geom_vlines每个plot有多个vlines [英] geom_vlines multiple vlines per plot

问题描述

如何让ggplot产生类似于

的

  library（ggplot2）
 library（reshape2）
 library（ecp）
 
 synthetic_control.data<  - read.table（/ path / synthetic_control.data.txt，quote =\，comment.char =）
n < -  2 
 
s < - 样本（1：100，n）
 idx <-c（s，100 + s，200 + s，300 + s，400 + s，500 + s）
 sample2 < -  synthetic_control.data [idx，] 
 df = as.data.frame（t（as.matrix（sample2）））
 
＃计算变化点
 changeP<  -  e.divisive （as.matrix（df [1]），k = 8，R = 400，alpha = 2，min.size = 3）
 changeP = changeP $ estimated 
 changeP = changeP [-c（1 ，length（changeP））] 
 
 changePoints = data.frame（changeP，variable = colnames（df）[1]）$ ​​b $ b for（series in 2：ncol（df））{$ （as.matrix（df [series]），k = 8，R = 400，alpha = 2，min.size = 3）
 changeP = changeP $ estimated 
 changeP = changeP [-c（1，leng （changeP））] 
 changePoints = rbind（changePoints，data.frame（changeP，variable = colnames（df）[2]））
} 
  

这是关于情节的有趣部分：

df $ id = 1：nrow（df ）
dfMelt < - reshape2 :: melt（df，id.vars =id）
p = ggplot（dfMelt，aes（x = id，y = value））+ geom_line（color =












$ $ b

到目前为止，我的结果是： https://www.dropbox.com /s/mysadkruo946oox/changePoint.pdf ，这意味着我的数组传递给 geom_vlines 时出错。

你能指出我在正确的方向，为什么我只在前两个图中获得 vlines ？

解决方案

这是解决方案：

  libr ary（ggplot2）
 library（reshape2）
 library（ecp）
 
 synthetic_control.data<  -  read.table（/ Users / geoHeil / Dropbox / 6.Semester /本科/研究/数据/ synthetic_control.data.txt，quote =\，comment.char =）
n < -  2 
 
s < -  sample（1 ：100，n）
 idx <-c（s，100 + s，200 + s，300 + s，400 + s，500 + s）
 sample2 < -  synthetic_control.data [idx ，] 
 df = as.data.frame（t（as.matrix（sample2）））
 
＃计算变化点
 changeP<  -  e.divisive（as .matrix（df [1]），k = 8，R = 400，alpha = 2，min.size = 3）
 changeP = changeP $ estimated 
 changeP = changeP [-c （changeP））] 
 
 changePoints = data.frame（changeP，variable = colnames（df）[1]）$ ​​b $ b for（series in 2：ncol（df））{$ b $ （换言之，矩阵（df [系列]），k = 8，R = 400，α= 2，min.size = 3）
 changeP = changeP $估计
 changeP = changeP [-c（1，length（changeP））] 
 changePoints = rbind（changePoints，data.frame（changeP，variable = colnames（d f）[系列]））
} 
 
＃plot 
 df $ id = 1：nrow（df）
 dfMelt<  -  reshape2 :: melt（df ，id.vars =id）
p = ggplot（dfMelt，aes（x = id，y = value））+ geom_line（color =steelblue）+ facet_grid（variable〜。，scales ='free_y' ）
p + geom_vline（aes（xintercept = changeP），data = changePoints，linetype ='dashed'，color ='darkgreen'）
  

How can I get ggplot to produce something similar like

library(ggplot2)
library(reshape2)
library(ecp)

synthetic_control.data <- read.table("/path/synthetic_control.data.txt", quote="\"", comment.char="")
n <- 2

s <- sample(1:100, n)
idx <- c(s, 100+s, 200+s, 300+s, 400+s, 500+s)
sample2 <- synthetic_control.data[idx,]
df = as.data.frame(t(as.matrix(sample2)))

#calculate the change points
changeP <- e.divisive(as.matrix(df[1]), k=8, R = 400, alpha = 2, min.size = 3)
changeP = changeP$estimates
changeP = changeP[-c(1,length(changeP))]

changePoints = data.frame(changeP,variable=colnames(df)[1])
for(series in 2:ncol(df)){
  changeP <- e.divisive(as.matrix(df[series]), k=8, R = 400, alpha = 2, min.size = 3)
  changeP = changeP$estimates
  changeP = changeP[-c(1,length(changeP))]
  changePoints = rbind(changePoints, data.frame(changeP,variable=colnames(df)[2]))
}

this is the interesting part about the plot:

df$id = 1:nrow(df) dfMelt <- reshape2::melt(df, id.vars = "id") p = ggplot(dfMelt,aes(x=id,y=value))+geom_line(color = "steelblue")+ facet_grid(variable ~ ., scales = 'free_y') p + geom_vline(aes(xintercept=changeP), data=changePoints, linetype='dashed')

So far my result is: https://www.dropbox.com/s/mysadkruo946oox/changePoint.pdf which means that there is something wrong with my array passed to the geom_vlines.

Could you point me in the right direction why I only get vlines in the first 2 plots?

解决方案

This is the solution:

library(ggplot2)
library(reshape2)
library(ecp)

synthetic_control.data <- read.table("/Users/geoHeil/Dropbox/6.Semester/BachelorThesis/rResearch/data/synthetic_control.data.txt", quote="\"", comment.char="")
n <- 2

s <- sample(1:100, n)
idx <- c(s, 100+s, 200+s, 300+s, 400+s, 500+s)
sample2 <- synthetic_control.data[idx,]
df = as.data.frame(t(as.matrix(sample2)))

#calculate the change points
changeP <- e.divisive(as.matrix(df[1]), k=8, R = 400, alpha = 2, min.size = 3)
changeP = changeP$estimates
changeP = changeP[-c(1,length(changeP))]

changePoints = data.frame(changeP,variable=colnames(df)[1])
for(series in 2:ncol(df)){
  changeP <- e.divisive(as.matrix(df[series]), k=8, R = 400, alpha = 2, min.size = 3)
  changeP = changeP$estimates
  changeP = changeP[-c(1,length(changeP))]
  changePoints = rbind(changePoints, data.frame(changeP,variable=colnames(df)[series]))
}

# plot
df$id = 1:nrow(df)
dfMelt <- reshape2::melt(df, id.vars = "id")
p = ggplot(dfMelt,aes(x=id,y=value))+geom_line(color = "steelblue")+ facet_grid(variable ~ ., scales = 'free_y')
p + geom_vline(aes(xintercept=changeP), data=changePoints, linetype='dashed', colour='darkgreen')

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