Reproducible Research Peer Assignment2 – by Feng QI
This is the results report for Coursera Course, reproducible research, Peer Assignment 2. The goal of this assignment is to explore the NOAA storm Database, Storm Data, and answer some basic questions about severe weather events. The report has analyzed and answered two questions: (1) Across the United States, which types of events ( as indicated in the EVTYPE variable) are most harmful with respect to population health? (2) Across the United States, which types of events have the greatest economic consequences? The analysis was carried out in R Studio Version 0.98.994. The report shows how the data was processed, how the analysis was conducted, and how the conclusions was drew from the analysis.
Data Processing
Load the download the NOAA storm Data from working directory and summarize the data
myData <- read.table("repdata-data-StormData.csv",sep=",",head=TRUE,na.string="NA")summary(myData)## 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 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :101 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0 :547332
## TSTM WIND :219940 1st Qu.: 0 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0 W : 38446
## TORNADO : 60652 Mean : 1 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1 E : 33178
## FLOOD : 25326 Max. :3749 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 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0 N : 28082
## Median :0 Median : 0 S : 22510
## Mean :0 Mean : 1 W : 20119
## 3rd Qu.:0 3rd Qu.: 0 E : 20047
## Max. :0 Max. :925 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH F
## :499225 Min. : 0.0 Min. : 0 Min. :0
## COUNTYWIDE : 19731 1st Qu.: 0.0 1st Qu.: 0 1st Qu.:0
## SOUTH PORTION : 833 Median : 0.0 Median : 0 Median :1
## NORTH PORTION : 780 Mean : 0.2 Mean : 8 Mean :1
## CENTRAL PORTION: 617 3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.:1
## SPRINGFIELD : 575 Max. :2315.0 Max. :4400 Max. :5
## (Other) :380536 NA's :843563
## MAG FATALITIES INJURIES PROPDMG
## Min. : 0 Min. : 0 Min. : 0.0 Min. : 0
## 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0
## Median : 50 Median : 0 Median : 0.0 Median : 0
## Mean : 47 Mean : 0 Mean : 0.2 Mean : 12
## 3rd Qu.: 75 3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0
## Max. :22000 Max. :583 Max. :1700.0 Max. :5000
##
## PROPDMGEXP CROPDMG CROPDMGEXP WFO
## :465934 Min. : 0.0 :618413 :142069
## K :424665 1st Qu.: 0.0 K :281832 OUN : 17393
## M : 11330 Median : 0.0 M : 1994 JAN : 13889
## 0 : 216 Mean : 1.5 k : 21 LWX : 13174
## B : 40 3rd Qu.: 0.0 0 : 19 PHI : 12551
## 5 : 28 Max. :990.0 B : 9 TSA : 12483
## (Other): 84 (Other): 9 (Other):690738
## STATEOFFIC
## :248769
## TEXAS, North : 12193
## ARKANSAS, Central and North Central: 11738
## IOWA, Central : 11345
## KANSAS, Southwest : 11212
## GEORGIA, North and Central : 11120
## (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) :588294Data Analyzing
From the summary of the data and the information provided by the database website. The data supplys the number of fatalites and the number of injuries for each recorders from 1950 to November 2011. So, we can tell the total number of fataliteis and injuries for each type of events, then we can show which type of events have the greatest economic consequences.
- Calculated the total number of fatalities and injures for each types of events.
library(sqldf)## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: RSQLite.extfunsTotalTable <- sqldf("select EVTYPE as Event_Type, sum(FATALITIES) as Total_Fatality, sum(INJURIES) as Total_Injure from myData group by EVTYPE")## Loading required package: tcltk- Plot the total falities of the top 10 events based on the total fatilities and injures
library(ggplot2)
top10_F <- data.frame(head(TotalTable[order(-TotalTable[,2]),],10))
top10_I <- data.frame(head(TotalTable[order(-TotalTable[,3]),],10))
ggplot(top10_F, aes(x=Event_Type, y=Total_Fatality))+geom_bar(stat="identity")+coord_flip()+ggtitle("Total fatalities of the top 10 most harmful events")
ggplot(top10_I, aes(x=Event_Type, y=Total_Injure))+geom_bar(stat="identity")+coord_flip()+ggtitle("Total fatalities of the top 10 most harmful events")
- Calculate total number of proporty loss and crop loss for each types of events
First, we need to convert the expense to corresponding unit (we are using damage in Million Dollor).
DamageTable <- sqldf("select EVTYPE, PROPDMGEXP, PROPDMG, CASE WHEN PROPDMGEXP = 'M' THEN 1 WHEN PROPDMGEXP = 'K' THEN 0.001 WHEN PROPDMGEXP = 'B' THEN 1000 ELSE 0 END as PROPUNI,CROPDMGEXP, CROPDMG, CASE WHEN CROPDMGEXP = 'M' THEN 1 WHEN CROPDMGEXP = 'K' THEN 0.001 WHEN CROPDMGEXP = 'B' THEN 1000 ELSE 0 END as CROPUNI from myData")Then, we can calculate the total damage cost of each recorder, and get the total damage cost for each type of event
DamageTable$Cost <- DamageTable$PROPDMG*DamageTable$PROPUNI+DamageTable$CROPDMG*DamageTable$CROPUNI
DamageTotal <- sqldf("select EVTYPE as Event_Type, sum(Cost) as Total_Cost from DamageTable group by EVTYPE")Finaly, we selecto the top 10 events based on the total damage cost (property and crop damage), and plot the results.
top10_D <- data.frame(head(DamageTotal[order(-DamageTotal[,2]),],10))
ggplot(top10_D, aes(x=Event_Type, y=Total_Cost))+geom_bar(stat="identity")+coord_flip()+ggtitle("Total damage cost (in Million) of the top 10 \n most harmful events")
Results
Based on the analysis we have done, we could answer the two questions
“Tornado” is the most harmul event in terms of pupulation health, both of fatalities and injuries, across the United States.
“Flood” has the greatest economic consequences among all the events across the United States.