绘制2 y轴,左侧为y轴,右侧为y轴

Question

我需要绘制一个显示计数的条形图和一个在一个图表中显示速率的折线图,我可以分别做两个,但是当我把它们放在一起时,我的第一层的比例(即geom_bar)与第二层重叠层(即geom_line).

我可以将轴geom_line向右移动吗？

Answer 1

这在ggplot2中是不可能的,因为我认为具有单独y尺度的图(不是彼此变换的y尺度)从根本上是有缺陷的.一些问题:

• 这是不可逆的:给定绘图空间上的一个点,您无法将其唯一映射回数据空间中的某个点.

• 与其他选项相比,它们相对难以正确阅读.有关详细信息,请参阅Petra Isenberg,Anastasia Bezerianos,Pierre Dragicevic和Jean-Daniel Fekete的双尺度数据图表研究.

• 它们很容易被误操作:没有独特的方法来指定轴的相对比例,使它们处于操作状态.来自Junkcharts博客的两个例子:一,二

• 它们是任意的:为什么只有2个刻度,而不是3个,4个或10个？

您还可以阅读Stephen Few关于图表中双重轴的主题的冗长讨论它们是否是最佳解决方案？.

Answer 2

从ggplot2 2.2.0开始,您可以添加这样的辅助轴(取自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)")
  )

Answer 3

有时客户想要两个y尺度.给他们"有缺陷"的演讲往往毫无意义.但我确实喜欢ggplot2坚持以正确的方式做事.我确信ggplot实际上是教育普通用户正确的可视化技术.

也许你可以使用faceting和scale来比较两个数据系列？- 例如,请看:https://github.com/hadley/ggplot2/wiki/Align-two-plots-on-a-page

Answer 4

采取以上答案和一些微调(以及任何它的价值),这是一种通过sec_axis以下方式实现两个尺度的方法:

假设一个简单的(纯粹虚构的)数据集dt:五天,它跟踪中断的数量VS生产力:

        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).

以下代码将绘制它们用完整个y轴的两个系列:

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"))

这是结果(上面的代码+一些颜色调整):

要点(除了sec_axis指定y_scale时使用的是在指定系列时将第2个数据系列的每个值乘以 5.为了在sec_axis定义中获得标签,它需要除以 5(和格式化).所以上面代码中的一个关键部分实际上*5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),这是两个图表在彼此之上的样子:

你可以自己判断哪一个更能传达信息("不要扰乱工作中的人!").猜猜这是一个公平的决定方式.

两个图像的完整代码(这不是真的比什么上面,刚刚完成并准备运行更多)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

Answer 5

这是我关于如何进行辅助轴转换的两点建议。首先，您想要耦合主要数据和辅助数据的范围。这通常很混乱，因为您不想要的变量会污染您的全局环境。

为了使这更容易，我们将创建一个函数工厂来生成两个函数，其中scales::rescale()完成所有繁重的工作。因为这些是闭包，所以它们知道创建它们的环境，因此它们“具有创建之前生成的to和from参数的记忆”。

• 一个函数执行正向转换：将辅助数据转换为主要比例。
• 第二个函数执行反向转换：将主要单位的数据转换为辅助单位。

library(ggplot2)
library(scales)

# Function factory for secondary axis transforms
train_sec <- function(primary, secondary, na.rm = TRUE) {
  # Thanks Henry Holm for including the na.rm argument!
  from <- range(secondary, na.rm = na.rm)
  to   <- range(primary, na.rm = na.rm)
  # Forward transform for the data
  forward <- function(x) {
    rescale(x, from = from, to = to)
  }
  # Reverse transform for the secondary axis
  reverse <- function(x) {
    rescale(x, from = to, to = from)
  }
  list(fwd = forward, rev = reverse)
}

这看起来相当复杂，但是使用函数工厂使其余的一切变得更容易。现在，在绘制绘图之前，我们将通过向工厂显示主要和辅助数据来生成相关函数。我们将使用经济学数据集，该数据集的unemploy和psavert列范围非常不同。

sec <- with(economics, train_sec(unemploy, psavert))

然后我们使用y = sec$fwd(psavert)将辅助数据重新缩放到主轴，并将其指定~ sec$rev(.)为辅助轴的变换参数。这给我们提供了一个图，其中主要范围和次要范围在图上占据相同的空间。

ggplot(economics, aes(date)) +
  geom_line(aes(y = unemploy), colour = "blue") +
  geom_line(aes(y = sec$fwd(psavert)), colour = "red") +
  scale_y_continuous(sec.axis = sec_axis(~sec$rev(.), name = "psavert"))

工厂比这稍微灵活一些，因为如果你只是想重新调整最大值，你可以传入下限为 0 的数据。

# Rescaling the maximum
sec <- with(economics, train_sec(c(0, max(unemploy)),
                                 c(0, max(psavert))))

ggplot(economics, aes(date)) +
  geom_line(aes(y = unemploy), colour = "blue") +
  geom_line(aes(y = sec$fwd(psavert)), colour = "red") +
  scale_y_continuous(sec.axis = sec_axis(~sec$rev(.), name = "psavert"))

^{由reprex 包(v0.3.0)于 2021-02-05 创建}

我承认这个例子中的差异不是很明显，但是如果你仔细观察，你会发现最大值是相同的，并且红线比蓝线低。

编辑：

现在，这种方法已在help_secondary()ggh4x 包的函数中被捕获和扩展。免责声明：我是 ggh4x 的作者。

Answer 6

有一些常见的使用情况决定y轴,例如显示月温度和降水量的气候照相.这是一个简单的解决方案,从Megatron的解决方案中推广,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)
  )

手动设置每个轴的限制:

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

以下内容根据这些限制进行必要的计算,并使图表本身:

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y轴,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)")

右手轴的颜色:

Answer 7

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.关于其解决方案的主题和技术已经在Stackoverflow上的几个实例中进行了讨论[ID:18989001,29235405,21026598].因此,我将仅使用上述解决方案提供特定的变体和一些解释性演练.

让我们假设我们确实在组G1中有一些数据y1,其中组G2中的一些数据y2以某种方式相关,例如变换范围/比例或添加了一些噪声.因此,人们希望将数据绘制在一个图上,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

如果我们现在将数据与类似的东西一起绘制

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())

它没有很好地对齐,因为较小的规模y1明显地被更大规模的y2折叠.

这里遇到挑战的技巧是在技术上绘制两个数据集与第一个比例y1,但报告第二个轴与副轴,标签显示原始比例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.该函数可以修改为ayones喜欢(这个只是将y2映射到y1的范围).

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

现在我在第二个辅助函数PlotWithFudgeAxis (我们在其中抛出新轴的ggplot对象和辅助对象)中包含了Kohske的解决方案:

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:下面的代码显示了所提出的解决方案如何在日常环境中使用.情节调用现在不再绘制原始数据y2,而是克隆版本yf(保存在预先计算的辅助对象FudgeAxis内),其运行y1的比例.然后使用Kohske的辅助函数PlotWithFudgeAxis操作原始的ggplot对象,以添加第二个轴,保留y2的比例.它也描绘了操纵的情节.

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

上面的解决方案是,直截了当,一个有限的摇摇欲坠的黑客.当它与ggplot内核一起使用时,它会抛出一些警告,我们交换事后的比例等等.它必须小心处理,并可能在另一个设置中产生一些不希望的行为.同样,可能需要使用辅助函数来调整以获得所需的布局.图例的位置是一个问题(它将放置在面板和新轴之间;这就是我下垂的原因).2轴的缩放/对齐也有点挑战性:当两个刻度包含"0"时,上面的代码很好地工作,否则一个轴会移位.所以肯定有一些改进的机会......

如果想要保存pic,必须将呼叫包装到设备打开/关闭中:

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

Answer 8

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

Answer 9

下面的文章帮助我将ggplot2生成的两个图组合在一行上:

Cookbook for R在一个页面上的多个图表(ggplot2)

以下是这种情况下代码的外观:

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)

Answer 10

总有办法的。

这是一个允许完全任意轴而无需重新缩放的解决方案。这个想法是生成两个图，除了轴之外相同，并使用包中的insert_yaxis_grob和get_y_axis函数将它们组合在一起cowplot。

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')))

Answer 11

对我来说,棘手的部分是找出两个轴之间的转换函数.我使用了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, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "scenario.node[0].nicVLCTail.phyVLC", class = "factor"), 
    configname = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L), .Label = "Road-Vlc", class = "factor"), packetByteLength = c(8192L, 
    8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 
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    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"
))

寻找转换功能

1. y1 - > y2 此功能用于根据第一个y轴将辅助y轴的数据转换为"标准化"

转换功能: f(y1) = 0.025*x + 2.75

2. y2 - > y1 此函数用于将第一个y轴的断点转换为第二个y轴的值.请注意,轴现在已交换.

转换功能: f(y1) = 40*x - 110

绘制

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轴设置基础的调用.stat_summary调用第二个调用来转换数据.请记住,所有数据都将作为第一个y轴的基础.因此,需要针对第一个y轴对数据进行归一化.为此,我在数据上使用转换函数:y=packetOkSinr*40 - 110

现在要转换第二个轴,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").

Answer 12

我们绝对可以使用基础 R 函数构建一个具有双 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))