File:NZ opinion polls 2005-2008 new.png
NZ_opinion_polls_2005-2008_new.png (778 × 487 pixels, file size: 72 KB, MIME type: image/png)
This is a file from the Wikimedia Commons. The description on its description page there is shown below.
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Summary
DescriptionNZ opinion polls 2005-2008 new.png |
English: Graph showing support for political parties in New Zealand since the 2005 election, according to various political polls. Data is obtained from the Wikipedia page, Opinion_polling_for_the_New_Zealand_general_election,_2008 |
Date | |
Source | Own work |
Author | Mark Payne, Denmark |
Figure is produced using the R statistical package, using the following code. It first reads the HTML directly from the website, then parses the data and saves the graph into your working directory. It should be able to be run directly by anyone with R.
rm(list=ls())
#Load the complete HTML file into memory
html <- readLines(url("http://en.wikipedia.org/wiki/Opinion_polling_for_the_New_Zealand_general_election,_2008"),encoding="UTF-8")
closeAllConnections()
#The third table is the opinion poll data
tbl <- html[(grep("<table.*",html)[3]):(grep("</table.*",html)[3])]
#Now split it into the rows, based on the <tr> tag
tbl.rows <- split(tbl,cumsum(tbl=="<tr>"))
#Now extract the data
survey.dat <- lapply(tbl.rows,function(x) {
#Start by only considering where we have <td> tags
td.tags <- x[grep("<td",x)]
#Polling data appears in columns 3-10
dat <- td.tags[3:10]
#Now strip the data and covert to numeric format
dat <- gsub("<td>|</td>","",dat)
dat <- gsub("%","",dat)
dat <- gsub("-","0",dat)
dat <- as.numeric(dat)
#Getting the date strings is a little harder. The approach we will take is to take advantage
#of the title="date" hyperlinks to generate a set of dates
date.str <- td.tags[2] #Dates are in the second column
date.str <- gsub("<sup.*</sup>","",date.str) #Throw out anything between superscript tags, as its an reference to the source
titles <- gregexpr("(?U)title=\".*\"",date.str,perl=TRUE)[[1]] #Find the location of the title tags
#Now, extract the actual date strings
date.strings <- rep(NULL,length(titles))
for(i in 1:length(titles)) {
date.strings[i] <- substr(date.str,titles[i]+7,titles[i]+attr(titles,"match.length")[i]-2)
}
yr <- rev(date.strings)[1]
dates <- rep(as.POSIXct(Sys.time()),length(date.strings)-1)
for(i in 1:(length(date.strings)-1)) {
dates[i] <- as.POSIXct(strptime(paste(date.strings[i],yr),"%B %d %Y"))
}
survey.time <- mean(dates)
#Get the name of the survey company too
survey.comp <- td.tags[1]
survey.comp <- gsub("<sup.*</sup>","",survey.comp)
survey.comp <- gsub("<td>|</td>","",survey.comp)
survey.comp <- gsub("<U+2013>","-",survey.comp,fixed=TRUE)
survey.comp <- gsub("(?U)<.*>","",survey.comp,perl=TRUE)
#And now return results
return(data.frame(Company=survey.comp,Date=survey.time,t(dat)))
})
#Combine results
surveys <- do.call(rbind,survey.dat)
colnames(surveys) <- c("Company","Date","Labour","National","NZ First","Maori Party","Greens","ACT","United Future","Progressive")
#Restrict plot(manually) to parties which have been over 5%
parties <- c("Greens","Labour","National","NZ First")
cols <- c("darkgreen","red","blue","black")
polls <- surveys[,c("Company","Date",parties)]
polls <- subset(polls,!is.na(surveys$Date))
polls <- polls[order(polls$Date),]
polls$Date <- as.double(polls$Date)
ticks <- ISOdate(c(2005,rep(2006,3),rep(2007,3),rep(2008,3)),c(9,rep(c(1,5,9),3)),1)
xlims <- range(as.double(c(ticks,ISOdate(2009,4,1))))
png("NZ_opinion_polls_2005-2008 -parties.png",width=778,height=487,pointsize=16)
par(mar=c(3,4,1,1))
matplot(polls$Date,polls[,parties],pch=NA,xlim=xlims,ylab="Party support (%)",xlab="",col=cols,xaxt="n",ylim=c(0,60))
abline(h=seq(0,95,by=5),col="lightgrey",lty=3)
abline(v=as.double(ticks),col="lightgrey",lty=3)
#Now add loess smoothers
smoothed <- list()
for(i in 1:length(parties)) {
smoother <- loess(polls[,parties[i]] ~ polls[,"Date"],span=0.25)
smoothed[[i]] <- predict(smoother,se=TRUE)
polygon(c(polls[,"Date"],rev(polls[,"Date"])),
c(smoothed[[i]]$fit+smoothed[[i]]$se.fit*1.96,rev(smoothed[[i]]$fit-smoothed[[i]]$se.fit*1.96)),
col=rgb(0.5,0.5,0.5,0.5),border=NA)
}
names(smoothed) <- parties
for(i in 1:length(parties)) {
lines(polls[,"Date"],smoothed[[i]]$fit,col=cols[i],lwd=2)
}
matpoints(polls$Date,polls[,parties],pch=20,col=cols)
legend("topleft",legend=parties,col=cols,pch=20,bg="white",lwd=2)
axis(1,at=as.double(ticks),labels=format(ticks,format="%b\n%Y"),cex.axis=0.8)
axis(4,at=axTicks(4),labels=rep("",length(axTicks(4))))
#Add best estimates
for(i in 1:length(smoothed)) {
lbl <- sprintf("%4.1f%% ± %2.1f",round(rev(smoothed[[i]]$fit)[1],1),round(1.96*rev(smoothed[[i]]$se.fit)[1],1))
text(rev(polls$Date)[1],rev(smoothed[[i]]$fit)[1],labels=lbl,pos=4,col=cols[i])
}
dev.off()
#As a cross validation, print the rows where there are NA's
checks <- subset(surveys,apply(surveys,1,function(x) any(is.na(x))))
print(checks)
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Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation License.http://www.gnu.org/copyleft/fdl.htmlGFDLGNU Free Documentation Licensetruetrue |
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4 October 2008
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Date/Time | Dimensions | User | Comment | |
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current | 12:45, 26 September 2016 | 778 × 487 (72 KB) | Cmdrjameson | Compressed with pngout. Reduced by 47kB (39% decrease). |
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