"
S2 <- rbind(S2,S20)
E1 <- sum.evsupertypes[is.na(sum.evsupertypes$evsupertype),] %>%
filter(fatalities+injuries+propdmg+cropdmg > 0) %>%
group_by(EVTYPE)Results
The following table shows the top 10 weather events in the US from 1995 to 2011 in terms of fatalities and injuries. The table is followed by a pie-chart demonstrating the proportion of these casualties.
require(xtable)## Loading required package: xtabletab.print<-xtable(S1,label="Events with Highest Human Casualties",caption="Event Types with Highest human casualties")
print(tab.print,type="html", floating=FALSE)| evsupertype | dmg.B.USD | injuries | fatalities | casualties | |
|---|---|---|---|---|---|
| 1 | TORNADO | 50.84 | 21787.00 | 1545.00 | 23332.00 |
| 2 | FLOOD | 281.43 | 8519.00 | 1386.00 | 9905.00 |
| 3 | EXCESSIVE HEAT | 0.01 | 6525.00 | 1903.00 | 8428.00 |
| 4 | WIND | 35.15 | 5324.00 | 899.00 | 6223.00 |
| 5 | LIGHTNING | 1.10 | 4632.00 | 730.00 | 5362.00 |
| 6 | STORM/HURRICANE | 1684.91 | 3427.00 | 423.00 | 3850.00 |
| 7 | HEAT | 0.00 | 2030.00 | 924.00 | 2954.00 |
| 8 | THUNDERSTORM | 11.42 | 1950.00 | 182.00 | 2132.00 |
| 9 | BLIZARD/SNOW/ICE | 5.89 | 1596.00 | 310.00 | 1906.00 |
| 10 | FIRE | 15.03 | 1458.00 | 87.00 | 1545.00 |
| 11 | ALL OTHER | 39.30 | 5207.00 | 1834.00 | 7041.00 |
y.breaks <- cumsum(S1$casualties) - S1$casualties/2
y.breaks## [1] 11666.0 28284.5 37451.0 44776.5 50569.0 55175.0 58577.0 61120.0
## [9] 63139.0 64864.5 69157.5ggplot(S1, aes(x=factor(1),casualties , fill=evsupertype)) +geom_bar(stat="identity", width=1) +
coord_polar(theta = "y") +
#geom_bar(stat="identity", color='black') + # black border around pie slices
#scale_fill_brewer(palette = "Set3") + # override default color palette
theme(axis.text.x=element_text(color='black' # axis text style
,size=8)) +
theme(axis.ticks=element_blank() # the axis ticks
,axis.title=element_blank() # the axis labels
,axis.text.y=element_blank() # the numeric labels.
) +
theme(legend.position="right") + # position bottom,left,right etc.
#theme(plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "in")) +
#guides(fill=guide_legend(override.aes=list(colour=NA))) + # remove diagonal from legend
scale_y_continuous(
breaks=y.breaks, # where to place the labels
labels=S1$evsupertype
) +
ggtitle("1995-2011 Most Costly Storm Events\nTop 10 in Combined Injuries and fatalities")
The table that follows summarizes weather event groupings in terms of property and crop damage recorded from 1995 to 2011 in the US. A pie-chart showing the same breakdown follows.
require(xtable)
tab.print<-xtable(S2,label="Events Causing Highest Damages",caption="Event Types causing Highest damage")
print(tab.print,type="html", floating=FALSE)| evsupertype | dmg.B.USD | injuries | fatalities | casualties | |
|---|---|---|---|---|---|
| 1 | STORM/HURRICANE | 1684.91 | 3427.00 | 423.00 | 3850.00 |
| 2 | FLOOD | 281.43 | 8519.00 | 1386.00 | 9905.00 |
| 3 | TORNADO | 50.84 | 21787.00 | 1545.00 | 23332.00 |
| 4 | WIND | 35.15 | 5324.00 | 899.00 | 6223.00 |
| 5 | ALL OTHER | 72.75 | 23398.00 | 5970.00 | 29368.00 |
y.breaks <- cumsum(S2$dmg.B.USD) - S2$dmg.B.USD/2
y.breaks## [1] 842.4559 1825.6281 1991.7620 2034.7523 2088.6990ggplot(S2, aes(x=factor(1),dmg.B.USD , fill=evsupertype)) +geom_bar(stat="identity", width=1) +
coord_polar(theta = "y") +
# geom_bar(stat="identity", color='black') +
#scale_fill_brewer(palette = "Set3") +
theme(axis.text.x=element_text(color='black' # axis text style
,size=8)) +
theme(axis.ticks=element_blank() # the axis ticks
,axis.title=element_blank() # the axis labels
,axis.text.y=element_blank() # the 0.75, 1.00, 1.25 labels.
,legend.position="right") +
# theme(plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "in")) +
# guides(fill=guide_legend(override.aes=list(colour=NA))) + # remove diagonal from legend
# scale_y_continuous(
# breaks=y.breaks, # where to place the labels
# labels=S2$evsupertype # the labels
# ) +
ggtitle("1995-2011 Reported Severe Weather Events\nTop Causes of Property and Crop Damage (Billion USD)")