在R中的分组箱图中显示中位数 [英] Showing median value in grouped boxplot in R

问题描述

我已使用带有此代码的ggplot2创建了箱线图.

I have created boxplots using ggplot2 with this code.

plotgraph <- function(x, y, colour, min, max)
{
    plot1 <- ggplot(dims, aes(x = x, y = y, fill = Region)) +
        geom_boxplot()
    #plot1 <- plot1 + scale_x_discrete(name = "Blog Type")
    plot1 <- plot1 + labs(color='Region') + geom_hline(yintercept = 0, alpha = 0.4)
    plot1 <- plot1 + scale_y_continuous(breaks=c(seq(min,max,5)), limits = c(min, max))
    plot1 <- plot1 + labs(x="Blog Type", y="Dimension Score") + scale_fill_grey(start = 0.3, end = 0.7) + theme_grey()
    plot1 <- plot1 + theme(legend.justification = c(1, 1), legend.position = c(1, 1))
    return(plot1)
}
plot1 <- plotgraph (Blog, Dim1, Region, -30, 25)

我使用的部分数据在这里复制.

A part of data I use is reproduced here.

Blog,Region,Dim1,Dim2,Dim3,Dim4
BlogsInd.,PK,-4.75,13.47,8.47,-1.29
BlogsInd.,PK,-5.69,6.08,1.51,-1.65
BlogsInd.,PK,-0.27,6.09,0.03,1.65
BlogsInd.,PK,-2.76,7.35,5.62,3.13
BlogsInd.,PK,-8.24,12.75,3.71,3.78
BlogsInd.,PK,-12.51,9.95,2.01,0.21
BlogsInd.,PK,-1.28,7.46,7.56,2.16
BlogsInd.,PK,0.95,13.63,3.01,3.35
BlogsNews,PK,-5.96,12.3,6.5,1.49
BlogsNews,PK,-8.81,7.47,4.76,1.98
BlogsNews,PK,-8.46,8.24,-1.07,5.09
BlogsNews,PK,-6.15,0.9,-3.09,4.94
BlogsNews,PK,-13.98,10.6,4.75,1.26
BlogsNews,PK,-16.43,14.49,4.08,9.91
BlogsNews,PK,-4.09,9.88,-2.79,5.58
BlogsNews,PK,-11.06,16.21,4.27,8.66
BlogsNews,PK,-9.04,6.63,-0.18,5.95
BlogsNews,PK,-8.56,7.7,0.71,4.69
BlogsNews,PK,-8.13,7.26,-1.13,0.26
BlogsNews,PK,-14.46,-1.34,-1.17,14.57
BlogsNews,PK,-4.21,2.18,3.79,1.26
BlogsNews,PK,-4.96,-2.99,3.39,2.47
BlogsNews,PK,-5.48,0.65,5.31,6.08
BlogsNews,PK,-4.53,-2.95,-7.79,-0.81
BlogsNews,PK,6.31,-9.89,-5.78,-5.13
BlogsTech,PK,-11.16,8.72,-5.53,8.86
BlogsTech,PK,-1.27,5.56,-3.92,-2.72
BlogsTech,PK,-11.49,0.26,-1.48,7.09
BlogsTech,PK,-0.9,-1.2,-2.03,-7.02
BlogsTech,PK,-12.27,-0.07,5.04,8.8
BlogsTech,PK,6.85,1.27,-11.95,-10.79
BlogsTech,PK,-5.21,-0.89,-6,-2.4
BlogsTech,PK,-1.06,-4.8,-8.62,-2.42
BlogsTech,PK,-2.6,-4.58,-2.07,-3.25
BlogsTech,PK,-0.95,2,-2.2,-3.46
BlogsTech,PK,-0.82,7.94,-4.95,-5.63
BlogsTech,PK,-7.65,-5.59,-3.28,-0.54
BlogsTech,PK,0.64,-1.65,-2.36,-2.68
BlogsTech,PK,-2.25,-3,-3.92,-4.87
BlogsTech,PK,-1.58,-1.42,-0.38,-5.15
Columns,PK,-5.73,3.26,0.81,-0.55
Columns,PK,0.37,-0.37,-0.28,-1.56
Columns,PK,-5.46,-4.28,2.61,1.29
Columns,PK,-3.48,2.38,12.87,3.73
Columns,PK,0.88,-2.24,-1.74,3.65
Columns,PK,-2.11,4.51,8.95,2.47
Columns,PK,-10.13,10.73,9.47,-0.47
Columns,PK,-2.08,1.04,0.11,0.6
Columns,PK,-4.33,5.65,2,-0.77
Columns,PK,1.09,-0.24,-0.92,-0.17
Columns,PK,-4.23,-4.01,-2.32,6.26
Columns,PK,-1.46,-1.53,9.83,5.73
Columns,PK,9.37,-1.32,1.27,-4.12
Columns,PK,5.84,-2.42,-5.21,1.07
Columns,PK,8.21,-9.36,-5.87,-3.21
Columns,PK,7.34,-7.3,-2.94,-5.86
Columns,PK,1.83,-2.77,1.47,-4.02
BlogsInd.,PK,14.39,-0.55,-5.42,-4.7
BlogsInd.,US,22.02,-1.39,2.5,-3.12
BlogsInd.,US,4.83,-3.58,5.34,9.22
BlogsInd.,US,-3.24,2.83,-5.3,-2.07
BlogsInd.,US,-5.69,15.17,-14.27,-1.62
BlogsInd.,US,-22.92,4.1,5.79,-3.88
BlogsNews,US,0.41,-2.03,-6.5,2.81
BlogsNews,US,-4.42,8.49,-8.04,2.04
BlogsNews,US,-10.72,-4.3,3.75,11.74
BlogsNews,US,-11.29,2.01,0.67,8.9
BlogsNews,US,-2.89,0.08,-1.59,7.06
BlogsNews,US,-7.59,8.51,3.02,12.33
BlogsNews,US,-7.45,23.51,2.79,0.48
BlogsNews,US,-12.49,15.79,-9.86,18.29
BlogsTech,US,-11.59,6.38,11.79,-7.28
BlogsTech,US,-4.6,4.12,7.46,3.36
BlogsTech,US,-22.83,2.54,10.7,5.09
BlogsTech,US,-4.83,3.37,-8.12,-0.9
BlogsTech,US,-14.76,29.21,6.23,9.33
Columns,US,-15.93,12.85,19.47,-0.88
Columns,US,-2.78,-1.52,8.16,0.24
Columns,US,-16.39,13.08,11.07,7.56

即使我尝试在y轴上添加详细的比例尺，但我仍然很难为每个箱形图确定确切的中位数.因此，我需要在每个箱线图中打印中间值.还有另一个可用的答案(用于多面boxplot )，这对我不起作用，因为打印的值不在框内，而是卡在中间.能够在箱形图的中线(中线以上)上打印它们将是很棒的. 谢谢你的帮助. 我做了如下的分组图. 添加

Even though I have tried to add detailed scale on y-axis, it is hard for me to pinpoint exact median score for each boxplot. So I need to print median value within each boxplot. There was another answer available (for faceted boxplot) which does not work for me as the printed values are not within the boxes but jammed together in the middle. It will be great to be able to print them within (middle and above the median line of) boxplots. Thanks for your help. I make a grouped graph as below. Add

推荐答案

library(dplyr)
dims=dims%>%
  group_by(Blog,Region)%>%
  mutate(med=median(Dim1))
plotgraph <- function(x, y, colour, min, max)
{
  plot1 <- ggplot(dims, aes(x = x, y = y, fill = Region)) +
    geom_boxplot()+
    labs(color='Region') + 
    geom_hline(yintercept = 0, alpha = 0.4)+
    scale_y_continuous(breaks=c(seq(min,max,5)), limits = c(min, max))+
    labs(x="Blog Type", y="Dimension Score") + scale_fill_grey(start = 0.3, end = 0.7) + 
    theme_grey()+
    theme(legend.justification = c(1, 1), legend.position = c(1, 1))+
    geom_text(aes(y = med,x=x, label = round(med,2)),position=position_dodge(width = 0.8),size = 3, vjust = -0.5,colour="blue")
  return(plot1)
}
plot1 <- plotgraph (Blog, Dim1, Region, -30, 25)

给出哪个(可以将文本颜色调整为较不俗气的内容):

注意:您应该考虑在函数中使用非标准评估，而不是要求使用attach()

Which gives (the text colour can be tweaked to something less tacky):

Note: You should consider using non-standard evaluation in your function rather than having it require the use of attach()

修改:
一种衬板，不是我想要的那么干净，因为我遇到了dplyr的问题，尽管该数据说已执行了分组，但仍未正确汇总数据. 此函数假定数据帧始终被称为dims

One liner, not as clean I wanted it to be since I ran into problems with dplyr not properly aggregating the data even though it says the grouping was performed. This function assume the dataframe is always called dims

library(ggplot2)
library(reshape2)
plotgraph <- function(x, y, colour, min, max)
{
  plot1 <- ggplot(dims, aes_string(x = x, y = y, fill = colour)) +
    geom_boxplot()+
    labs(color=colour) + 
    geom_hline(yintercept = 0, alpha = 0.4)+
    scale_y_continuous(breaks=c(seq(min,max,5)), limits = c(min, max))+
    labs(x="Blog Type", y="Dimension Score") +
    scale_fill_grey(start = 0.3, end = 0.7) + 
    theme_grey()+
    theme(legend.justification = c(1, 1), legend.position = c(1, 1))+
    geom_text(data= melt(with(dims, tapply(eval(parse(text=y)),list(eval(parse(text=x)),eval(parse(text=colour))), median)),varnames=c("Blog","Region"),value.name="med"),
              aes_string(y = "med",x=x, label = "med"),position=position_dodge(width = 0.8),size = 3, vjust = -0.5,colour="blue")
  return(plot1)
}
plot1 <- plotgraph ("Blog", "Dim1", "Region", -30, 25)

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