Reproducible Research: Course Project 2: Impact of Severe Weather Conditions on Health and Economic Factors
friedoutkombii
15/06/2017
Synopsis
The dataset used here is the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database which contains events from 1950 to 2011 (Can be found here). This data contains storm event data as well as impact data including injuries, fatalities, property damage and crop damage among others. We will use this data to answer two questions:
Q1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Q2. Across the United States, which types of events have the greatest economic consequences?
The conclusion reached by the analysis is that the weather event with the largest injuries & fatalities is tornadoes. The weather event with the greatest total damage cost worked out to be floods, followed by hurricane and typhoons. Further analysis can be done about these weather types and their impacts but this study is limited in scope.
Data Processing
Load library
library(ggplot2)Loading the data in R Studio
file="C:/Users/Nicholas/Documents/repdata%2Fdata%2FStormData.csv.bz2"
data<- read.csv(bzfile(file),header=TRUE)
dim(data)Looking at the dim(data) we see that we have 37 columns (variables) and 902297 rows or instances
summary(data)## 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) :588295Looking at the summary it is apparent we have too many columns of data to deal with. For the assignment questions we will only need to cherry pick particular columns. These are below:
- EVTYPE as a measure of event type (e.g. tornado, flood, etc.)
- FATALITIES as a measure of harm to human health
- INJURIES as a measure of harm to human health
- PROPDMG as a measure of property damage and hence economic damage in USD
- PROPDMGEXP as a measure of magnitude of property damage (e.g. thousands, millions USD, etc.)
- CROPDMG as a measure of crop damage and hence economic damage in USD
- CROPDMGEXP as a measure of magnitude of crop damage (e.g. thousands, millions USD, etc.)
To make our dataset smaller and easier to work with we will subset this data and name it cutdata.
cutdata<-data[,c('EVTYPE', 'FATALITIES', 'INJURIES', 'PROPDMG', 'PROPDMGEXP','CROPDMG', 'CROPDMGEXP')]Let us check the first 5 rows of cutdata to check it’s accurate.
head(cutdata,5)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0We need to establish what our property and crop damage are in terms of dollars. Right now each is split into two columns. The value, and then a mutiplier indicated by a letter ie. K for thousands, B for billions. The multiplier is contained in columns PROPDMGEXP and CROPDMGEXP respectively. Let us use the function unique to see what entries we have for each column.
unique(cutdata$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Munique(cutdata$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MAs we can see there is a mix of upper & lower case as well empty, numeric and symbols. We would like to remove these and end up with our expense cost. There is no easy way to do this so we will replace each type with the correct exponent.
# Replace missing data in PROPDMG and CROPDMG with 0's
cutdata$PROPDMG[(cutdata$PROPDMG == "")] <- 0
cutdata$CROPDMG[(cutdata$CROPDMG == "")] <- 0
cutdata$PROPDMGEXP <- as.character(cutdata$PROPDMGEXP)
cutdata$CROPDMGEXP <- as.character(cutdata$CROPDMGEXP)
#Replace EXP value with appropriate order of magnitude value.
cutdata$PROPDMGEXP[(cutdata$PROPDMGEXP == "")] <- 0
cutdata$PROPDMGEXP[(cutdata$PROPDMGEXP == "+") | (cutdata$PROPDMGEXP == "-") | (cutdata$PROPDMGEXP == "?")] <- 1
cutdata$PROPDMGEXP[(cutdata$PROPDMGEXP == "h") | (cutdata$PROPDMGEXP == "H")] <- 2
cutdata$PROPDMGEXP[(cutdata$PROPDMGEXP == "k") | (cutdata$PROPDMGEXP == "K")] <- 3
cutdata$PROPDMGEXP[(cutdata$PROPDMGEXP == "m") | (cutdata$PROPDMGEXP == "M")] <- 6
cutdata$PROPDMGEXP[(cutdata$PROPDMGEXP == "B")] <- 9
cutdata$CROPDMGEXP[(cutdata$CROPDMGEXP == "")] <- 0
cutdata$CROPDMGEXP[(cutdata$CROPDMGEXP == "+") | (cutdata$CROPDMGEXP == "-") | (cutdata$CROPDMGEXP == "?")] <- 1
cutdata$CROPDMGEXP[(cutdata$CROPDMGEXP == "h") | (cutdata$CROPDMGEXP == "H")] <- 2
cutdata$CROPDMGEXP[(cutdata$CROPDMGEXP == "k") | (cutdata$CROPDMGEXP == "K")] <- 3
cutdata$CROPDMGEXP[(cutdata$CROPDMGEXP == "m") | (cutdata$CROPDMGEXP == "M")] <- 6
cutdata$CROPDMGEXP[(cutdata$CROPDMGEXP == "B")] <- 9Let’s make sure our two new columns are numeric values
cutdata$PROPDMGEXP<-as.numeric(cutdata$PROPDMGEXP)
cutdata$CROPDMGEXP<-as.numeric(cutdata$CROPDMGEXP)We will create a new column for both PROP/CROP which will take the value to the exponent.
cutdata$PROPDMGcost<-cutdata$PROPDMG*(10^cutdata$PROPDMGEXP)
cutdata$CROPDMGcost<-cutdata$CROPDMG*(10^cutdata$CROPDMGEXP)Taking a look at our data we can see it has the new data filled in for the two cost columns.
head(cutdata,5)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 3 0 0
## 2 TORNADO 0 0 2.5 3 0 0
## 3 TORNADO 0 2 25.0 3 0 0
## 4 TORNADO 0 2 2.5 3 0 0
## 5 TORNADO 0 2 2.5 3 0 0
## PROPDMGcost CROPDMGcost
## 1 25000 0
## 2 2500 0
## 3 25000 0
## 4 2500 0
## 5 2500 0Results (with Analysis)
Q1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health ?
We will look at fatalities & injuries to answer the question. First we will do the FATALITIES. Aggregate the data by event type, this will give the total fatalities for each event.
fatalities<-aggregate(FATALITIES ~ EVTYPE, cutdata, sum)Taking the head of the fatalities data we can see it isn’t in order
head(fatalities,5)## EVTYPE FATALITIES
## 1 HIGH SURF ADVISORY 0
## 2 COASTAL FLOOD 0
## 3 FLASH FLOOD 0
## 4 LIGHTNING 0
## 5 TSTM WIND 0Use the order() function to sort this from highest to lowest by fatalities.Please note the [1:20,] is being used to capture only the top 20 rows, that is the top 20 events by fatalities. If we don’t do this our graph will contain way too many event types to be legible and useful
orderedfatalities<- fatalities[order(-fatalities$FATALITIES),][1:20, ]Let’s double check this by looking at the head.
head(orderedfatalities,5)## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816Last step before graphing is to prep the x axis (EVTYPE) so it shows on the graph in highest to lowest.
orderedfatalities$EVTYPE<-factor(orderedfatalities$EVTYPE,order=TRUE,levels=orderedfatalities$EVTYPE)Now let us plot this graph!
We are now going to do the exact same process for INJURIES. The methodology is the same so will post as a block of code leading up to the plotting.
injuries<-aggregate(INJURIES ~ EVTYPE, cutdata, sum)
orderedinjuries<- injuries[order(-injuries$INJURIES),][1:20, ]
orderedinjuries$EVTYPE<-factor(orderedinjuries$EVTYPE,order=TRUE,levels=orderedinjuries$EVTYPE)Now let us plot this INJURY graph!
Once again TORNADO appears to be the leader of FATALITIES and INJURIES!
Q2. Across the United States, which types of events have the greatest economic consequences?
To answer this we turn our attention to the PROPDMGEXP & CROPDMGEXP columns we created earlier. These contain the damage costs for property and crops for each case. We will first combine these into a new column TOTALEXP.
cutdata$TOTALEXP<-cutdata$PROPDMGcost+cutdata$CROPDMGcostAggregate the data by event type, this will give the total damage cost for each event type.
totalcost<-aggregate(TOTALEXP ~ EVTYPE, cutdata, sum)Use the order function to sort this from highest to lowest by totalcost.Please note the [1:20,] is being used to capture only the top 20 rows, that is the highest 20 costs.
orderedtotalcost<- totalcost[order(-totalcost$TOTALEXP),][1:20, ]Last step before graphing is to prep the x axis (EVTYPE) so it shows on the graph from highest to lowest.
orderedtotalcost$EVTYPE<-factor(orderedtotalcost$EVTYPE,order=TRUE,levels=orderedtotalcost$EVTYPE)Now let us plot this greatest economic consequences graph!
At the conlusion of this study we can see overrall Tornadoes are the greatest cause of injury and death whilst floods are the greatest total cost in damages.