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2016年总统选举的预测
ASA的美国总统竞选
在这个大选之年，美国统计协会(ASA)将学生竞赛和总统选举放在一起，将学生预测谁是2016年总统大选的赢家准确的百分比作为比赛点。详情见:
http://thisisstatistics.org/electionprediction2016/
获取数据
互联网上有很多公开的民调数据。可以下面的网站获取总统大选的相关数据：
http://projects.fivethirtyeight.com/2016-election-forecast/national-polls/
其他较好的数据源是：
http://www.realclearpolitics.com/epolls/latest_polls/
http://elections.huffingtonpost.com/pollster/2016-general-election-trump-vs-clinton
http://www.gallup.com/products/170987/gallup-analytics.aspx)
值得注意的是：数据是每天更新的，所以你在看本文的时候很可能数据变化而得到不同的结果。
因为原始的数据是JSON文件，R拉取下来将其作为了lists中的一个list(列表)。
原文的Github地址：https://github.com/hardin47/prediction2016/blob/master/predblog.Rmd
##载入需要的包
require(XML)
require(dplyr)
require(tidyr)
require(readr)
require(mosaic)
require(RCurl)
require(ggplot2)
require(lubridate)
require(RJSONIO)
##数据拉取
url = "http://projects.fivethirtyeight.com/2016-election-forecast/national-polls/"
doc <- htmlParse(url, useInternalNodes = TRUE) #爬取网页内容
sc = xpathSApply(doc,
"//script[contains(., ‘race.model‘)]",
function(x) c(xmlValue(x), xmlAttrs(x)[["href"]]))
jsobj = gsub(".*race.stateData = http://www.mamicode.com/(.*);race.pathPrefix.*", "\\1", sc)
data = http://www.mamicode.com/fromJSON(jsobj)"wtpolls", "wtplus", "wtnow")
row.names(pollswt) <- NULL
pollsdf <- cbind(pollsdf, pollswt)
#unlisting the voting
indxv <- sapply(pollsdf$votingAnswers, length)
pollsvot <- as.data.frame(do.call(rbind, lapply(pollsdf$votingAnswers,
‘length<-‘, max(indxv))))
pollsvot1 <- rbind(as.data.frame(do.call(rbind, lapply(pollsvot$V1, data.frame,
stringsAsFactors=FALSE))))
pollsvot2 <- rbind(as.data.frame(do.call(rbind, lapply(pollsvot$V2, data.frame,
stringsAsFactors=FALSE))))
pollsvot1 <- cbind(polltype = rownames(pollsvot1), pollsvot1,
polltypeA = gsub(‘[0-9]+‘, ‘‘, rownames(pollsvot1)),
polltype1 = extract_numeric(rownames(pollsvot1)))
pollsvot1$polltype1 <- ifelse(is.na(pollsvot1$polltype1), 1, pollsvot1$polltype1 + 1)
pollsvot2 <- cbind(polltype = rownames(pollsvot2), pollsvot2,
polltypeA = gsub(‘[0-9]+‘, ‘‘, rownames(pollsvot2)),
polltype1 = extract_numeric(rownames(pollsvot2)))
pollsvot2$polltype1 <- ifelse(is.na(pollsvot2$polltype1), 1, pollsvot2$polltype1 + 1)
pollsdf <- pollsdf %>%
mutate(population = unlist(population),
sampleSize = as.numeric(unlist(sampleSize)),
pollster = unlist(pollster),
startDate = ymd(unlist(startDate)),
endDate = ymd(unlist(endDate)),
pollsterRating = unlist(pollsterRating)) %>%
select(population, sampleSize, pollster, startDate, endDate, pollsterRating,
wtpolls, wtplus, wtnow)
allpolldata <- cbind(rbind(pollsdf[rep(seq_len(nrow(pollsdf)), each=3),],
pollsdf[rep(seq_len(nrow(pollsdf)), each=3),]),
rbind(pollsvot1, pollsvot2))
allpolldata <- allpolldata %>%
arrange(polltype1, choice)
查看所有的选择数据：allolldata
快速可视化
在找出2016年美国总统竞选的预测选票比例之前，简单的查看数据是非常有必要的。数据集已经整理好了，使用ggplot2包对其进行可视化（选取2016年8月以后的数据，x轴为endDate，y轴为adj_pct，颜色根据choice也就是两种颜色克林顿和希拉里，并根据wtnow设置点的大小）：
##快速可视化
ggplot(subset(allpolldata, ((polltypeA == "now") & (endDate > ymd("2016-08-01")))),
aes(y=adj_pct, x=endDate, color=choice)) +
geom_line() + geom_point(aes(size=wtnow)) +
labs(title = "Vote percentage by date and poll weight\n",
y = "Percent Vote if Election Today", x = "Poll Date",
color = "Candidate", size="538 Poll\nWeight")
快速分析
考虑到每位候选人的选票比例会基于当前投票的票数百分比，所以，必须基于538人（样本容量samplesize）的想法（投票举动）和投票关闭天数（day sine poll）进行选票权重设置。权重的计算公式如下：
使用计算出的权重，我将计算被预测选票百分比的加权平均和其标准偏差（SE）。标准偏差（SE）计算公式来自 Cochran (1977) 。
##快速分析
# 参考文献
# code found at http://stats.stackexchange.com/questions/25895/computing-standard-error-in-weighted-mean-estimation
# cited from http://www.cs.tufts.edu/~nr/cs257/archive/donald-gatz/weighted-standard-error.pdf
# Donald F. Gatz and Luther Smith, "THE STANDARD ERROR OF A WEIGHTED MEAN CONCENTRATION-I. BOOTSTRAPPING VS OTHER METHODS"
weighted.var.se <- function(x, w, na.rm=FALSE)
# Computes the variance of a weighted mean following Cochran 1977 definition
{
if (na.rm) { w <- w[i <- !is.na(x)]; x <- x[i] }
n = length(w)
xWbar = weighted.mean(x,w,na.rm=na.rm)
wbar = mean(w)
out = n/((n-1)*sum(w)^2)*(sum((w*x-wbar*xWbar)^2)-2*xWbar*sum((w-wbar)*(w*x-wbar*xWbar))+xWbar^2*sum((w-wbar)^2))
return(out)
}
# 计算累计平均和加权平均值Cumulative Mean / Weighted Mean
allpolldata2 <- allpolldata %>%
filter(wtnow > 0) %>%
filter(polltypeA == "now") %>%
mutate(dayssince = as.numeric(today() - endDate)) %>%
mutate(wt = wtnow * sqrt(sampleSize) / dayssince) %>%
mutate(votewt = wt*pct) %>%
group_by(choice) %>%
arrange(choice, -dayssince) %>%
mutate(cum.mean.wt = cumsum(votewt) / cumsum(wt)) %>%
mutate(cum.mean = cummean(pct))
View(allpolldata2 )
可视化累计平均和加权平均值
##绘制累计平均/加权平均Cumulative Mean / Weighted Mean
# 累计平均
ggplot(subset(allpolldata2, ( endDate > ymd("2016-01-01"))),
aes(y=cum.mean, x=endDate, color=choice)) +
geom_line() + geom_point(aes(size=wt)) +
labs(title = "Cumulative Mean Vote Percentage\n",
y = "Cumulative Percent Vote if Election Today", x = "Poll Date",
color = "Candidate", size="Calculated Weight")
# 加权平均
ggplot(subset(allpolldata2, (endDate > ymd("2016-01-01"))),
aes(y=cum.mean.wt, x=endDate, color=choice)) +
geom_line() + geom_point(aes(size=wt)) +
labs(title = "Cumulative Weighted Mean Vote Percentage\n",
y = "Cumulative Weighted Percent Vote if Election Today", x = "Poll Date",
color = "Candidate", size="Calculated Weight")
选票百分比预测
此外，加权平均和平均的标准偏差(科克伦(1977))可以对每个候选人进行计算。使用这个公式，我们可以预测主要候选人的最后的百分比！
pollsummary <- allpolldata2 %>%
select(choice, pct, wt, votewt, sampleSize, dayssince) %>%
group_by(choice) %>%
summarise(mean.vote = weighted.mean(pct, wt, na.rm=TRUE),
std.vote = sqrt(weighted.var.se(pct, wt, na.rm=TRUE)))
pollsummary
## # A tibble: 2 x 3
## choice mean.vote std.vote
## <chr> <dbl> <dbl>
## 1 Clinton 43.48713 0.5073771
## 2 Trump 38.95760 1.0717574
显然，主要的候选人是克林顿和希拉里，克林顿的选票平均百分比高于希拉里，并且其标准偏差小于希拉里，也就是说其选票变化稳定，最后胜出的很可能就是克林顿，但是按照希拉里的变化波动大，也不排除希拉里获胜的可能。可以看到希拉里的选票比例最高曾达到51%。
原文链接：https://www.r-statistics.com/2016/08/presidential-election-predictions-2016/
本文链接：http://www.cnblogs.com/homewch/p/5811945.html
2016年总统选举的预测