ggplot，每侧有2个y轴，并且比例不同

231

我需要在一张图表中绘制显示计数的条形图和显示利率的折线图，我可以分别将它们都做，但是当我将它们放在一起时，第一层的比例（即geom_bar）与第二层的比例重叠层（即geom_line）。

我可以geom_line向右移动轴吗？

r ggplot2 r-faq

— 乐心
source

5

您能否使用rpubs.com/kohske/dual_axis_in_ggplot2这样的方法？

— Tom Wenseleers

相关

— Axeman'2

2

向下滚动以查看其中的本机ggplot2实现scale_y_*，当前称为sec.axis。

— PatrickT

106

有时客户想要两个y标度。给他们“有缺陷的”言论通常是毫无意义的。但是我喜欢ggplot2坚持以正确的方式做事。我确信ggplot实际上正在教育普通用户正确的可视化技术。

也许您可以使用构面和自由缩放比较这两个数据系列？-例如，请看这里：https : //github.com/hadley/ggplot2/wiki/Align-two-plots-on-a-page

— 安德烈亚斯
source

30

我同意安德烈亚斯（Andreas）的观点-有时（例如，对我而言），客户希望在同一图上获得两组数据，而又不想听我谈论图论。我要么说服他们不再想要（我并不总是想打一场仗），要么告诉他们“我使用的绘图程序不支持这一点。” 因此，今天我将不再为此特定项目使用ggplot。=（

— 肯·威廉姆斯

58

为什么绘图程序包需要在操作方式中插入自己的个人见解？不，谢谢。

— colin 2014年

5

您的链接已消失。您能否编辑您的答案并发布其过去所说的摘要？

— Zach

24

无法同意此评论（请求）。为了尽可能快速地传达信息，尽可能地压缩信息是非常（！）现象，例如，由于科学期刊等施加了严格的限制。因此，无论如何都要添加第二个y轴，我认为ggplot应该可以帮助实现这一点。

— Stingery '16

57

令人惊讶的是，像“有瑕疵的”和“正确的方式”这样的词语毫无疑问地被扔掉了，好像它们并不是基于一种实际上是固执己见和教条主义的理论一样，但却被太多人毫无疑问地接受了，正如我们所看到的那样。在撰写本文时，这个完全无用的答案（抛出链接）的事实有72个投票。WHE 比较时间序列，例如，它可以是非常宝贵的同时具有相同的图表上，由于不同的相关性是很容易被发现。只需询问成千上万每天整天都做这件事的金融专业人士。

— 托马斯·布朗

149

在ggplot2中是不可能的，因为我认为具有单独y比例（而不是彼此转换的y比例）的图从根本上来说是有缺陷的。一些问题：

不可逆：给定绘图空间上的一个点，您无法将其唯一地映射回数据空间中的一个点。
与其他选项相比，它们相对难以正确阅读。有关详细信息，请参见Petra Isenberg，Anastasia Bezerianos，Pierre Dragicevic和Jean-Daniel Fekete的“ 双尺度数据图表研究”。
它们很容易被误导：没有独特的方法来指定轴的相对比例，使它们易于操作。Junkcharts博客中的两个示例：一个，两个
它们是任意的：为什么只有2个标度，而不是3、4或10？

您可能还想阅读Stephen Few关于图形中的双标度轴的冗长讨论，它们是最佳解决方案吗？。

— 哈德利
source

39

您介意阐述您的意见吗？没有启发，我认为这是绘制两个自变量的相当紧凑的方法。这也是一个似乎需要的功能，并且被广泛使用。

— 2010年

66

@hadley：我大都同意，但是确实有多个y刻度的用途-对同一数据使用2个不同的单位，例如温度时间序列上的摄氏和华氏刻度。

— Richie Cotton

11

@Hadley在您看来。不是我的，也不是其他许多科学家。当然，可以通过将第二个绘图（具有完全透明的背景）直接放在第一个绘图上来实现，因此它们看起来像一个。我只是不知道如何确保边界boxex的角相互对齐/对齐。

— 尼古拉斯·汉密尔顿

8

@hadley例如，在Walther-Lieth气候图中，通常使用两个y轴。由于有固定的处方，怎么办才能将可能的混乱降到最低...

— sebschub 2014年

32

@hadley我很抱歉，我看不到给定的气候图表有什么问题。将温度和降水绘制在一张图表中（有固定的处方），可以快速猜测是潮湿还是干旱的气候。还是绕过去：可视化温度，降水量及其“关系”的更好方法是什么？无论如何，非常感谢您在ggplot2中所做的工作！

— sebschub 2014年

121

从ggplot2 2.2.0开始，您可以添加这样的辅助轴（摘自ggplot2 2.2.0公告）：

ggplot(mpg, aes(displ, hwy)) + 
  geom_point() + 
  scale_y_continuous(
    "mpg (US)", 
    sec.axis = sec_axis(~ . * 1.20, name = "mpg (UK)")
  )

— tstenner
source

25

缺点是，它只能使用当前轴的某些公式转换，例如不能使用新变量。

— 徒弟

41

采取上述答案并进行一些微调（无论其价值如何），这是通过以下方法实现两个量表的一种方法sec_axis：

假设一个简单的（纯粹是虚构的）数据集dt：连续五天，它跟踪中断次数与生产率之间的关系：

        when numinter prod
1 2018-03-20        1 0.95
2 2018-03-21        5 0.50
3 2018-03-23        4 0.70
4 2018-03-24        3 0.75
5 2018-03-25        4 0.60

（两列的范围相差约5倍）。

以下代码将绘制两个序列，它们消耗了整个y轴：

ggplot() + 
  geom_bar(mapping = aes(x = dt$when, y = dt$numinter), stat = "identity", fill = "grey") +
  geom_line(mapping = aes(x = dt$when, y = dt$prod*5), size = 2, color = "blue") + 
  scale_x_date(name = "Day", labels = NULL) +
  scale_y_continuous(name = "Interruptions/day", 
    sec.axis = sec_axis(~./5, name = "Productivity % of best", 
      labels = function(b) { paste0(round(b * 100, 0), "%")})) + 
  theme(
      axis.title.y = element_text(color = "grey"),
      axis.title.y.right = element_text(color = "blue"))

结果如下（上面的代码+一些颜色调整）：

要点（除了sec_axis在指定y_scale时使用之外，还要在指定序列时将第二个数据系列的每个值乘以 5。为了在sec_axis定义中正确获取标签，然后需要将其除以 5（并格式化）。因此上面代码中的关键部分实际上*5是在geom_line和~./5sec_axis中（将当前值.除以5 的公式）。

相比之下（我不想在这里判断方法），这就是两个相互重叠的图表的样子：

您可以自己判断哪个更好地传达了信息（“不要打扰工作中的人！”）。猜猜这是一个公平的决定方式。

两个图像的完整代码（这不是真的不止是头上，只是完成并准备运行）是在这里：https://gist.github.com/sebastianrothbucher/de847063f32fdff02c83b75f59c36a7d这里更详细的解释：https：//开头sebastianrothbucher。 github.io/datascience/r/visualization/ggplot/2018/03/24/two-scales-ggplot-r.html

— 塞巴斯蒂安·罗斯布彻
source

31

有共同的用例决斗y轴，例如，climatograph表示每月温度和沉淀。这是一个简单的解决方案，从威震天的解决方案中推广而来，它允许您将变量的下限设置为零以外的其他值：

示例数据：

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)
  )

将以下两个值设置为接近数据限制的值（您可以使用它们来调整图形的位置；轴仍然正确）：

ylim.prim <- c(0, 180)   # in this example, precipitation
ylim.sec <- c(-4, 18)    # in this example, temperature

以下基于这些限制进行必要的计算，并绘制图本身：

b <- diff(ylim.prim)/diff(ylim.sec)
a <- b*(ylim.prim[1] - ylim.sec[1])

ggplot(climate, aes(Month, Precip)) +
  geom_col() +
  geom_line(aes(y = a + Temp*b), color = "red") +
  scale_y_continuous("Precipitation", sec.axis = sec_axis(~ (. - a)/b, name = "Temperature")) +
  scale_x_continuous("Month", breaks = 1:12) +
  ggtitle("Climatogram for Oslo (1961-1990)")

如果要确保红线对应于右侧的y轴，则可以theme在代码中添加一个句子：

ggplot(climate, aes(Month, Precip)) +
  geom_col() +
  geom_line(aes(y = a + Temp*b), color = "red") +
  scale_y_continuous("Precipitation", sec.axis = sec_axis(~ (. - a)/b, 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)")

为右轴着色：

— 达格·赫尔曼
source

这处断裂的一些价值观ylim.prim和ylim.sec。

— 埃里克·克兰兹

5

这很棒。两轴图表没有“瑕疵”的好例子。认为他们比您更了解您的工作的部分整理思路是其中的一部分。

— Leo Barlach

当我选择特定的轴限制时（在我的情况下为ylim.prim <-c（90，130）和ylim.sec <-c（15，30）），它并不适用，而是选择任意限制，将所有比例尺弄乱了。我不确定在复制上面的代码并更改变量名和轴限制时缺少的内容

— Anke，

@anke：当引用ylim.prim和ylim.sec时，文本有些草率。它们不是指轴的限制，而是指数据的限制。如前所述，当您设置ylim.prim <-c（90，130）和ylim.sec <-c（15，30）时，温度图最终在条形图上方结束（因为温度轴从-75开始），但每个图形的轴仍然正确。

— 达格·赫尔曼

16

您可以创建一个缩放系数，该缩放系数将应用于第二个几何图形和右y轴。这源自塞巴斯蒂安的解决方案。

library(ggplot2)

scaleFactor <- max(mtcars$cyl) / max(mtcars$hp)

ggplot(mtcars, aes(x=disp)) +
  geom_smooth(aes(y=cyl), method="loess", col="blue") +
  geom_smooth(aes(y=hp * scaleFactor), method="loess", col="red") +
  scale_y_continuous(name="cyl", sec.axis=sec_axis(~./scaleFactor, name="hp")) +
  theme(
    axis.title.y.left=element_text(color="blue"),
    axis.text.y.left=element_text(color="blue"),
    axis.title.y.right=element_text(color="red"),
    axis.text.y.right=element_text(color="red")
  )

注意：使用ggplot2 v3.0.0

— 威震天
source

14

技术骨干到这一挑战的解决方案已经提供Kohske一些3年前[ KOHSKE。在此处的Stackoverflow [ID：18989001、29235405、21026598]的多个实例上讨论了该主题及其解决方案的技术性。因此，我将仅使用上述解决方案提供特定的变化形式和一些解释性的演练。

让我们假设我们确实有一些数据Y1组G1到一些数据Y2组G2在某种程度上是相关的，如范围/规模转化或添加一些噪音。因此，人们希望将数据一起绘制在一个图上，y1的标度在左侧，y2的标度在右侧。

  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

如果我们现在将数据与类似

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())

由于较小的比例尺y1被较大的比例尺y2塌陷，它不能很好地对准。

应对挑战的技巧是在技术上将两个数据集相对于第一个比例尺y1绘制，但针对第二个轴报告第二个数据集，并用标签显示原始比例尺y2。

因此，我们构建了第一个辅助函数CalcFudgeAxis，该函数计算并收集了要显示的新轴的特征。可以将函数修改为不喜欢（此函数仅将y2映射到y1的范围内）。

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 （将新轴的ggplot对象和helper对象放入该函数中）：

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：下面的代码显示了如何在日常环境中使用建议的解决方案。现在，plot调用不再绘制原始数据y2，而是绘制一个克隆版本yf（保留在预先计算的帮助对象FudgeAxis中），该版本的缩放比例为y1。然后使用Kohske的辅助函数PlotWithFudgeAxis处理原始的ggplot对象，以添加第二个轴来保留y2的比例。它也绘制了操纵图。

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)

这个现在图解为期望具有两个轴，Y1在左侧和Y2在右侧

直截了当，上述解决方案是有限的摇摇欲坠。当它与ggplot内核一起使用时，将发出一些警告，通知我们交换事后的标度等。必须谨慎处理它，并且在其他情况下可能会产生某些不良行为。同样，可能需要四处寻找帮助功能以获取所需的布局。图例的位置就是一个问题（它将放置在面板和新轴之间；这就是为什么我将其放置）。2轴的缩放/对齐也有些挑战：当两个比例都包含“ 0”时，上面的代码可以很好地工作，否则一个轴会移动。因此肯定会有一些改善的机会...

如果要保存图片，则必须将呼叫包装到设备打开/关闭中：

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

— CK
source

9

以下文章帮助我将ggplot2生成的两个图合并到一行上：

Cookbook for R在一页上的多个图形（ggplot2）

在这种情况下，代码如下所示：

p1 <- 
  ggplot() + aes(mns)+ geom_histogram(aes(y=..density..), binwidth=0.01, colour="black", fill="white") + geom_vline(aes(xintercept=mean(mns, na.rm=T)), color="red", linetype="dashed", size=1) +  geom_density(alpha=.2)

p2 <- 
  ggplot() + aes(mns)+ geom_histogram( binwidth=0.01, colour="black", fill="white") + geom_vline(aes(xintercept=mean(mns, na.rm=T)), color="red", linetype="dashed", size=1)  

multiplot(p1,p2,cols=2)

— Stas Prihod'co
source

多图功能发生了什么？尽管安装并加载了ggplot2库，但我收到一个找不到该功能的错误。

— Nneka '17

1

@Danka多图功能是一个自定义功能（在链接页面的底部）。

— Dribbel '17

您可以添加地块吗？

— 斯波江

最近，有很多的包，有更多的选项/功能比multiplot stackoverflow.com/a/51220506

— 东

7

对我来说，棘手的部分是弄清楚两个轴之间的转换函数。我为此使用了myCurveFit。

> dput(combined_80_8192 %>% filter (time > 270, time < 280))
structure(list(run = c(268L, 268L, 268L, 268L, 268L, 268L, 268L, 
268L, 268L, 268L, 263L, 263L, 263L, 263L, 263L, 263L, 263L, 263L, 
263L, 263L, 269L, 269L, 269L, 269L, 269L, 269L, 269L, 269L, 269L, 
269L, 261L, 261L, 261L, 261L, 261L, 261L, 261L, 261L, 261L, 261L, 
267L, 267L, 267L, 267L, 267L, 267L, 267L, 267L, 267L, 267L, 265L, 
265L, 265L, 265L, 265L, 265L, 265L, 265L, 265L, 265L, 266L, 266L, 
266L, 266L, 266L, 266L, 266L, 266L, 266L, 266L, 262L, 262L, 262L, 
262L, 262L, 262L, 262L, 262L, 262L, 262L, 264L, 264L, 264L, 264L, 
264L, 264L, 264L, 264L, 264L, 264L, 260L, 260L, 260L, 260L, 260L, 
260L, 260L, 260L, 260L, 260L), repetition = c(8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), module = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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    41.55465351989, 31.509340361646, 6.2867464196657, 13.941251828322, 
    24.140336174865, 4.7666691818074, 10.679016976694, 49.867624820879, 
    57.412299088098, 60.224172277442, 41.630930975211, 24.074540962859, 
    6.279972363168, 10.644529778044, 24.19227425387, 10.546845071479, 
    10.699414795917, 49.862851240855, 57.397787176282, 60.212457625018, 
    41.61637603957, 31.529239767749, 6.2952688513108, 10.640565481982, 
    24.178672145334, 8.0771089950663, 10.694731030907, 53.262541905639, 
    57.43627424514, 61.382796189332, 31.747253311549, 24.093100244121, 
    6.2658701281075, 10.661949889074, 18.495227442305, 18.417839037171, 
    8.1845086722809), ookSnirBer = c(8.8808636558081e-24, 3.2219795637026e-27, 
    2.6468895519653e-28, 3.9807779074715e-20, 1.0849324265615e-15, 
    2.5705217057696e-05, 4.7313805615763e-08, 1.8800438086075e-12, 
    0.00021005320203921, 1.9147343768384e-06, 8.8808636558081e-24, 
    3.0694773489537e-27, 2.6468895519653e-28, 3.9807779074715e-20, 
    1.0849324265615e-15, 2.5705217057696e-05, 4.7223753038869e-08, 
    1.8800438086075e-12, 0.00021005320203921, 1.9171738578051e-06, 
    8.8229427230445e-24, 3.9715925056443e-27, 2.6045198111088e-28, 
    3.9014083702734e-20, 1.0342658440386e-15, 0.00019591630514278, 
    6.4692014108683e-08, 1.8600094209271e-12, 0.0002140067535655, 
    1.9074922485477e-06, 8.7096574467175e-24, 4.2779443633862e-27, 
    2.5231916788231e-28, 3.5761615214425e-20, 1.9750692814982e-12, 
    0.0001960392878411, 1.9748966344895e-06, 1.7515881895994e-12, 
    2.2078334799411e-06, 1.8649940680806e-06, 8.954486301678e-24, 
    3.2021085732779e-25, 2.690441113724e-28, 4.0627628846548e-20, 
    1.1134484878561e-15, 2.6061691733331e-05, 4.777159157954e-08, 
    9.4891388749738e-16, 0.00020359398491544, 1.9542110660398e-06, 
    8.8229427230445e-24, 3.9715925056443e-27, 2.6045198111088e-28, 
    3.8819641115984e-20, 1.0237769828158e-15, 0.00019562832342849, 
    6.4455095380046e-08, 1.8468752030971e-12, 0.0010099091367628, 
    1.9051035165106e-06, 8.8085966897635e-24, 3.9715925056443e-27, 
    2.594108048185e-28, 3.8819641115984e-20, 1.0237769828158e-15, 
    0.00019562832342849, 6.4455095380046e-08, 1.8468752030971e-12, 
    0.0010088638355194, 1.9051035165106e-06, 8.7096574467175e-24, 
    4.2987746909572e-27, 2.5231916788231e-28, 3.593647329558e-20, 
    1.9750692814982e-12, 0.00019705170257492, 1.9748966344895e-06, 
    1.7515881895994e-12, 2.1868296425817e-06, 1.8649940680806e-06, 
    8.7517439682173e-24, 4.3621551072316e-27, 2.553168170837e-28, 
    3.6469582463164e-20, 1.0032983660212e-15, 0.00019385229409318, 
    1.9830820164805e-06, 1.7760568361323e-12, 2.919419915209e-05, 
    1.8741284335866e-06, 2.8285944348148e-25, 4.1960751547207e-27, 
    7.8468215407139e-29, 8.0407329049747e-16, 1.9380328071065e-12, 
    0.00020004849911333, 1.9393279417733e-06, 5.9354475879597e-10, 
    6.4258355913627e-10, 2.6065221215415e-05), ookSnrBer = c(8.8808636558081e-24, 
    3.2219795637026e-27, 2.6468895519653e-28, 3.9807779074715e-20, 
    1.0849324265615e-15, 2.5705217057696e-05, 4.7313805615763e-08, 
    1.8800438086075e-12, 0.00021005320203921, 1.9147343768384e-06, 
    8.8808636558081e-24, 3.0694773489537e-27, 2.6468895519653e-28, 
    3.9807779074715e-20, 1.0849324265615e-15, 2.5705217057696e-05, 
    4.7223753038869e-08, 1.8800438086075e-12, 0.00021005320203921, 
    1.9171738578051e-06, 8.8229427230445e-24, 3.9715925056443e-27, 
    2.6045198111088e-28, 3.9014083702734e-20, 1.0342658440386e-15, 
    0.00019591630514278, 6.4692014108683e-08, 1.8600094209271e-12, 
    0.0002140067535655, 1.9074922485477e-06, 8.7096574467175e-24, 
    4.2779443633862e-27, 2.5231916788231e-28, 3.5761615214425e-20, 
    1.9750692814982e-12, 0.0001960392878411, 1.9748966344895e-06, 
    1.7515881895994e-12, 2.2078334799411e-06, 1.8649940680806e-06, 
    8.954486301678e-24, 3.2021085732779e-25, 2.690441113724e-28, 
    4.0627628846548e-20, 1.1134484878561e-15, 2.6061691733331e-05, 
    4.777159157954e-08, 9.4891388749738e-16, 0.00020359398491544, 
    1.9542110660398e-06, 8.8229427230445e-24, 3.9715925056443e-27, 
    2.6045198111088e-28, 3.8819641115984e-20, 1.0237769828158e-15, 
    0.00019562832342849, 6.4455095380046e-08, 1.8468752030971e-12, 
    0.0010099091367628, 1.9051035165106e-06, 8.8085966897635e-24, 
    3.9715925056443e-27, 2.594108048185e-28, 3.8819641115984e-20, 
    1.0237769828158e-15, 0.00019562832342849, 6.4455095380046e-08, 
    1.8468752030971e-12, 0.0010088638355194, 1.9051035165106e-06, 
    8.7096574467175e-24, 4.2987746909572e-27, 2.5231916788231e-28, 
    3.593647329558e-20, 1.9750692814982e-12, 0.00019705170257492, 
    1.9748966344895e-06, 1.7515881895994e-12, 2.1868296425817e-06, 
    1.8649940680806e-06, 8.7517439682173e-24, 4.3621551072316e-27, 
    2.553168170837e-28, 3.6469582463164e-20, 1.0032983660212e-15, 
    0.00019385229409318, 1.9830820164805e-06, 1.7760568361323e-12, 
    2.919419915209e-05, 1.8741284335866e-06, 2.8285944348148e-25, 
    4.1960751547207e-27, 7.8468215407139e-29, 8.0407329049747e-16, 
    1.9380328071065e-12, 0.00020004849911333, 1.9393279417733e-06, 
    5.9354475879597e-10, 6.4258355913627e-10, 2.6065221215415e-05
    )), class = "data.frame", row.names = c(NA, -100L), .Names = c("run", 
"repetition", "module", "configname", "packetByteLength", "numVehicles", 
"dDistance", "time", "distanceToTx", "headerNoError", "receivedPower_dbm", 
"snr", "frameId", "packetOkSinr", "snir", "ookSnirBer", "ookSnrBer"
))

寻找变换函数

y1-> y2 此函数用于根据第一个y轴转换要“归一化”的第二个y轴的数据

转换功能： f(y1) = 0.025*x + 2.75

y2-> y1 此功能用于将第一个y轴的断点转换为第二个y轴的值。请注意，现在已交换轴。

转换功能： f(y1) = 40*x - 110

绘图

请注意如何在调用中使用转换函数来ggplot“即时”转换数据

ggplot(data=combined_80_8192 %>% filter (time > 270, time < 280), aes(x=time) ) +
  stat_summary(aes(y=receivedPower_dbm ), fun.y=mean, geom="line", colour="black") +
  stat_summary(aes(y=packetOkSinr*40 - 110 ), fun.y=mean, geom="line", colour="black", position = position_dodge(width=10)) +
  scale_x_continuous() +
  scale_y_continuous(breaks = seq(-0,-110,-10), "y_first", sec.axis=sec_axis(~.*0.025+2.75, name="y_second") )

第一个stat_summary调用是为第一个y轴设置基准的调用。stat_summary调用第二个调用以转换数据。请记住，所有数据都将第一个y轴作为基础。因此，需要针对第一个y轴对数据进行标准化。为此，我对数据使用了转换功能：y=packetOkSinr*40 - 110

现在，要变换第二个轴，我在scale_y_continuous调用中使用相反的函数：sec.axis=sec_axis(~.*0.025+2.75, name="y_second")。

— 用户名
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2

R可以做这种事情，coef(lm(c(-70, -110) ~ c(1,0)))并且coef(lm(c(1,0) ~ c(-70, -110)))。您可以定义一个辅助函数，例如

equationise <- function(range = c(-70, -110), target = c(1,0)){   c = coef(lm(target ~ range))     as.formula(substitute(~ a*. + b, list(a=c[[2]], b=c[[1]]))) }

— baptiste'Apr

是的，我知道...只是以为网站会更直观

— user4786271

4

我们绝对可以使用base R函数构建具有两个Y轴的图plot。

# pseudo dataset
df <- data.frame(x = seq(1, 1000, 1), y1 = sample.int(100, 1000, replace=T), y2 = sample(50, 1000, replace = T))

# plot first plot 
with(df, plot(y1 ~ x, col = "red"))

# set new plot
par(new = T) 

# plot second plot, but without axis
with(df, plot(y2 ~ x, type = "l", xaxt = "n", yaxt = "n", xlab = "", ylab = ""))

# define y-axis and put y-labs
axis(4)
with(df, mtext("y2", side = 4))

— 演示版
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1

您可以使用facet_wrap(~ variable, ncol= )变量来创建新的比较。它不在同一条轴上，但是相似。

— 猜猜
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1

我承认并同意hadley（和其他公司）的观点，即单独的y尺度存在“根本缺陷”。话虽这么说–我经常希望ggplot2拥有该功能–特别是当数据为宽格式并且我想快速查看或检查数据时（即仅供个人使用）。

虽然该tidyverse库使将数据转换为长格式非常容易（这样就facet_grid()可以使用），但是该过程仍然不容易，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")

— 会
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在编写ggplot2时，它已经通过提供了支持sec_axis。

— 康拉德·鲁道夫

0

Hadley的答案为Stephen Few的报告“ 图形中的双比例轴是否是最好的解决方案”提供了有趣的参考。。

我不知道是什么OP手段与“罪状”和“速度”，而是一个快速搜索给我计数和速率，所以我得到有关在北美登山事故的一些数据¹：

Years<-c("1998","1999","2000","2001","2002","2003","2004")
Persons.Involved<-c(281,248,301,276,295,231,311)
Fatalities<-c(20,17,24,16,34,18,35)
rate=100*Fatalities/Persons.Involved
df<-data.frame(Years=Years,Persons.Involved=Persons.Involved,Fatalities=Fatalities,rate=rate)
print(df,row.names = FALSE)

 Years Persons.Involved Fatalities      rate
  1998              281         20  7.117438
  1999              248         17  6.854839
  2000              301         24  7.973422
  2001              276         16  5.797101
  2002              295         34 11.525424
  2003              231         18  7.792208
  2004              311         35 11.254019

然后，我尝试按照上述报告第7页中的建议（并根据OP的要求以条形图和线形图的形式绘制计数）来制作图形：

另一个不太明显的解决方案（仅适用于时间序列）是通过显示每个值与参考（或指数）值之间的百分比差异，将所有值集转换为通用的定量标度。例如，选择一个特定的时间点，例如图形中出现的第一个间隔，然后将每个后续值表示为它与初始值之间的百分比差。这是通过将每个时间点的值除以初始时间点的值，然后将其乘以100来将比率转换为百分比来完成的，如下所示。

df2<-df
df2$Persons.Involved <- 100*df$Persons.Involved/df$Persons.Involved[1]
df2$rate <- 100*df$rate/df$rate[1]
plot(ggplot(df2)+
  geom_bar(aes(x=Years,weight=Persons.Involved))+
  geom_line(aes(x=Years,y=rate,group=1))+
  theme(text = element_text(size=30))
  )

结果如下：

但是我不太喜欢它，我不能轻易地在上面画上一个传奇...

1 _{WILLIAMSON，Jed等。2005年北美登山运动中的事故。登山者图书，2005年。}

— 亚历山德罗·贾科普森（Alessandro Jacopson）
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0

看来这似乎是一个简单的问题，但它围绕两个基本问题感到困惑。A）如何在比较图中显示时如何处理多标量数据；其次，B）是否可以在不使用R编程的一些经验法则的情况下完成此操作，例如i）合并数据，ii）分面，iii）添加现有的另一层。下面给出的解决方案满足了以上两个条件，因为它无需重新缩放即可处理数据，其次，不使用上述技术。

结果是这样

对于那些有兴趣更多地了解这种方法的人，请点击下面的链接。如何在不重新缩放数据的情况下并排绘制带有杆的2-y轴图

— 安布赖斯达卡
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0

我发现这个答案对我的帮助最大，但发现有些边缘情况似乎无法正确处理，特别是否定情况，还有我的极限距离为0的情况（如果我们抢劫的话会发生这种情况）我们根据数据的最大/最小限制）。测试似乎表明这始终如一

我使用以下代码。在这里，我假设我们想将[x1，x2]转换为[y1，y2]。我处理此问题的方法是将[x1，x2]转换为[0,1]（足够简单的转换），然后将[0,1]转换为[y1，y2]。

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)
)
#Set the limits of each axis manually:

  ylim.prim <- c(0, 180)   # in this example, precipitation
ylim.sec <- c(-4, 18)    # in this example, temperature



  b <- diff(ylim.sec)/diff(ylim.prim)

#If all values are the same this messes up the transformation, so we need to modify it here
if(b==0){
  ylim.sec <- c(ylim.sec[1]-1, ylim.sec[2]+1)
  b <- diff(ylim.sec)/diff(ylim.prim)
}
if (is.na(b)){
  ylim.prim <- c(ylim.prim[1]-1, ylim.prim[2]+1)
  b <- diff(ylim.sec)/diff(ylim.prim)
}


ggplot(climate, aes(Month, Precip)) +
  geom_col() +
  geom_line(aes(y = ylim.prim[1]+(Temp-ylim.sec[1])/b), color = "red") +
  scale_y_continuous("Precipitation", sec.axis = sec_axis(~((.-ylim.prim[1]) *b  + ylim.sec[1]), name = "Temperature"), limits = ylim.prim) +
  scale_x_continuous("Month", breaks = 1:12) +
  ggtitle("Climatogram for Oslo (1961-1990)")

这里的关键部分是我们用变换辅助y轴，~((.-ylim.prim[1]) *b + ylim.sec[1])然后将反值应用于实际值y = ylim.prim[1]+(Temp-ylim.sec[1])/b)。我们还应确保limits = ylim.prim。

— 基兰·马丁（Kieran Martin）
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