Machine Learning for hackers读书笔记(九)MDS：可视化地研究参议员相似性
library('foreign')
library('ggplot2')
data.dir <- file.path('G:\\dataguru\\ML_for_Hackers\\ML_for_Hackers-master\\09-MDS\\data\\roll_call')
data.files <- list.files(data.dir)
rollcall.data <- lapply(data.files,function(f) { read.dta(file.path(data.dir, f), convert.factors = FALSE) })
#看一下数据情况,103行,647列
#每一行对应一个议员,包括个人信息及投票结果
dim(rollcall.data[[1]])
rollcall.simplified <- function(df)
{
#99的很少投票,干脆删掉
no.pres <- subset(df, state < 99)
#10列后才是投票数据
for(i in 10:ncol(no.pres))
{
#有10种投票类型,分为三组,赞成全放一起,反对全放一起,无效全放一起,>6的是无效,1~3是赞成,4~6是反对票
no.pres[,i] <- ifelse(no.pres[,i] > 6, 0, no.pres[,i])
no.pres[,i] <- ifelse(no.pres[,i] > 0 & no.pres[,i] < 4, 1, no.pres[,i])
no.pres[,i] <- ifelse(no.pres[,i] > 1, -1, no.pres[,i])
}
return(as.matrix(no.pres[,10:ncol(no.pres)]))
}
rollcall.simple <- lapply(rollcall.data, rollcall.simplified)
#来一个矩离矩阵
rollcall.dist <- lapply(rollcall.simple, function(m) dist(m %*% t(m)))
rollcall.mds <- lapply(rollcall.dist,function(d) as.data.frame((cmdscale(d, k = 2)) * -1))
congresses <- 101:111
for(i in 1:length(rollcall.mds))
{
names(rollcall.mds[[i]]) <- c("x", "y")
congress <- subset(rollcall.data[[i]], state < 99)
congress.names <- sapply(as.character(congress$name),function(n) strsplit(n, "[, ]")[[1]][1])
rollcall.mds[[i]] <- transform(rollcall.mds[[i]], name = congress.names,party = as.factor(congress$party),congress = congresses[i])
}
cong.110 <- rollcall.mds[[9]]
base.110 <- ggplot(cong.110, aes(x = x, y = y)) + scale_size(range = c(2,2), guide = 'none') + scale_alpha(guide = 'none') + theme_bw() +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), panel.grid.major = element_blank()) +
ggtitle("Roll Call Vote MDS Clustering for 110th U.S. Senate") + xlab("") + ylab("") + scale_shape(name = "Party", breaks = c("100", "200", "328"),
labels = c("Dem.", "Rep.", "Ind."), solid = FALSE) + scale_color_manual(name = "Party", values = c("100" = "black","200" = "dimgray","328"="grey"),
breaks = c("100", "200", "328"), labels = c("Dem.", "Rep.", "Ind."))
print(base.110 + geom_point(aes(shape = party, alpha = 0.75, size = 2)))
print(base.110 + geom_text(aes(color = party, alpha = 0.75, label = cong.110$name, size = 2)))
all.mds <- do.call(rbind, rollcall.mds)
all.plot <- ggplot(all.mds, aes(x = x, y = y)) +
geom_point(aes(shape = party, alpha = 0.75, size = 2)) +
scale_size(range = c(2, 2), guide = 'none') +
scale_alpha(guide = 'none') +
theme_bw() +
theme(axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.grid.major = element_blank()) +
ggtitle("Roll Call Vote MDS Clustering for U.S. Senate (101st - 111th Congress)") +
xlab("") +
ylab("") +
scale_shape(name = "Party",
breaks = c("100", "200", "328"),
labels = c("Dem.", "Rep.", "Ind."),
solid = FALSE) +
facet_wrap(~ congress)
print(all.plot)
