Reproducible Research Week4

Size: px

Start display at page:

Download "Reproducible Research Week4"

Gervase Sutton
5 years ago
Views:

1 Page 1 of 10 Reproducible Research Week4 Assignment Madhu Lakshmikanthan July 31, 2016 This paper analyzes NOAA storm data from 1950 to November, 2011 obtained from Storm Data ( 2Fdata%2FStormData.csv.bz2.) The analysis below finds the worst 6 event types in terms of property damage, crop damage, fatalities and injuries. Also, the event that caused the worst property damage, crop damage, fatalities and injuries respectively is reported. Loading and caching data download.file(" a.csv.bz2","stormdata.bz2") stormdata <- read.table("stormdata.bz2",header = TRUE, sep=",", quote="\"") # create a PROPDMGVAL column. stormdata$propdmgval <- stormdata$propdmg This section answers this question: Across the United States, which types of events have the greatest economic consequences?

2 Page 2 of 10 The code below gets the top 1% of property/crop damages, groups and totals by event type. Next, it gets the top 6 total property/crop damages and plots it against the corresponding events. The code also shows the details of the event that cause the worst property/crop damage ## ## 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

3 Page 3 of 10 # PROPERTY DAMAGE createvalcol <- Vectorize(function(a,b){switch(as.character(a),"h"=b*100,"H"=b* 100,"k"=b*1000,"K"=b*1000, "m"=b* , "M"=b* ,"b"=b* , "B"=b * , "+"=b, "-"=0,"?"=0, "0"=b*10,"1"=b*10, "2"=b*10,"3"=b*10,"4"=b*10,"5"=b*10,"6"=b*10,"7"=b*10,"8"=b*10,0)}) stormdata$propdmgval <- createvalcol(stormdata$propdmgexp,stormdata$propdmg) # filter to get the top 1% of the data in terms of property damage toponepercentpropdmg <- filter(stormdata,stormdata$propdmgval >= quantile(unlis t(stormdata$propdmgval),c(.25,.5,.9,.99))[4]) # group by event groupbyevt <- group_by(toponepercentpropdmg,evtype) # get the top 6 events that caused most property damage summprop <- arrange(summarize(groupbyevt,totalpropdmg=sum(propdmgval,na.rm=tru E)),desc(TOTALPROPDMG))[1:6,] # find the event that cause the most property damage mostpropdmg <- filter(stormdata,stormdata$propdmgval >= max(stormdata$propdmgva L)) dispcols <- names(mostpropdmg) %in% c("evtype","bgn_date","bgn_time", "END_DAT E", "END_TIME","COUNTYNAME","STATE","PROPDMG","PROPDMGEXP") disppropdmg <- mostpropdmg[dispcols] # display the row with details on the event that caused most property damage wi th other details disppropdmg ## BGN_DATE BGN_TIME COUNTYNAME STATE EVTYPE END_DATE ## 1 1/1/2006 0:00:00 12:00:00 AM NAPA CA FLOOD 1/1/2006 0:00:00 ## END_TIME PROPDMG PROPDMGEXP ## 1 07:00:00 AM 115 B

4 Page 4 of 10 EVENT CAUSING THE WORST PROPERTY DAMAGE in US$ (m/m:millions, b/b:billions) # 2 rows in panel par(mfrow = c(2, 1), mar = c(3, 7, 2, 1), oma = c(1, 1, 1, 1)) plot(unclass(summprop$evtype),summprop$totalpropdmg,type="h",col="red",lwd=10,x axt="n",las=2,ylab="",xlab="events",main="figure 1.1: Top 6 Event types by the extent of property damage") axis(1,at=unclass(summprop$evtype),labels=unclass(summprop$evtype),cex.axis=1,l as=2) title(ylab="property Damage",line=5) legend("topright", pch = 1,ncol=2,cex=0.75, legend = c(paste(unclass(summprop$e VTYPE)[1],summprop$EVTYPE[1],sep=":"), paste(unclass(summprop$evtype)[2],summprop$evtype[2],sep=":"),paste(unclass(sum mprop$evtype)[3],summprop$evtype[3],sep=":"), paste(unclass(summprop$evtype)[4],summprop$evtype[4],sep=":"),paste(unclass(sum mprop$evtype)[5],summprop$evtype[5],sep=":"), paste(unclass(summprop$evtype)[6],summprop$evtype[6],sep=":")) ) # CROP DAMAGE stormdata$cropdmgval <- createvalcol(stormdata$cropdmgexp,stormdata$cropdmg) # filter to get the top 1% of the data in terms of crop damage toponepercentcropdmg <- filter(stormdata,stormdata$cropdmgval >= quantile(unlis t(stormdata$cropdmgval),c(.25,.5,.9,.99))[4]) # group by event groupbyevt <- group_by(toponepercentcropdmg,evtype) # get the top 6 events that caused most crop damage summcrop <- arrange(summarize(groupbyevt,totalcropdmg=sum(cropdmgval,na.rm=tru E)),desc(TOTALCROPDMG))[1:6,] plot(unclass(summcrop$evtype),summcrop$totalcropdmg,xaxt="n",las=2,type="h",col ="red",lwd=10,ylab="",xlab="events",main="figure 1.2: Top 6 Event types by the extent of crop damage") axis(1,at=unclass(summcrop$evtype),labels=unclass(summcrop$evtype),cex.axis=1,l as=2) title(ylab="crop Damage",line=5) legend("topright", pch = 1, ncol=2,cex=0.75,legend = c(paste(unclass(summcrop$e VTYPE)[1],summcrop$EVTYPE[1],sep=":"), paste(unclass(summcrop$evtype)[2],summcrop$evtype[2],sep=":"),paste(unclass(sum mcrop$evtype)[3],summcrop$evtype[3],sep=":"), paste(unclass(summcrop$evtype)[4],summcrop$evtype[4],sep=":"),paste(unclass(sum mcrop$evtype)[5],summcrop$evtype[5],sep=":"), paste(unclass(summcrop$evtype)[6],summcrop$evtype[6],sep=":")) )

5 Page 5 of 10 # find the event that cause the most crop damage mostcropdmg <- filter(stormdata,stormdata$cropdmgval >= max(stormdata$cropdmgva L)) dispcols <- names(mostcropdmg) %in% c("evtype","bgn_date","bgn_time", "END_DAT E", "END_TIME","COUNTYNAME","STATE","CROPDMG","CROPDMGEXP") dispcropdmg <- mostcropdmg[dispcols] # display the row with details on the event that caused most crop damage with o ther details dispcropdmg ## BGN_DATE BGN_TIME COUNTYNAME STATE ## 1 8/31/1993 0:00: ADAMS, CALHOUN AND JERSEY IL ## 2 2/9/1994 0:00: MSZ MS ## EVTYPE END_DATE END_TIME CROPDMG CROPDMGEXP ## 1 RIVER FLOOD 5 B ## 2 ICE STORM 2/10/1994 0:00:00 5 B EVENT CAUSING THE WORST CROP DAMAGE in US$ (m/m:millions, b/b:billions)

6 Page 6 of 10 This section answers this question: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? " The code below gets the top 1% of fatalities/injuries, groups and totals by event type. Next, it gets the top 6 total fatalities/injuries and plots it against the corresponding event types. The code also shows the details of the event that cause the worst fatality/max # of injuries # FATALITIES # filter to get the top 1% of the data in terms of fatalities toponepercentfatalities <- filter(stormdata,stormdata$fatalities >= quantile(un list(stormdata$fatalities),c(.25,.5,.9,.99))[4]) # group by event groupbyevt <- group_by(toponepercentfatalities,evtype) # get the top 6 events that caused most fatalities summfatalities <- arrange(summarize(groupbyevt,totalfatalities=sum(fatalities,n a.rm=true)),desc(totalfatalities))[1:6,] # find the event that cause the most fatalities mostfatalities <- filter(stormdata,stormdata$fatalities >= max(stormdata$fatali TIES)) dispcols <- names(mostfatalities) %in% c("evtype","bgn_date","bgn_time", "END_D ATE", "END_TIME","COUNTYNAME","STATE","FATALITIES") dispfatalities <- mostfatalities[dispcols] # display the row with details on the event that caused most fatalities with ot her details dispfatalities

7 Page 7 of 10 ## BGN_DATE BGN_TIME ## 1 7/12/1995 0:00: ## COUNTYNAME STATE EVTYPE ## 1 ILZ003> > > IL HEAT ## END_DATE END_TIME FATALITIES ## 1 7/16/1995 0:00: CST 583 EVENT CAUSING THE WORST FATALITIES plot(unclass(summfatalities$evtype),summfatalities$totalfatalities,xaxt="n",las =2,type="h",col="red",lwd=10,xlab="Events",ylab="Fatalities",main="Figure 2: To p 6 Event types by most fatalities caused") axis(1,at=unclass(summfatalities$evtype),labels=unclass(summfatalities$evtype), cex.axis=1,las=2) legend("topleft", pch = 1,ncol=2, cex=0.75,legend = c(paste(unclass(summfatalit ies$evtype)[1],summfatalities$evtype[1],sep=":"), paste(unclass(summfatalities$evtype)[2],summfatalities$evtype[2],sep=":"),paste (unclass(summfatalities$evtype)[3],summfatalities$evtype[3],sep=":"), paste(unclass(summfatalities$evtype)[4],summfatalities$evtype[4],sep=":"),paste (unclass(summfatalities$evtype)[5],summfatalities$evtype[5],sep=":"), paste(unclass(summfatalities$evtype)[6],summcrop$evtype[6],sep=":")) )

8 Page 8 of 10 # INJURIES # filter to get the top 1% of the data in terms of injuries toponepercentinjuries <- filter(stormdata,stormdata$injuries >= quantile(unlist (stormdata$injuries),c(.25,.5,.9,.99))[4]) # group by event groupbyevt <- group_by(toponepercentinjuries,evtype) # get the top 6 events that caused most injuries summinjuries <- arrange(summarize(groupbyevt,totalinjuries=sum(injuries,na.rm=t RUE)),desc(TOTALINJURIES))[1:6,] # find the event that cause the most injuries mostinjuries <- filter(stormdata,stormdata$injuries >= max(stormdata$injuries)) dispcols <- names(mostinjuries) %in% c("evtype","bgn_date","bgn_time", "END_DAT E", "END_TIME","COUNTYNAME","STATE","INJURIES") dispinjuries <- mostinjuries[dispcols] # display the row with details on the event that caused most injuries with othe r details print("event CAUSING THE WORST INJURIES")

9 Page 9 of 10 ## [1] "EVENT CAUSING THE WORST INJURIES" dispinjuries ## BGN_DATE BGN_TIME COUNTYNAME STATE EVTYPE END_DATE END_TIME ## 1 4/10/1979 0:00: WICHITA TX TORNADO ## INJURIES ## EVENT CAUSING THE WORST INJURIES plot(unclass(summinjuries$evtype),summinjuries$totalinjuries,xaxt="n",las=2,typ e="h",col="red",lwd=10,xlab="events",ylab="injuries",main="figure 3: Top 6 Even t types by the most injuries caused") axis(1,at=unclass(summinjuries$evtype),labels=unclass(summinjuries$evtype),cex. axis=1,las=2) legend("topleft", pch = 1,ncol=2,cex=0.75, legend = c(paste(unclass(summinjurie s$evtype)[1],summinjuries$evtype[1],sep=":"), paste(unclass(summinjuries$evtype)[2],summinjuries$evtype[2],sep=":"),paste(unc lass(summinjuries$evtype)[3],summinjuries$evtype[3],sep=":"), paste(unclass(summinjuries$evtype)[4],summinjuries$evtype[4],sep=":"),paste(unc lass(summinjuries$evtype)[5],summinjuries$evtype[5],sep=":"), paste(unclass(summinjuries$evtype)[6],summinjuries$evtype[6],sep=":")) )

10 Page 10 of 10

Subsetting, dplyr, magrittr Author: Lloyd Low; add:

Subsetting, dplyr, magrittr Author: Lloyd Low; Email add: wai.low@adelaide.edu.au Introduction So you have got a table with data that might be a mixed of categorical, integer, numeric, etc variables? And