我需要绘制一个柱状图来显示计数，同时又要绘制一条折线图来显示比率，两者需要在同一张图表中呈现。我可以分别绘制它们，但当我将它们放在一起时，第一层（即 geom_bar）的比例尺被第二层（即 geom_line）遮盖。
我能否将 geom_line 的坐标轴移到右侧？
18个回答
200
从ggplot2 2.2.0开始，您可以像这样添加第二个轴（摘自ggplot2 2.2.0公告）：
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
scale_y_continuous(
"mpg (US)",
<b>sec.axis = sec_axis(~ . * 1.20, name = "mpg (UK)")</b>
)
- tstenner
2
49缺点是，它只能使用当前轴的某些公式转换，而不能使用新变量，例如。 - discipulus
5但是你可以转换新变量，使其具有与旧变量大致相同的范围，然后使用 sec_axis 显示标签，将新变量放回其原始比例。 - John Paul
172
在ggplot2中，不可能实现这一点，因为我相信具有独立y轴刻度（而不是互相变换的y轴刻度）的图表从根本上存在缺陷。以下是一些问题：
它们不可逆：给定图形空间中的一个点，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。请参见Petra Isenberg、Anastasia Bezerianos、Pierre Dragicevic和Jean-Daniel Fekete的文章《双刻度数据图表研究》了解详情。
它们很容易被操纵误导：没有唯一的方法来指定坐标轴的相对比例，使它们容易受到操纵。Junkcharts博客上的两个例子：一个，两个
它们是任意的：为什么只有2个刻度，而不是3、4或10个？
你可能还想阅读Stephen Few的详细讨论：图表中的双刻度轴——它们真的是最好的解决方案吗？。
- hadley
8
56您介意详细阐述一下您的观点吗？在没有得到启发的情况下，我认为这是一种相当简洁的绘制两个独立变量的方法。同时，它似乎是一个被广泛使用和要求的特性。 - KarlP
85大多数情况下我同意，但是使用多个y轴有其真正的用途——在温度时间序列上使用不同单位的两个数据，例如摄氏度和华氏度刻度。 - Richie Cotton
26在你看来，但不是在我或其他许多科学家看来。当然，这可以通过将第二个图绘制在第一个图的正上方（背景完全透明）来实现，使它们看起来像一个图。我只是不知道如何确保边界框的角落对齐/注册。 - Nicholas Hamilton
19例如，在[沃尔特-利思气候图表]（http://commons.wikimedia.org/wiki/Category:Climate_diagrams_system_Walter%2BLieth）中，通常使用两个y轴。由于有固定的规则，如何进行操作，因此可能会产生的混淆最小化... - sebschub
42@hadley对不起，我没有看出这个气候图表有什么问题。将温度和降水量放在同一个图表中（使用固定的标度），可以快速初步判断是湿润或干旱气候。或者反过来问：有什么更好的方式来展示温度、降水量及它们之间的“关系”吗？无论如何，非常感谢您在ggplot2中的工作！ - sebschub
显示剩余3条评论
135
有时客户想要两个y轴。告诉他们“有缺陷”的说法通常是毫无意义的。但我确实喜欢ggplot2坚持按正确方式处理事情的做法。我相信ggplot实际上正在教育普通用户正确的可视化技术。
也许您可以使用分面和自由比例来比较两个数据系列？-例如：看这里：https://github.com/hadley/ggplot2/wiki/Align-two-plots-on-a-page
也许您可以使用分面和自由比例来比较两个数据系列？-例如：看这里：https://github.com/hadley/ggplot2/wiki/Align-two-plots-on-a-page
- Andreas
9
38我同意Andreas的观点-有时（比如现在，对于我来说），客户想在同一张图上显示两组数据，而不想听我谈论绘图理论。我要么说服他们不再需要这样做（这不是我想要进行的战斗），要么告诉他们“我使用的绘图包不支持这种操作”。所以今天我将在这个特定项目中停止使用ggplot。=( - Ken Williams
89绘图软件包为什么需要在其操作中插入自己的个人观点？谢谢，不需要。 - colin
33不同意这条评论（关于抱怨的）。尽可能地压缩信息是很常见的，例如受科学期刊等严格限制，为了快速传递信息。因此，添加第二个y轴已经成为一种常见做法，我认为ggplot应该帮助完成这个操作。 - Stingery
90令人惊讶的是，“有缺陷”和“正确的方式”等词汇被毫无疑问地使用，好像它们不是基于一种理论，这种理论本身实际上相当主观和教条，但由于太多人不加思考地接受了它，可以看出这个完全没有帮助的答案（其中包含一个链接）在撰写时已经获得了72个赞。例如，在比较时间序列时，将它们放在同一张图表上可能非常有价值，因为差异的相关性更容易被发现。只需向那些每天都在做这件事情的成千上万位受过高度教育的金融专业人士询问即可。 - Thomas Browne
7我同意，ggplot 绝对百分之百需要双轴。每天有成千上万的人将继续使用双轴，在 R 中拥有这些功能将是极好的，这是一个痛苦的疏忽。我正在将数据从 R 转移到 Excel。 - user3022875
显示剩余4条评论
100
存在一些通用的双Y轴使用场景，例如展示月平均气温和降水量的气象图。这里提供了一个简单的解决方案，是建立在 Megatron 的基础上并允许您设置变量的下限而不一定是零：
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极限的值（您可以尝试调整这些值以调整图表位置；轴仍将正确）：
将以下两个值设置为靠近数据极限的值（您可以根据需要进行微调，来调整图表位置；坐标轴将保持正确）：
ylim.prim <- c(0, 180) # in this example, precipitation
ylim.sec <- c(-4, 18) # in this example, temperature
b <- diff(ylim.prim)/diff(ylim.sec)
a <- ylim.prim[1] - b*ylim.sec[1]) # there was a bug here
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)")
如果您想确保红色线对应右侧的y轴，您可以向代码添加一个theme语句:
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)")
将右侧轴线的颜色更改为红色：
- Dag Hjermann
5
这会在某些
ylim.prim 和 ylim.sec 的值处中断。 - Eric Krantz30这很棒。这是一个很好的例子，说明双轴图并非“有缺陷”。这是整个tidyverse思维方式的一部分，他们认为自己比你更了解你的工作。 - Leo Barlach
当我选择特定的轴限制（在我的情况下，ylim.prim <- c(90, 130)和ylim.sec <- c(15, 30)），它并没有应用它，而是选择了任意的限制，破坏了所有的比例尺。我不确定我漏掉了什么，因为我复制了上面的代码，只改变了变量名和轴限制。 - Anke
@anke：当它涉及到ylim.prim和ylim.sec时，文本有些混乱。它们并不是指轴的限制，而是指您的数据的限制。当您设置ylim.prim <- c(90, 130)和ylim.sec <- c(15, 30)时，如您所提到的，温度图高于条形图（因为温度轴从-75开始），但每个图的轴仍然是正确的。 - Dag Hjermann
@DagHjermann 在你输入
ylim.prim[1] - b*ylim.sec[1]) 后面写了 "there was a bug here"，我认为最后不应该有一个 )。 - undefined65
您可以创建一个缩放因子，该因子应用于第二个geom和右y轴。这是从Sebastian的解决方案中推导出来的。
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
- Megatron
3
10这是一个简洁的解决方案。 - landroni
5比起其他二次坐标轴方案，scaleFactor 是我见过的最优雅的解决方案！ - Momchill
非常（合乎逻辑）和优雅的解决方案。
双手赞成 - SilSur64
根据以上回答，并进行一些微调后（不管值不值得），以下是通过 sec_axis 实现两个比例尺的方法：
假设有一个简单的（纯虚构的）数据集 dt: 在五天内，它跟踪了干扰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
（两列数据的范围相差约为5倍）。
以下代码将绘制这两个系列，以便它们占用整个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"))
这是上面代码加一些调色后的结果：
除了使用sec_axis来指定y轴比例以外，重点是在指定数据系列时将第二个数据系列的每个值乘以5。为了正确设置sec_axis中的标签，需要除以5（并进行格式化）。因此，以上代码中一个至关重要的部分是在geom_line中的*5和在sec_axis中的~./5（一个公式将当前值.除以5）。
相比之下（我不想评判这两种方法），这就是堆叠在一起的两个图表的样子：
你可以自己判断哪一个更好地传达了信息（“不要打扰工作中的人！”）。我想这是一个公平的决定方式。
两张图片的完整代码（实际上不会比以上内容多太多，只是完整而已，可以直接运行）在这里：https://gist.github.com/sebastianrothbucher/de847063f32fdff02c83b75f59c36a7d 更详细的解释在这里：https://sebastianrothbucher.github.io/datascience/r/visualization/ggplot/2018/03/24/two-scales-ggplot-r.html
- Sebastian Rothbucher
1
这是一个巧妙的技巧，可以通过虚拟分面获得两个不同的比例尺！我想知道是否有比使用全局变量更“正当”的方法？如果数据中的一列可以用作
scale_y_continuous的labels参数的输入，那就太好了。 - James Hirschorn21
以下是如何为副轴进行转换的建议。首先，您需要将主要数据和次要数据的范围耦合起来。通常情况下，这会使您的全局环境中充斥着您不想要的变量，非常混乱。
为了使这个过程更容易，我们将创建一个函数工厂，生成两个函数，其中scales :: rescale（）完成所有重要工作。因为这些是闭包，它们知道它们被创建的环境，所以它们“有记忆”之前生成的to和from参数。
一个函数执行正向转换：将次要数据转换为主要比例尺。 第二个函数执行反向转换：将主单位中的数据转换为次要单位。
然后我们使用
为了使这个过程更容易，我们将创建一个函数工厂，生成两个函数，其中scales :: rescale（）完成所有重要工作。因为这些是闭包，它们知道它们被创建的环境，所以它们“有记忆”之前生成的to和from参数。
一个函数执行正向转换：将次要数据转换为主要比例尺。 第二个函数执行反向转换：将主单位中的数据转换为次要单位。
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)
}
这似乎相当复杂，但是制作函数工厂可以使所有其他操作变得更加容易。现在，在我们制作图表之前，我们将通过向工厂展示主要和次要数据来生成相关函数。我们将使用经济数据集，该数据集的unemploy和psavert列具有非常不同的范围。
sec <- with(economics, train_sec(unemploy, psavert))
然后我们使用
y = sec$fwd(psavert)将辅助数据重新调整到主轴，并将~ sec$rev(.)指定为辅助轴的转换参数。这样，我们就可以得到一个图表，其中主轴和辅助轴的范围在图表上占据相同的空间。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"))
工厂比这个稍微灵活一些，因为如果你只想重新缩放最大值，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"))
该示例创建于2021年02月05日，使用reprex package（v0.3.0）。
我承认在此示例中差异并不是非常明显，但如果您仔细观察，就会发现最大值相同，而红线比蓝线更低。
编辑：
此方法现已在ggh4x软件包的help_secondary()函数中进行捕获和扩展。免责声明：我是ggh4x的作者。
- teunbrand
4
1这是一个很好的解决方案 - 我唯一要补充的是，在那些范围函数中加入"rm.na = TRUE"，以防被绘制的数据有一些NA值。 - Henry Holm
这是一个好建议，谢谢！我已经将它包含在上面的答案中了。 - teunbrand
@HenryHolm，你是如何移除第二轴的NA值的？我认为这是导致我出现“！辅助轴的转换必须是单调的”错误的原因。 - Brian Wiley
这个应该排名更高——它是一个看似简单而优雅的方法来解决问题，而不需要使用丑陋的hack。对于那些0.1%的情况，双轴实际上是一个好主意。 - Fluffy
14
The technical solution for this challenge was provided by Kohske about 3 years ago [KOHSKE]. The topic and technical details regarding its solution have been discussed on several occasions here on Stackoverflow [IDs: 18989001, 29235405, 21026598]. Therefore, I will only provide a specific variation and an explanatory walkthrough using the aforementioned solutions.
Let us assume that we have some data y1 in group G1, to which some data y2 in group G2 is related in some way (e.g. range/scale transformed or with some noise added). So, one wants to plot the data together on one plot, with the scale of y1 on the left and y2 on the right.
如果我们现在将数据与类似的内容绘制在一起，会得到以下结果：
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一起传入）：
现在所有的内容都可以整合起来：以下代码展示了如何在日常环境中使用所提出的解决方案。现在，plot调用不再绘制原始数据y2，而是克隆版本yf（保存在预先计算的辅助对象FudgeAxis中），该版本运行在y1的比例尺上。然后使用Kohske的辅助函数PlotWithFudgeAxis操纵原始ggplot对象，以添加保持y2比例尺的第二个轴。它还绘制了经过操作的图形。
Let us assume that we have some data y1 in group G1, to which some data y2 in group G2 is related in some way (e.g. range/scale transformed or with some noise added). So, one wants to plot the data together on one plot, with the scale of y1 on the left and y2 on the right.
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内容绘制在一起，会得到以下结果：
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())
它不能很好地对齐，因为较小比例的y1被较大比例的y2明显压缩。
在这里应对挑战的诀窍是技术上绘制两个数据集针对第一个比例y1，但报告第二个针对具有显示原始比例y2标签的次要轴。
因此，我们构建了第一个帮助函数CalcFudgeAxis，该函数计算并收集要显示的新轴的特征。该函数可以根据任何人的喜好进行修改（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)
))
}
什么会产生一些：
> 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$yf
1 A -0.8684 -19.154567 -0.4094344
2 B 4.2242 219.092499 4.6831656
3 C -0.3181 18.849686 0.4029175
4 D 0.5797 46.945161 1.0034664
5 E -0.4875 -4.721973 -0.1009335
现在我将 Kohske 的解决方案包装在第二个辅助函数 PlotWithFudgeAxis 中（我们将 ggplot 对象和新轴的辅助对象一起传入）：
library(gtable)
library(grid)
PlotWithFudgeAxis = function( plot1, FudgeAxis) {
# based on: https://rpubs.com/kohske/dual_axis_in_ggplot2
plot2 <- plot1 + with(FudgeAxis, scale_y_continuous( breaks=breaks, labels=labels))
#extract gtable
g1<-ggplot_gtable(ggplot_build(plot1))
g2<-ggplot_gtable(ggplot_build(plot2))
#overlap the panel of the 2nd plot on that of the 1st plot
pp<-c(subset(g1$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)
}
现在所有的内容都可以整合起来：以下代码展示了如何在日常环境中使用所提出的解决方案。现在，plot调用不再绘制原始数据y2，而是克隆版本yf（保存在预先计算的辅助对象FudgeAxis中），该版本运行在y1的比例尺上。然后使用Kohske的辅助函数PlotWithFudgeAxis操纵原始ggplot对象，以添加保持y2比例尺的第二个轴。它还绘制了经过操作的图形。
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，这个图表已经按照要求绘制出来了，有两个轴，左侧是y1，右侧是y2
如果想要保存图片，则必须将调用包装到设备打开/关闭中：
png(...)
PlotWithFudgeAxis(tmpPlot, FudgeAxis)
dev.off()
- C.K
10
以下文章帮助我将由ggplot2生成的两个图合并在一行中：
R菜谱书中ggplot2绘制多图于一页
这是这种情况下代码的示例：
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
4
多图绘制函数出了什么问题？尽管我已经安装并加载了ggplot2库，但我收到了一个找不到该函数的错误。 - Nneka
1@Danka 多图绘制函数是一个自定义函数（位于链接页面底部）。 - Dribbel
你能添加这个情节吗？ - SJ9
最近，有很多软件包比
multiplot拥有更多的选项/功能。https://dev59.com/F2sz5IYBdhLWcg3wTl4T#51220506/ - Tung7
对我来说最困难的部分是找出两个轴之间的转换函数。我使用了myCurveFit进行计算。
> 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,
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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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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,
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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,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L,
8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L, 8192L
), numVehicles = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), dDistance = c(80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L), time = c(270.166006903445,
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15257, 15312, 15371, 15430, 14975, 15034, 15089, 15140, 15195,
15254, 15313, 15368, 15427, 15478, 14987, 15046, 15105, 15160,
15215, 15274, 15329, 15384, 15447, 15506, 14943, 15002, 15061,
15116, 15171, 15230, 15285, 15344, 15399, 15454, 14971, 15026,
15081, 15136, 15195, 15258, 15313, 15368, 15423, 15478, 15039,
15094, 15149, 15204, 15263, 15314, 15369, 15428, 15487, 15546
), packetOkSinr = c(0.99999999314881, 0.9999999998736, 0.99999999996428,
0.99999952114066, 0.99991568416005, 3.00628034688444e-08,
0.51497487795954, 0.99627877136019, 0, 0.011303253101957,
0.99999999314881, 0.99999999987726, 0.99999999996428, 0.99999952114066,
0.99991568416005, 3.00628034688444e-08, 0.51530974419663,
0.99627877136019, 0, 0.011269851265775, 0.9999999931708,
0.99999999985986, 0.99999999996428, 0.99999952599145, 0.99991770469509,
0, 0.45861812482641, 0.99629897628155, 0, 0.011403119534097,
0.99999999321568, 0.99999999985437, 0.99999999996519, 0.99999954639936,
0.99618434878558, 0, 0.010513119213425, 0.99641022914441,
0.00801687746446111, 0.012011103529927, 0.9999999931195,
0.99999999871861, 0.99999999996428, 0.99999951617905, 0.99991456738049,
2.6525298291169e-08, 0.51328066587104, 0.9999212220316, 0,
0.010777054258914, 0.9999999931708, 0.99999999985986, 0.99999999996428,
0.99999952718674, 0.99991812902805, 0, 0.45929307038653,
0.99631228046814, 0, 0.011436292559188, 0.99999999317629,
0.99999999985986, 0.99999999996428, 0.99999952718674, 0.99991812902805,
0, 0.45929307038653, 0.99631228046814, 0, 0.011436292559188,
0.99999999321568, 0.99999999985437, 0.99999999996519, 0.99999954527918,
0.99618434878558, 0, 0.010513119213425, 0.99641022914441,
0.00821047996950475, 0.012011103529927, 0.99999999319919,
0.99999999985345, 0.99999999996519, 0.99999954188106, 0.99991896371849,
0, 0.010410830482692, 0.996384831822, 9.12484388049251e-09,
0.011877185067536, 0.99999999879646, 0.9999999998562, 0.99999999998077,
0.99992756868677, 0.9962208785486, 0, 0.010971897073662,
0.93214999078663, 0.92943956665979, 2.64925478221656e-08),
snir = c(49.848348091678, 57.698190927109, 60.17669971462,
41.529809724535, 31.452202106925, 8.1976890851341, 14.240447804094,
24.122884195464, 6.2202875499406, 10.674183333671, 49.848348091678,
57.746270018264, 60.17669971462, 41.529809724535, 31.452202106925,
8.1976890851341, 14.242292077376, 24.122884195464, 6.2202875499406,
10.672962852322, 49.854827699773, 57.49079026127, 60.192705735317,
41.549715223147, 31.499301851462, 6.2853718719014, 13.937702343688,
24.133388256416, 6.2028757927148, 10.677815810561, 49.867624820879,
57.417115267867, 60.224172277442, 41.635752021705, 24.074540962859,
6.2847854917092, 10.644529778044, 24.19227425387, 10.537686730745,
10.699414795917, 49.84017267426, 53.139646558768, 60.160512118809,
41.509660845114, 31.42665220053, 8.1846370024428, 14.231126423354,
31.584125885363, 6.2494585568733, 10.654622041348, 49.854827699773,
57.49079026127, 60.192705735317, 41.55465351989, 31.509340361646,
6.2867464196657, 13.941251828322, 24.140336174865, 4.765718874642,
10.679016976694, 49.856439162736, 57.49079026127, 60.196678846453,
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"
))
查找转换函数
- y1 --> y2 这个函数用于将第二个y轴的数据根据第一个y轴进行“标准化”转换。
转换函数: f(y1) = 0.025*x + 2.75
- y2 --> y1 这个函数用于将第一个y轴的断点转换为第二个y轴的值。注意此时轴已经交换了。
转换函数: f(y1) = 40*x - 110
绘图
请注意在ggplot调用中如何使用转换函数即时转换数据。
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 调用为第一个 y 轴设置基础。
第二个 stat_summary 调用被称为数据转换。请记住，所有数据都将以第一个 y 轴为基础。因此，需要对第一个 y 轴进行归一化处理。为此，我在数据上使用了转换函数：y=packetOkSinr*40 - 110
现在，为了转换第二个轴，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")。
- user4786271
2
2R可以做这种事情，例如
coef(lm(c(-70, -110) ~ c(1,0)))和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是的，我知道...只是觉得这个网站会更直观一些。 - user4786271
网页内容由stack overflow 提供, 点击上面的可以查看英文原文，
原文链接
原文链接
scale_y_*中已经被称为sec.axis的本地ggplot2实现。 - PatrickT