It seemingly appears to be a simple question but it boggles around 2 fundamental questions. A) How to deal with a multi-scalar data while presenting in a comparative chart, and secondly, B) whether this can be done without some thumb rule practices of R programming such as i) melting data, ii) faceting, iii) adding another layer to existing one. The solution given below satisfies both the above conditions as it deals data without having to rescale it and secondly, the techniques mentioned are not used.

这是结果，

如果有兴趣了解更多关于此方法的信息，请点击下面的链接。 如何绘制一个2 y轴图表与条形并排而不重新缩放数据

Kohske大约在3年前提供了解决这一挑战的技术骨干。在Stackoverflow [id: 18989001, 29235405, 21026598]的几个实例中已经讨论过这个主题及其解决方案的技术细节。因此，我将只提供一个特定的变化和一些解释性演练，使用上述解决方案。

让我们假设我们确实在组G1中有一些数据y1，而组G2中的一些数据y2以某种方式相关，例如范围/比例转换或添加了一些噪声。我们想把数据画在一张图上，左边是y1右边是y2。

  df <- data.frame(item=LETTERS[1:n],  y1=c(-0.8684, 4.2242, -0.3181, 0.5797, -0.4875), y2=c(-5.719, 205.184, 4.781, 41.952, 9.911 )) # made up!

> df
  item      y1         y2
1    A -0.8684 -19.154567
2    B  4.2242 219.092499
3    C -0.3181  18.849686
4    D  0.5797  46.945161
5    E -0.4875  -4.721973

如果我们现在把数据画在一起

ggplot(data=df, aes(label=item)) +
  theme_bw() + 
  geom_segment(aes(x='G1', xend='G2', y=y1, yend=y2), color='grey')+
  geom_text(aes(x='G1', y=y1), color='blue') +
  geom_text(aes(x='G2', y=y2), color='red') +
  theme(legend.position='none', panel.grid=element_blank())

它并没有很好地对齐，因为小尺度y1明显被大尺度y2折叠了。

这里应对挑战的技巧是在技术上根据第一个尺度y1绘制两个数据集，但根据二级轴报告第二个数据集，并使用标签显示原始尺度y2。

因此，我们构建了第一个辅助函数CalcFudgeAxis，它计算并收集要显示的新轴的特征。这个函数可以被修改成任意的形式(这个函数只是将y2映射到y1的范围上)。

CalcFudgeAxis = function( y1, y2=y1) {
  Cast2To1 = function(x) ((ylim1[2]-ylim1[1])/(ylim2[2]-ylim2[1])*x) # x gets mapped to range of ylim2
  ylim1 <- c(min(y1),max(y1))
  ylim2 <- c(min(y2),max(y2))    
  yf <- Cast2To1(y2)
  labelsyf <- pretty(y2)  
  return(list(
    yf=yf,
    labels=labelsyf,
    breaks=Cast2To1(labelsyf)
  ))
}

什么产生了一些:

> FudgeAxis <- CalcFudgeAxis( df$y1, df$y2 )

> FudgeAxis
$yf
[1] -0.4094344  4.6831656  0.4029175  1.0034664 -0.1009335

$labels
[1] -50   0  50 100 150 200 250

$breaks
[1] -1.068764  0.000000  1.068764  2.137529  3.206293  4.275058  5.343822


> cbind(df, FudgeAxis$yf)
  item      y1         y2 FudgeAxis$yf
1    A -0.8684 -19.154567   -0.4094344
2    B  4.2242 219.092499    4.6831656
3    C -0.3181  18.849686    0.4029175
4    D  0.5797  46.945161    1.0034664
5    E -0.4875  -4.721973   -0.1009335

现在我将Kohske的解决方案包装在第二个辅助函数PlotWithFudgeAxis中(我们将ggplot对象和新轴的辅助对象放入其中):

library(gtable)
library(grid)

PlotWithFudgeAxis = function( plot1, FudgeAxis) {
  # based on: https://rpubs.com/kohske/dual_axis_in_ggplot2
  plot2 <- plot1 + with(FudgeAxis, scale_y_continuous( breaks=breaks, labels=labels))

  #extract gtable
  g1<-ggplot_gtable(ggplot_build(plot1))
  g2<-ggplot_gtable(ggplot_build(plot2))

  #overlap the panel of the 2nd plot on that of the 1st plot
  pp<-c(subset(g1$layout, name=="panel", se=t:r))
  g<-gtable_add_grob(g1, g2$grobs[[which(g2$layout$name=="panel")]], pp$t, pp$l, pp$b,pp$l)

  ia <- which(g2$layout$name == "axis-l")
  ga <- g2$grobs[[ia]]
  ax <- ga$children[[2]]
  ax$widths <- rev(ax$widths)
  ax$grobs <- rev(ax$grobs)
  ax$grobs[[1]]$x <- ax$grobs[[1]]$x - unit(1, "npc") + unit(0.15, "cm")
  g <- gtable_add_cols(g, g2$widths[g2$layout[ia, ]$l], length(g$widths) - 1)
  g <- gtable_add_grob(g, ax, pp$t, length(g$widths) - 1, pp$b)

  grid.draw(g)
}

现在可以将所有内容放在一起:下面的代码显示了建议的解决方案如何在日常环境中使用。plot调用现在不再绘制原始数据y2，而是一个克隆版本yf(保存在预先计算的辅助对象FudgeAxis中)，它以y1的规模运行。然后使用Kohske的辅助函数PlotWithFudgeAxis操作原始ggplot对象，以添加第二个轴，保留y2的刻度。它的情节和被操纵的情节一样。

FudgeAxis <- CalcFudgeAxis( df$y1, df$y2 )

tmpPlot <- ggplot(data=df, aes(label=item)) +
      theme_bw() + 
      geom_segment(aes(x='G1', xend='G2', y=y1, yend=FudgeAxis$yf), color='grey')+
      geom_text(aes(x='G1', y=y1), color='blue') +
      geom_text(aes(x='G2', y=FudgeAxis$yf), color='red') +
      theme(legend.position='none', panel.grid=element_blank())

PlotWithFudgeAxis(tmpPlot, FudgeAxis)

现在它有两个轴，左边是y1右边是y2

Above solution is, to put it straight, a limited shaky hack. As it plays with the ggplot kernel it will throw some warnings that we exchange post-the-fact scales, etc. It has to be handled with care and may produce some undesired behaviour in another setting. As well one may need to fiddle around with the helper functions to get the layout as desired. The placement of the legend is such an issue (it would be placed between the panel and the new axis; this is why I droped it). The scaling / alignment of the 2 axis is as well a bit challenging: The code above works nicely when both scales contain the "0", else one axis gets shifted. So definetly with some opportunities to improve...

如果on想要保存图片，就必须将调用包装成设备打开/关闭:

png(...)
PlotWithFudgeAxis(tmpPlot, FudgeAxis)
dev.off()

这在ggplot2中是不可能的，因为我认为具有单独y尺度的图(不是相互转换的y尺度)从根本上是有缺陷的。一些问题:

The are not invertible: given a point on the plot space, you can not uniquely map it back to a point in the data space. They are relatively hard to read correctly compared to other options. See A Study on Dual-Scale Data Charts by Petra Isenberg, Anastasia Bezerianos, Pierre Dragicevic, and Jean-Daniel Fekete for details. They are easily manipulated to mislead: there is no unique way to specify the relative scales of the axes, leaving them open to manipulation. Two examples from the Junkcharts blog: one, two They are arbitrary: why have only 2 scales, not 3, 4 or ten?

你也可能想要阅读Stephen Few关于双缩放轴在图形中的主题的冗长讨论，它们是最好的解决方案吗?

以下内容结合了Dag Hjermann的基本数据和编程，改进了user4786271创建“转换函数”的策略，以优化组合图和数据轴，并响应了浸信会的提示，这样的函数可以在R中创建。

#Climatogram for Oslo (1961-1990)
climate <- tibble(
  Month = 1:12,
  Temp = c(-4,-4,0,5,11,15,16,15,11,6,1,-3),
  Precip = c(49,36,47,41,53,65,81,89,90,84,73,55))

#y1 identifies the position, relative to the y1 axis, 
#the locations of the minimum and maximum of the y2 graph.
#Usually this will be the min and max of y1.
#y1<-(c(max(climate$Precip), 0))
#y1<-(c(150, 55))
y1<-(c(max(climate$Precip), min(climate$Precip)))

#y2 is the Minimum and maximum of the secondary axis data.
y2<-(c(max(climate$Temp), min(climate$Temp)))

#axis combines y1 and y2 into a dataframe used for regressions.
axis<-cbind(y1,y2)
axis<-data.frame(axis)

#Regression of Temperature to Precipitation:
T2P<-lm(formula = y1 ~ y2, data = axis)
T2P_summary <- summary(lm(formula = y1 ~ y2, data = axis))
T2P_summary   

#Identifies the intercept and slope of regressing Temperature to Precipitation:
T2PInt<-T2P_summary$coefficients[1, 1] 
T2PSlope<-T2P_summary$coefficients[2, 1] 


#Regression of Precipitation to Temperature:
P2T<-lm(formula = y2 ~ y1, data = axis)
P2T_summary <- summary(lm(formula = y2 ~ y1, data = axis))
P2T_summary   

#Identifies the intercept and slope of regressing Precipitation to Temperature:
P2TInt<-P2T_summary$coefficients[1, 1] 
P2TSlope<-P2T_summary$coefficients[2, 1] 


#Create Plot:
ggplot(climate, aes(Month, Precip)) +
  geom_col() +
  geom_line(aes(y = T2PSlope*Temp + T2PInt), color = "red") +
  scale_y_continuous("Precipitation", sec.axis = sec_axis(~.*P2TSlope + P2TInt, name = "Temperature")) +
  scale_x_continuous("Month", breaks = 1:12) +
  theme(axis.line.y.right = element_line(color = "red"), 
        axis.ticks.y.right = element_line(color = "red"),
        axis.text.y.right = element_text(color = "red"), 
        axis.title.y.right = element_text(color = "red")) +
  ggtitle("Climatogram for Oslo (1961-1990)")

Most noteworthy is that a new "transformation function" works better with just two data points from the data set of each axes—usually the maximum and minimum values of each set. The resulting slopes and intercepts of the two regressions enable ggplot2 to exactly pair the plots of the minimums and maximums of each axis. As user4786271 pointed out, the two regressions transform each data set and plot to the other. One transforms the break points of the first y axis to the values of the second y axis. The second transforms the data of the secondary y axis to be "normalized" according to the first y axis. The following output shows how the axis align the minimums and maximums of each dataset:

使最大值和最小值匹配可能是最合适的;但是，这种方法的另一个好处是，如果需要，可以通过更改与主轴数据相关的编程行轻松地移动与次要轴相关的绘图。下面的输出只是将y1编程行中输入的最小降水量更改为“0”，从而将最小温度水平与“0”降水水平对齐。

从:y1<-(c(max(气候$ precp)， min(气候$ precp)))

到:y1<-(c(max(气候$ precp)， 0))

请注意，生成的新回归和ggplot2如何自动调整绘图和轴，以正确地将最低温度与“0”降水水平的新“基数”对齐。同样，可以很容易地提升Temperature图，使其更加明显。下面的图是通过简单地将上面提到的线更改为:

“日元<——(c(150年,55岁))”

上面的线表示温度曲线的最大值与“150”降水水平相吻合，温度曲线的最小值与“55”降水水平相吻合。再次注意，ggplot2和由此产生的新的回归输出如何使图保持与轴的正确对齐。

以上可能不是理想的输出;然而，这是一个例子，说明了如何容易地操纵图形，并且在图和轴之间仍然有正确的关系。 Dag Hjermann的主题的结合提高了与情节对应的轴的识别。

我承认并同意哈德利(和其他人)的观点，即单独的y量表“存在根本缺陷”。说到这里，我经常希望ggplot2有这个特性——特别是当数据是宽格式的，并且我想快速地可视化或检查数据时(即仅供个人使用)。

虽然tidyverse库可以很容易地将数据转换为长格式(这样facet_grid()就可以工作)，但这个过程仍然不是简单的，如下所示:

library(tidyverse)
df.wide %>%
    # Select only the columns you need for the plot.
    select(date, column1, column2, column3) %>%
    # Create an id column – needed in the `gather()` function.
    mutate(id = n()) %>%
    # The `gather()` function converts to long-format. 
    # In which the `type` column will contain three factors (column1, column2, column3),
    # and the `value` column will contain the respective values.
    # All the while we retain the `id` and `date` columns.
    gather(type, value, -id, -date) %>%
    # Create the plot according to your specifications
    ggplot(aes(x = date, y = value)) +
        geom_line() +
        # Create a panel for each `type` (ie. column1, column2, column3).
        # If the types have different scales, you can use the `scales="free"` option.
        facet_grid(type~., scales = "free")

总有办法的。

这里有一个解决方案，允许完全任意轴而不重新缩放。其思想是生成两个除了轴以外完全相同的图，并使用cowplot包中的insert_yaxis_grob和get_y_axis函数将它们组合在一起。

library(ggplot2)
library(cowplot)

## first plot 
p1 <- ggplot(mtcars,aes(disp,hp,color=as.factor(am))) + 
    geom_point() + theme_bw() + theme(legend.position='top', text=element_text(size=16)) +
    ylab("Horse points" )+ xlab("Display size") + scale_color_discrete(name='Transmitter') +
    stat_smooth(se=F)

## same plot with different, arbitrary scale   
p2 <- p1 +
    scale_y_continuous(position='right',breaks=seq(120,173,length.out = 3),
                       labels=c('little','medium little','medium hefty'))

ggdraw(insert_yaxis_grob(p1,get_y_axis(p2,position='right')))