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.
The events in the database start in the year 1950 and end in November 2011.
The analysis in this document try to respond with tables and graphs at two questions:
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?


1. Data Processing

The data for the assignment can be downloaded here.

1.1 Settings
Load data and required libraries.

storm <- read.csv("repdata%2Fdata%2FStormData.csv")
library(dplyr)
stormDP <- tbl_df(storm)
library(ggplot2)
library(xtable)


1.2 Table summary for find the most harmful events with respect to population health.
Injuries and fatalities are the variables considerated for this part of the analysis. Tables with summaries are created with this new variables for each event: Num (total number), Fatalities, Injuries, FatalitiesAVG (average number of fatalities), InjuriesAVG, PercWithFatalities (percentage of events with at least one dead) PercWithInjuries (percentage of events with at least one injury).

# table with total injuries and fatalities for event
StormSummary <- stormDP %>% group_by(EVTYPE) %>% summarize(Num=n(),Fatalities=sum(FATALITIES),Fatalities_AVG=round(mean(FATALITIES),2),Injuries=sum(INJURIES),Injuries_AVG=round(mean(INJURIES),2))
# tables with events with at least one injury / death
WithInjuriesTB <- stormDP %>% filter(INJURIES>0) %>% group_by(EVTYPE) %>% summarize(WithInjuries=n())
WithDeadsTB <- stormDP %>% filter(FATALITIES>0) %>% group_by(EVTYPE) %>% summarize(WithDeads=n())
# join with summary table
StormSummary <- left_join(StormSummary,WithInjuriesTB, by="EVTYPE")
StormSummary <- left_join(StormSummary,WithDeadsTB, by="EVTYPE")
# percentage with at least one injury / fatality
StormSummary <- mutate(StormSummary, Perc_with_Injuries=round(WithInjuries/Num*100,2))
StormSummary <- mutate(StormSummary, Perc_with_Fatalities=round(WithDeads/Num*100,2))
# final summary table for the analysis
StormSummary2 <- StormSummary %>% select(EVTYPE,Num,Fatalities,Fatalities_AVG,Perc_with_Fatalities,Injuries,Injuries_AVG,Perc_with_Injuries) %>% arrange(desc(Num))


1.3 Table summary for find the events that have the greatest economic consequences.
Property and crop damage exponents for each level is listed out and assigned those values for the property exponent data. Invalid data was excluded. Property damage value was calculated by multiplying the property damage and property exponent value. Total damages are the final variable that sum property and crop damages.

# values of PROPDMGEXP
unique(stormDP$PROPDMGEXP)
##  [1] K M   B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels:  - ? + 0 1 2 3 4 5 6 7 8 B h H K m M
# traduction of PROPDMGEXP
stormDP$PropExpN <- 0
stormDP$PropExpN[stormDP$PROPDMGEXP == ""] <- 1
stormDP$PropExpN[stormDP$PROPDMGEXP == "-"] <- 0
stormDP$PropExpN[stormDP$PROPDMGEXP == "?"] <- 0
stormDP$PropExpN[stormDP$PROPDMGEXP == "+"] <- 0
stormDP$PropExpN[stormDP$PROPDMGEXP == "0"] <- 1
stormDP$PropExpN[stormDP$PROPDMGEXP == "1"] <- 10
stormDP$PropExpN[stormDP$PROPDMGEXP == "2"] <- 100
stormDP$PropExpN[stormDP$PROPDMGEXP == "3"] <- 1000
stormDP$PropExpN[stormDP$PROPDMGEXP == "4"] <- 10000
stormDP$PropExpN[stormDP$PROPDMGEXP == "5"] <- 100000
stormDP$PropExpN[stormDP$PROPDMGEXP == "6"] <- 1000000
stormDP$PropExpN[stormDP$PROPDMGEXP == "7"] <- 10000000
stormDP$PropExpN[stormDP$PROPDMGEXP == "8"] <- 100000000
stormDP$PropExpN[stormDP$PROPDMGEXP == "B"] <- 1000000000
stormDP$PropExpN[stormDP$PROPDMGEXP == "h"] <- 100
stormDP$PropExpN[stormDP$PROPDMGEXP == "H"] <- 100
stormDP$PropExpN[stormDP$PROPDMGEXP == "K"] <- 1000
stormDP$PropExpN[stormDP$PROPDMGEXP == "m"] <- 1000000
stormDP$PropExpN[stormDP$PROPDMGEXP == "M"] <- 1000000
# Final value for property damages
stormDP$PropDMGN <- stormDP$PROPDMG*stormDP$PropExpN
# values of CROPDMGEXP
unique(stormDP$CROPDMGEXP)
## [1]   M K m B ? 0 k 2
## Levels:  ? 0 2 B k K m M
# traduction of CROPDMGEXP
stormDP$CropExpN <- 0
stormDP$CropExpN[stormDP$CROPDMGEXP == ""] <- 1
stormDP$CropExpN[stormDP$CROPDMGEXP == "?"] <- 0
stormDP$CropExpN[stormDP$CROPDMGEXP == "0"] <- 1
stormDP$CropExpN[stormDP$CROPDMGEXP == "2"] <- 100
stormDP$CropExpN[stormDP$CROPDMGEXP == "B"] <- 1000000000
stormDP$CropExpN[stormDP$CROPDMGEXP == "k"] <- 1000
stormDP$CropExpN[stormDP$CROPDMGEXP == "K"] <- 1000
stormDP$CropExpN[stormDP$CROPDMGEXP == "m"] <- 1000000
stormDP$CropExpN[stormDP$CROPDMGEXP == "M"] <- 1000000
# Final value for crop damages
stormDP$CropDMGN <- stormDP$CROPDMG*stormDP$CropExpN
# summary table for this analysis
StormSummary3 <- stormDP %>% group_by(EVTYPE) %>% summarize(PropDam=round(sum(PropDMGN),2),PropDam_AVG=round(mean(PropDMGN),2),CropDam=sum(CropDMGN),CropDam_AVG=round(mean(CropDMGN),2), TotalDamages = round(sum(PropDMGN)+sum(CropDMGN),2), TotalDamages_AVG = round(mean(sum(PropDMGN)+sum(CropDMGN)),2)) %>% arrange(desc(TotalDamages))



2. Results

2.1 The most harmful events with respect to population health.
The table and the graph below show the events with the large number of fatalities.

# top 20 for fatalities
print(xtable(as.data.frame(StormSummary2 %>% arrange(desc(Fatalities)))[1:20, ], auto = TRUE, caption='Top 20 events for number of fatalities'),type='html')
Top 20 events for number of fatalities
EVTYPE Num Fatalities Fatalities_AVG Perc_with_Fatalities Injuries Injuries_AVG Perc_with_Injuries
1 TORNADO 60652 5633 0.09 2.64 91346 1.51 12.70
2 EXCESSIVE HEAT 1678 1903 1.13 34.39 6525 3.89 9.83
3 FLASH FLOOD 54277 978 0.02 1.17 1777 0.03 0.70
4 HEAT 767 937 1.22 23.21 2100 2.74 6.00
5 LIGHTNING 15754 816 0.05 4.82 5230 0.33 17.83
6 TSTM WIND 219940 504 0.00 0.18 6957 0.03 1.21
7 FLOOD 25326 470 0.02 1.18 6789 0.27 0.61
8 RIP CURRENT 470 368 0.78 69.57 232 0.49 25.32
9 HIGH WIND 20212 248 0.01 0.91 1137 0.06 2.03
10 AVALANCHE 386 224 0.58 45.08 170 0.44 27.72
11 WINTER STORM 11433 206 0.02 1.11 1321 0.12 1.33
12 RIP CURRENTS 304 204 0.67 59.54 297 0.98 28.29
13 HEAT WAVE 74 172 2.32 35.14 309 4.18 12.16
14 EXTREME COLD 655 160 0.24 16.49 231 0.35 2.29
15 THUNDERSTORM WIND 82563 133 0.00 0.13 1488 0.02 0.74
16 HEAVY SNOW 15708 127 0.01 0.58 1021 0.06 0.83
17 EXTREME COLD/WIND CHILL 1002 125 0.12 8.68 24 0.02 0.90
18 STRONG WIND 3566 103 0.03 2.52 280 0.08 4.18
19 BLIZZARD 2719 101 0.04 2.21 805 0.30 1.77
20 HIGH SURF 725 101 0.14 9.24 152 0.21 5.10 
# modification for the graph label
levels(StormSummary2$EVTYPE) <- gsub(" ", "\n",levels(StormSummary2$EVTYPE))
# desc order for the first graph
StormSummary2$EVTYPE <- factor(StormSummary2$EVTYPE, levels = StormSummary2$EVTYPE[order(StormSummary2$Fatalities, decreasing=TRUE)])
# graph with fatalities per event
g <- ggplot(head(as.data.frame(StormSummary2),n=8), aes(EVTYPE, Fatalities))
g+geom_bar(stat='identity')+labs(title="Top weather events for number of fatalities", x="Event",y="Fatalities")


The table and the graph below show the events with the large number of injuries.

# top 20 for injuries
print(xtable(as.data.frame(StormSummary2 %>% arrange(desc(Injuries)))[1:20, ], auto = TRUE, caption='Top 20 events for number of injuries'),type='html')
Top 20 events for number of injuries
EVTYPE Num Fatalities Fatalities_AVG Perc_with_Fatalities Injuries Injuries_AVG Perc_with_Injuries
1 TORNADO 60652 5633 0.09 2.64 91346 1.51 12.70
2 TSTM WIND 219940 504 0.00 0.18 6957 0.03 1.21
3 FLOOD 25326 470 0.02 1.18 6789 0.27 0.61
4 EXCESSIVE HEAT 1678 1903 1.13 34.39 6525 3.89 9.83
5 LIGHTNING 15754 816 0.05 4.82 5230 0.33 17.83
6 HEAT 767 937 1.22 23.21 2100 2.74 6.00
7 ICE STORM 2006 89 0.04 2.84 1975 0.98 3.09
8 FLASH FLOOD 54277 978 0.02 1.17 1777 0.03 0.70
9 THUNDERSTORM WIND 82563 133 0.00 0.13 1488 0.02 0.74
10 HAIL 288661 15 0.00 0.00 1361 0.00 0.10
11 WINTER STORM 11433 206 0.02 1.11 1321 0.12 1.33
12 HURRICANE/TYPHOON 88 64 0.73 21.59 1275 14.49 13.64
13 HIGH WIND 20212 248 0.01 0.91 1137 0.06 2.03
14 HEAVY SNOW 15708 127 0.01 0.58 1021 0.06 0.83
15 WILDFIRE 2761 75 0.03 1.01 911 0.33 6.66
16 THUNDERSTORM WINDS 20843 64 0.00 0.24 908 0.04 1.55
17 BLIZZARD 2719 101 0.04 2.21 805 0.30 1.77
18 FOG 538 62 0.12 6.88 734 1.36 14.50
19 WILD/FOREST FIRE 1457 12 0.01 0.62 545 0.37 8.92
20 DUST STORM 427 22 0.05 2.11 440 1.03 10.30 
# desc order for the second graph
StormSummary2$EVTYPE <- factor(StormSummary2$EVTYPE, levels = StormSummary2$EVTYPE[order(StormSummary2$Injuries, decreasing=TRUE)])
# graph with injuries per event
g2 <- ggplot(head(as.data.frame(StormSummary2),n=8), aes(EVTYPE, Injuries))
g2+geom_bar(stat='identity')+labs(title="Top weather events for number of injuries", x="Event",y="Injuries")

Based on the data, TORNADO caused the maximum number of fatalities and injuries, and for this reason it’s the most harmful with respect to population health.


2.2 The events that have the greatest economic consequences.

# top 20 for damages
print(xtable(as.data.frame(StormSummary3)[1:20, ], digits=0, auto = TRUE, caption='Top 20 events for economic damages'),type='html')
## Warning in formatC(x = c(5661968450, 2607872800, 414953270, 5000,
## 3025954473, : NAs introduced by coercion to integer range
Top 20 events for economic damages
EVTYPE PropDam PropDam_AVG CropDam CropDam_AVG TotalDamages TotalDamages_AVG
1 FLOOD 144657709807 5711826 NA 223563 150319678257 150319678257
2 HURRICANE/TYPHOON 69305840000 787566364 NA 29634918 71913712800 71913712800
3 TORNADO 56947380617 938920 414953270 6842 57362333887 57362333887
4 STORM SURGE 43323536000 165990559 5000 19 43323541000 43323541000
5 HAIL 15735267513 54511 NA 10483 18761221986 18761221986
6 FLASH FLOOD 16822673979 309941 1421317100 26186 18243991079 18243991079
7 DROUGHT 1046106000 420461 NA 5615983 15018672000 15018672000
8 HURRICANE 11868319010 68208730 NA 15758103 14610229010 14610229010
9 RIVER FLOOD 5118945500 29589280 NA 29072017 10148404500 10148404500
10 ICE STORM 3944927860 1966564 NA 2503546 8967041360 8967041360
11 TROPICAL STORM 7703890550 11165059 678346000 983110 8382236550 8382236550
12 WINTER STORM 6688497251 585017 26944000 2357 6715441251 6715441251
13 HIGH WIND 5270046260 260738 638571300 31594 5908617560 5908617560
14 WILDFIRE 4765114000 1725865 295472800 107017 5060586800 5060586800
15 TSTM WIND 4484928495 20392 554007350 2519 5038935845 5038935845
16 STORM SURGE/TIDE 4641188000 31359378 850000 5743 4642038000 4642038000
17 THUNDERSTORM WIND 3483122472 42187 414843050 5025 3897965522 3897965522
18 HURRICANE OPAL 3172846000 352538444 19000000 2111111 3191846000 3191846000
19 WILD/FOREST FIRE 3001829500 2060281 106796830 73299 3108626330 3108626330
20 HEAVY RAIN/SEVERE WEATHER 2500000000 1250000000 0 0 2500000000 2500000000 
# modification for the graph label
levels(StormSummary3$EVTYPE) <- gsub(" ", "\n",levels(StormSummary3$EVTYPE))
# desc order for the graph
StormSummary3$EVTYPE <- factor(StormSummary3$EVTYPE, levels = StormSummary3$EVTYPE[order(StormSummary3$TotalDamages, decreasing=TRUE)])
# graph with damages per event
h <- ggplot(head(as.data.frame(StormSummary3),n=8), aes(EVTYPE, TotalDamages/1000000000))
h+geom_bar(stat='identity')+labs(title="Top weather events for damages (billions of dollars)", x="Event",y="Total Damages (billions of dollars)")

Based on the data, FLOOD have the greatest economic consequences.