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())

#Parameters
party.names <- c("Venstre","Soc.Dem.","DFP","SF","Kons.","Rad.V.","Lib.A.","Enhed.","KD")
parties.to.plot <- party.names[1:8]
party.cols    <- c("darkblue","red","gold","darkred","lawngreen","blue","darkolivegreen")
ylims <- c(0,35)
xlims <- c(ISOdate(2010,1,15),ISOdate(2011,10,15))
ticks <- ISOdate(rep(c(2010,2011),each=4),rep(c(1,4,7,10),times=2),15)

#Load the complete HTML file into memory
html <- readLines(url("http://en.wikipedia.org/wiki/Danish_parliamentary_election,_2011"),encoding="UTF-8")
closeAllConnections()

#Extract the opinion poll data table
tbl.no <- 6
tbl <- html[][tbl.no]):(grep("</table.*",html)[tbl.no])]

#Now split it into the rows, based on the <tr> tag
tbl.rows <- list()
open.tr <- grep("<tr",tbl)
close.tr <- grep("</tr",tbl)
for(i in 1:length(open.tr)) tbl.rows[[i]] <- tbl[][i]:close.tr[i]

#Throwout items that are headers or extra info
tbl.rows <- tbl.rows[sapply(tbl.rows,function(x) length(grep("<td",x)))>1]

#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[4:12]
  #Now strip the data and covert to numeric format
  dat     <- gsub("<td>|</td>","",dat)
  dat     <- gsub("<b>|</b>","",dat)
  dat     <- gsub("%","",dat)
  dat     <- gsub("-","0",dat)
  dat     <- as.numeric(dat)
  dat     <- ifelse(is.na(dat),0,dat)
  names(dat) <- party.names
  #Getting the date strings is a little harder. Start by tidying up the dates
  date.str <- td.tags[2]                        #Dates are in the second column
  date.str <- gsub("<td>|</td>","",date.str)  #Throw out any tags
  
  #And finally the survey time
  survey.time <- as.POSIXct(strptime(date.str,format="%Y-%m-%d"))
  #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,date.str,t(dat)))
})

#Combine results
surveys <- do.call(rbind,survey.dat)

#Subset data to only plot selected parties in time window
polls   <- surveys[,c("Company","Date",parties.to.plot)]
polls <- subset(polls,!is.na(polls$Date) )
polls <- subset(polls,polls$Date>=min(xlims) )
polls <- polls[order(polls$Date),]
polls$date.num  <- as.double(polls$Date)

#Setup plot
png("DK_opinion_polls_2011_election.png",width=778,height=487,pointsize=16)
par(mar=c(3,4,1,1),oma=c(3,0,0,0))
matplot(polls$date.num,polls[,parties.to.plot],pch=NA,xlim=xlims,ylab="Party support (%)",xlab="",
  col=party.cols,xaxt="n",ylim=ylims)
abline(h=seq(0,95,by=5),col="lightgrey",lty=3)
abline(v=as.double(ticks),col="lightgrey",lty=3)
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))))

#Now calculate the loess smoothers and add the confidence interval
smoothed <- list()
predict.x <- seq(min(polls$date.num),max(polls$date.num),length.out=100)
for(i in 1:length(parties.to.plot)) {
  smoother <- loess(polls[][i] ~ polls[,"date.num"],span=0.75)
  smoothed[[i]] <- predict(smoother,newdata=predict.x,se=TRUE)
#  polygon(c(predict.x,rev(predict.x)),
#    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.to.plot
#Then add the data points
matpoints(polls$date.num,polls[,parties.to.plot],pch=20,col=party.cols)
#And finally the smoothers themselves
for(i in 1:length(parties.to.plot)) {
  lines(predict.x,smoothed[[i]]$fit,col=party.cols[i],lwd=2)
}

legend(grconvertX(0.5,"npc"),grconvertY(0,"nfc"),legend=parties.to.plot,col=party.cols,
    pch=20,bg="white",lwd=2,ncol=ceiling(length(parties.to.plot)/2),cex=0.75,xpd=NA,xjust=0.5)
    
#Add best estimates
for(i in 1:length(smoothed)) {
  lbl <- sprintf("%2.0f ± %1.0f",round(rev(smoothed[[i]]$fit)[1],0),round(1.96*rev(smoothed[[i]]$se.fit)[1],0))
  text(rev(polls$date.num)[1],rev(smoothed[[i]]$fit)[1],labels=lbl,pos=4,col=party.cols[i])
}
dev.off()

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