Resum

 

Aquesta PNG imatge rasteritzada ha estat creada amb R.

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
major.parties <- TRUE
if(major.parties) {
  selected.parties <- c("Green","Labour","National")   #use precise names from Table headers
  ylims <- c(0,65)   #Vertical range
  output.fname <- "NZ_opinion_polls_2009-2011 -parties.png"
} else {  #Small parties - please use "Maori" for the Maori party
  selected.parties <- c("ACT","Maori","NZ First","United Future","Mana")   #use precise names from Table headers
  ylims <- c(0,6)   #Vertical range
  output.fname <- "NZ opinion polls 2009-2011 -smallparties.png"

}

#==========================================
#Shouldn't need to edit anything below here

#Misc preparation
selected.parties <- gsub(" ","_",selected.parties)  #Handle the space in some names

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

#Extract the opinion poll data table
tbl.no <- 2
tbl <- html[(grep("<table.*",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[open.tr[i]:close.tr[i]]

#Extract table headers
hdrs <- grep("<th",tbl,value=TRUE)
hdrs <- hdrs[1:(length(hdrs)/2)]
party.names <- gsub("<.*?>","",hdrs)[-c(1:2)]
party.names <- gsub(" ","_",party.names)  #Replace space with a _
party.names <- gsub("M.{1}ori","Maori",party.names)  #Apologies, but the hard "a" is too hard to handle otherwise
party.cols   <- gsub("^.*bgcolor=\"(.*?)\".*$","\\1",hdrs)[-c(1:2)]
names(party.cols) <- party.names

#Extract data rows
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 3-11
  dat     <- td.tags[3:12]
  #Now strip the data and covert to numeric format
  dat     <- gsub("<td>|</td>","",dat)
  dat     <- gsub("%","",dat)
  dat     <- gsub("-","0",dat)
  dat     <- gsub("<","",dat)
  dat     <- as.numeric(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("<sup.*</sup>","",date.str)   #Throw out anything between superscript tags, as its an reference to the source
  date.str <- gsub("<td>|</td>","",date.str)  #Throw out any tags
  #Get numeric parts of string
  digits.str <- gsub("[^0123456789]"," ",date.str)
  digits.str <- gsub("^ +","",digits.str)    #Drop leading whitespace
  digits     <- strsplit(digits.str," +")[[1]]
  yrs        <- grep("[0-9]{4}",digits,value=TRUE)
  days       <- digits[!digits%in%yrs]
  #Get months
  month.str <- gsub("[^A-Z,a-z]"," ",date.str)
  month.str <- gsub("^ +","",month.str)        #Drop leading whitespace
  mnths     <- strsplit(month.str," +",month.str)[[1]]
  #Now paste together to make standardised date strings
  days  <- rep(days,length.out=2)
  mnths <- rep(mnths,length.out=2)
  yrs <- rep(yrs,length.out=2)
  dates.std <- paste(days,mnths,yrs)
#  cat(sprintf("%s\t -> \t %s, %s\n",date.str,dates.std[1],dates.std[2]))
  #And finally the survey time
  survey.time <- mean(as.POSIXct(strptime(dates.std,format="%d %B %Y")))
  #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)

#Restrict plot(manually) to selected parties
selected.parties <- sort(selected.parties)
selected.cols <- party.cols[selected.parties]
polls   <- surveys[,c("Company","Date",selected.parties)]
polls <- subset(polls,!is.na(surveys$Date))
polls <- polls[order(polls$Date),]
polls$date.num  <- as.double(polls$Date)

#Setup plot
ticks <- ISOdate(c(rep(2009,2),rep(2010,2),rep(2011,2),2012),c(rep(c(1,7),3),1),1)
xlims <- range(c(ISOdate(2008,11,1),ticks))
png(output.fname,width=778,height=487,pointsize=16)
par(mar=c(5,4,1,1))
matplot(polls$date.num,polls[,selected.parties],pch=NA,xlim=xlims,ylab="Party support (%)",
    xlab="",col=selected.cols,xaxt="n",ylim=ylims,yaxs="i")
abline(h=seq(0,95,by=5),col="lightgrey",lty=3)
abline(v=as.double(ticks),col="lightgrey",lty=3)
box()
axis(1,at=as.double(ticks),labels=format(ticks,format="1 %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(selected.parties)) {
  smoother <- loess(polls[,selected.parties[i]] ~ polls[,"date.num"],span=0.5)
  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) <- selected.parties
#Then add the data points
matpoints(polls$date.num,polls[,selected.parties],pch=20,col=selected.cols)
#And finally the smoothers themselves
for(i in 1:length(selected.parties)) {
  lines(predict.x,smoothed[[i]]$fit,col=selected.cols[i],lwd=2)
}

#Add election date too
#abline(v=election.date,lwd=4)
#text(election.date,0,format(election.date,"%d %b %Y"),srt=90,pos=4)

legend("bottom",legend=gsub("_"," ",selected.parties),col=selected.cols,pch=20,bg="white",lwd=2,horiz=TRUE,inset=-0.225,xpd=NA)
#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=selected.cols[i])
}
dev.off()

cat("Complete.\n")

Llicència

Jo, el titular dels drets d'autor d'aquest treball, el public sota la següent llicència:

Aquest fitxer està subjecte a la llicència de Creative Commons Reconeixement 3.0 No adaptada.

Sou lliure de:

• compartir – copiar, distribuir i comunicar públicament l'obra
• adaptar – fer-ne obres derivades

Amb les condicions següents:

• reconeixement – Heu de donar la informació adequada sobre l'autor, proporcionar un enllaç a la llicència i indicar si s'han realitzat canvis. Podeu fer-ho amb qualsevol mitjà raonable, però de cap manera no suggereixi que l'autor us dóna suport o aprova l'ús que en feu.

https://creativecommons.org/licenses/by/3.0CC BY 3.0 Creative Commons Attribution 3.0 truetrue