Storm and Wather Events Analysis
Ronaldo Echeverria
21/1/2022
Storm and Wather Events Analysis
The following information is accross to storm and other weather events, between 1950 and 2005.
The information is disponible in this url https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
## Registered S3 methods overwritten by 'tibble':
## method from
## format.tbl pillar
## print.tbl pillar##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combineData
- First step is getting information and uncompress data.
- Second step is put this data into dataframe.
#getting values
temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", temp)
storm <- read.csv(bzfile(temp))
summary(storm)## 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) :588295Calculate property damage and crop damage
- Third step is calculate crop and property economic damage, this operation consist in multiply (prop or crop [Exp]) with (prop or crop [Value])
- Four step is add it all up rows in colums prop_value, crop_value, injuries, fatalities by event type.
#calculate crop and prop value
storm$PROP_VALUE <- ifelse(toupper(storm$PROPDMGEXP) == "K", 1000, ifelse(toupper(storm$PROPDMGEXP) == "M", 1000000, ifelse(toupper(storm$PROPDMGEXP) == "B", 1000000000,0))) * storm$PROPDMG
storm$CROP_VALUE <- ifelse(toupper(storm$CROPDMGEXP) == "K", 1000, ifelse(toupper(storm$CROPDMGEXP) == "M", 1000000, ifelse(toupper(storm$CROPDMGEXP) == "B", 1000000000,0))) * storm$CROPDMG
storm$PROP_VALUE <- storm$PROP_VALUE/1000000
storm$CROP_VALUE <- storm$CROP_VALUE/1000000
#calculate total prop value and getting top 10 by EVTYPE
prop_dmg <- aggregate(PROP_VALUE ~ EVTYPE, data= storm, FUN = sum)
prop_dmg <- arrange(prop_dmg, desc(prop_dmg[,2]))
top10_prop_dmg <- prop_dmg[1:10,]
#calculate total crop value and getting top 10 by EVTYPE
crop_dmg <- aggregate(CROP_VALUE ~ EVTYPE, data= storm, FUN = sum)
crop_dmg <- arrange(crop_dmg, desc(crop_dmg[,2]))
top10_crop_dmg <- crop_dmg[1:10,]
#calculate total fatalities and getting top 10 by EVTYPE
fatalities <- aggregate(FATALITIES ~ EVTYPE, data = storm, FUN = sum)
fatalities <- arrange(fatalities, desc(fatalities[,2]))
top10_fatalities <- fatalities[1:10,]
#calculate total injuries and getting top 10 by EVTYPE
injuries <- aggregate(INJURIES ~ EVTYPE, data = storm, FUN = sum)
injuries <- arrange(injuries, desc(injuries[,2]))
top10_injuries <- injuries[1:10,]Analysis top 10 health damage
This reprentetation is top 10 health damage in injuries and fatalities.
#plot injuries and fatalities
options(scipen=10)
injPlot <- ggplot(top10_injuries, aes(x = reorder(EVTYPE, -INJURIES), y = INJURIES)) +
geom_bar(stat = "identity") +
xlab("Weather Event Type") +
ylab("Injuries") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle('Top 10 Injuries')
fatPlot <- ggplot(top10_fatalities, aes(x = reorder(EVTYPE, -FATALITIES), y = FATALITIES)) +
geom_bar(stat = "identity") +
xlab("Weather Event Type") +
ylab("Fatalities ") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle('Top 10 Fatalities')
grid.arrange(injPlot, fatPlot, nrow = 1)
Analysis top 10 economic damage
This reprentetation is top 10 economic value damage in property and crop.
#plot injuries and fatalities
options(scipen=10)
propPlot <- ggplot(top10_prop_dmg, aes(x = reorder(EVTYPE, -PROP_VALUE), y = PROP_VALUE)) +
geom_bar(stat = "identity") +
xlab("Weather Event Type") +
ylab("Property Damage in Million") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle('Top 10 Property Damage')
cropPlot <- ggplot(top10_crop_dmg, aes(x = reorder(EVTYPE, -CROP_VALUE), y = CROP_VALUE)) +
geom_bar(stat = "identity") +
xlab("Weather Event Type") +
ylab("Crop Damage in Million ") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle('Top 10 Crop Damage')
grid.arrange(propPlot, cropPlot, nrow = 1)