NZ_opinion_polls_2017-2020-majorparties.png (778 × 487 pixels, file size: 10 KB, MIME type: image/png)
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Summary
DescriptionNZ opinion polls 2017-2020-majorparties.png |
English: poll graph |
Date | |
Source | Own work |
Author | Limegreen |
Licensing
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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())
#require(mgcv)
library(tidyverse)
#==========================================
#Parameters - specified as a list
opts <- list()
opts$major <- list(parties= c("GRN", "LAB", "NAT", "NZF"), #use precise names from Table headers
ylims = c(0,65), #Vertical range
fname= "NZ_opinion_polls_2017-2020-majorparties.png",
dp=0) #Number of decimal places to round estimates to
opts$minor <- list(parties=c("ACT","TOP", "MRI","NCP" #please use "Maori" for the Maori party
),
ylims = c(0,6), #Vertical range
fname = "NZ_opinion_polls_2017-2020-minorparties.png",
dp=1) #Number of decimal places to round estimates to
#==========================================
#Shouldn't need to edit anything below here
#==========================================
#Load the complete HTML file into memory
html <- readLines("http://en.wikipedia.org/wiki/Opinion_polling_for_the_2020_New_Zealand_general_election",encoding="UTF-8")
closeAllConnections()
#Extract the opinion poll data table
tbl.no <- 1
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)
party.names <- gsub("<.*?>","",hdrs)[-c(1:3, 12)] %>% #nasty hack
gsub(" ","_",.) %>% #Replace space with a _
gsub("M.{1}ori","Maori",.) #Apologies, but the hard "a" is too hard to handle otherwise
#extract party colours
# party.cols <- str_extract(hdrs, "(?<=color:).{7}") %>%
# na.omit()
party.cols <- c("#00529F", "#D82A20", "#000000", "#098137",
"#FDE401", "#800080", "#800000", "#6698FF")
names(party.cols) <- party.names
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
dat <- tbl.rows
dat <- lapply(dat, function(x) x[c(7:14)])
dat <- unlist(dat)
dat <- gsub("<.{1,3}>", "", dat)
dat <- gsub("<.*>", "", dat)
dat <- as.numeric(dat)
survey.dat <- matrix(dat, nrow = length(tbl.rows), byrow = TRUE) %>%
as.data.frame()
names(survey.dat) = party.names
#get survey dates
date.str <- lapply(tbl.rows, function(x) x[2])
date.str <- str_extract(date.str, '".*"')
date.str <- gsub('\\"', '', date.str)
date.str <- strtrim(date.str, 10)
survey.date <- strptime(date.str, format = "%Y-%m-%d")
#get survey company
company <- lapply(tbl.rows, function(x) x[4]) %>%
unlist()
company <- str_extract(company, '(?=">).*')
company <- gsub('\\">', '', company)
company <- strsplit(company, '<') %>%
lapply(function(x) x[1]) %>%
unlist()
#Combine results
surveys <- cbind(survey.date, company, survey.dat) %>%
rename(Date = survey.date, Company = company) %>%
filter(NAT > 0)
#Ugly fix to remove Opportunities party while not enough data
# surveys <- select(surveys, -TOP)
#==========================================
#Now generate each plot
#==========================================
smoothers <- list()
for(opt in opts) {
#Restrict data to selected parties
selected.parties <- gsub(" ","_",sort(opt$parties))
selected.cols <- party.cols[selected.parties]
plt.dat <- surveys[,c("Company","Date",selected.parties)]
plt.dat <- subset(plt.dat,!is.na(surveys$Date))
plt.dat <- plt.dat[order(plt.dat$Date),]
plt.dat$date.num <- as.double(plt.dat$Date)
plt.dat <- subset(plt.dat,Company!="2017 election result")
plt.dat$Company <- factor(plt.dat$Company)
#Setup plot
ticks <- ISOdate(c(rep(2017,1),rep(2018,2),rep(2019,2),rep(2020,2),2021),c(rep(c(7,1),4)),1)
xlims <- range(c(ISOdate(2017,11,1),ticks))
png(opt$fname,width=778,height=487,pointsize=16)
par(mar=c(5.5,4,1,1))
matplot(plt.dat$date.num,plt.dat[,selected.parties],pch=NA,xlim=xlims,ylab="Party support (%)",
xlab="",col=selected.cols,xaxt="n",ylim=opt$ylims,yaxs="i")
abline(h=seq(0,95,by=5),col="lightgrey",lty=3)
abline(v=as.double(ticks),col="lightgrey",lty=3)
abline(v=1506121200, col = "black", lty = 1)
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))))
smoothed <- list()
predict.x <- seq(min(surveys$Date),max(surveys$Date),length.out=100)
for(i in 1:length(selected.parties)) {
smoother <- loess(surveys[,selected.parties[i]] ~ as.numeric(surveys[,"Date"]),span=0.5)
smoothed[[i]] <- predict(smoother,newdata=predict.x,se=TRUE)
poly.data <- data.frame(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))) %>%
na.omit() %>%
polygon(col=rgb(0.5,0.5,0.5,0.5),border=NA, fillOddEven = TRUE)
}
names(smoothed) <- selected.parties
#Then add the data points
matpoints(surveys$Date, surveys[,selected.parties],pch=20,col=selected.cols)
#And finally the smoothers themselves
for(i in 1:length(selected.parties)) {
lines(predict.x,smoothed[[i]]$fit,col=selected.cols[i],lwd=2)
}
# #Then add the data points
# matpoints(plt.dat$date.num,plt.dat[,selected.parties],pch=20,col=selected.cols)
# #And finally the smoothers themselves
# for(n in selected.parties) {
# lines(smoothed.l[[n]]$date,smoothed.l[[n]]$fit,col=selected.cols[n],lwd=2)
# }
n.parties <- length(selected.parties)
legend(grconvertX(0.5,"npc"),grconvertY(0.0,"ndc"),xjust=0.5,yjust=0,
legend=gsub("_"," ",selected.parties), col=selected.cols,
pch=20,bg="white",lwd=2,
ncol=ifelse(n.parties>4,ceiling(n.parties/2),n.parties),xpd=NA)
#Add best estimates
# fmt.str <- sprintf("%%2.%if\261%%1.%if %%%%",opt$dp,opt$dp)
# for(n in names(smoothed)) {
# lbl <- sprintf(fmt.str,
# round(rev(smoothed[[n]]$fit)[1],opt$dp),
# round(1.96*rev(smoothed[[n]]$se.fit)[1],opt$dp)
# )
# text(rev(plt.dat$date.num)[1],rev(smoothed[[n]]$fit)[1],
# labels=lbl,pos=4,col=selected.cols[n],xpd=NA)
# }
dev.off()
}
#==========================================
#Finished!
#==========================================
cat("Complete.\n")
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File history
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Date/Time | Thumbnail | Dimensions | User | Comment | |
---|---|---|---|---|---|
current | 09:49, 21 May 2020 | ![]() | 778 × 487 (10 KB) | Limegreen | add new poll |
09:48, 21 May 2020 | ![]() | 778 × 487 (9 KB) | Limegreen | add new poll | |
09:15, 18 May 2020 | ![]() | 778 × 487 (10 KB) | Limegreen | add new poll | |
16:09, 13 February 2020 | ![]() | 778 × 487 (9 KB) | Limegreen | add next new poll | |
22:53, 12 February 2020 | ![]() | 778 × 487 (9 KB) | Limegreen | add first poll of 2020 | |
22:38, 2 December 2019 | ![]() | 778 × 487 (9 KB) | Limegreen | add 2 new polls | |
09:48, 15 October 2019 | ![]() | 778 × 487 (9 KB) | Limegreen | another new poll | |
20:32, 13 October 2019 | ![]() | 778 × 487 (9 KB) | Limegreen | add latest reid poll | |
11:08, 2 August 2019 | ![]() | 778 × 487 (9 KB) | Limegreen | add new poll | |
09:47, 9 June 2019 | ![]() | 778 × 487 (8 KB) | Limegreen | add two new polls to the graph |
File usage
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