用2个y轴绘制，左边是一个y轴，右边是另一个y轴
我需要在一个图表中显示计数和显示比率的折线图，我可以单独做这两个，但是当我把它们放在一起时，第一层（即geom_bar ）的比例与第二层（即geom_line ）。
我可以将geom_line的轴向右移动吗？
有时客户想要两个尺度。 给他们“有缺陷”的发言往往是毫无意义的。 但是我确实喜欢ggplot2坚持做正确的做法。 我相信ggplot实际上是在教育普通用户关于正确的可视化技术。 </rant>
也许你可以使用faceting和scale来比较两个数据系列吗？ – 例如看这里： https ： //github.com/hadley/ggplot2/wiki/Align-two-plots-on-a-page
这在ggplot2中是不可能的，因为我相信具有不同y尺度（不是相互转换的y尺度）的图是根本上有缺陷的。 一些问题：
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这是不可逆的：给定绘图空间上的一个点，不能将其唯一映射回数据空间中的一个点。
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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撰写的双尺度数据图表研究 。
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他们很容易被误操作：没有独特的方式来指定轴的相对比例尺，使其可以操作。 Junkcharts博客的两个例子： 一个 ， 两个
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他们是武断的：为什么只有2个尺度，而不是3,4或10？
您也可以阅读Stephen Few关于图表中双尺度轴的长篇讨论， 他们是否已经是最佳解决scheme？ 。
从ggplot2 2.2.0开始，你可以像这样添加一个辅助轴（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)") )
Kohske大约三年前提供了解决这个难题的技术骨干[ KOHSKE ]。 有关其解决scheme的主题和技术性已经在Stackoverflow上的几个实例中进行了讨论[ID：18989001，29235405，21026598]。 所以我只会提供一个特定的变化和一些解释性的演练，使用上面的解决scheme。
假设我们在组G1中有一些数据y1 ，其中组G2中的一些数据y2以某种方式与其相关，例如范围/尺度变换或者添加了一些噪声。 因此，想要将数据一起绘制在一张图上，左侧为y1 ，右侧为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alignment，因为较小的y1明显地被较大的y2折叠。
这里面临的挑战是技术性地将两个数据集与第一个比例y1相比较，而将第二个数据集与次级轴相比较，其中标签显示原始比例y2 。
所以我们build立了第一个辅助函数CalcFudgeAxis来计算和收集要显示的新轴的特征。 该函数可以修改为喜欢（这只是将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$yf 1 A -0.8684 -19.154567 -0.4094344 2 B 4.2242 219.092499 4.6831656 3 C -0.3181 18.849686 0.4029175 4 D 0.5797 46.945161 1.0034664 5 E -0.4875 -4.721973 -0.1009335
现在我把第二个辅助函数PlotWithFudgeAxis （我们把ggplot对象和新轴的辅助对象） 放在Kohske的解决scheme中：
library(gtable) library(grid) PlotWithFudgeAxis = function( plot1, FudgeAxis) { # based on: https://rpubs.com/kohske/dual_axis_in_ggplot2 plot2 <- plot1 + with(FudgeAxis, scale_y_continuous( breaks=breaks, labels=labels)) #extract gtable g1<-ggplot_gtable(ggplot_build(plot1)) g2<-ggplot_gtable(ggplot_build(plot2)) #overlap the panel of the 2nd plot on that of the 1st plot pp<-c(subset(g1$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) }
现在所有的东西都可以放在一起： 下面的代码显示了如何在日常的环境中使用所提出的解决scheme 。 现在调用plot不再绘制原始数据y2 ，而是绘制了一个克隆的版本yf （保存在预先计算的帮助器对象FudgeAxis中 ），该版本运行y1的比例尺。 然后使用Kohske的辅助函数PlotWithFudgeAxis处理原始的ggplot对象，以添加保存y2的尺度的第二个轴。 它也绘制操纵阴谋。
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，按照需要绘制两个轴， y1在左边， y2在右边
以上的解决scheme是，直接，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，我们会交换事后的比例等等。它必须小心处理，并且可能会在另一个环境中产生一些不受欢迎的行为。 也可能需要使用辅助函数来获得所需的布局。 图例的位置是这样一个问题（它将被放置在面板和新的坐标轴之间;这就是为什么我把它扔掉了）。 2轴的缩放/alignment也有一定的挑战性：当两个尺度都包含“0”时，上面的代码很好地工作，否则一个轴被移位。 所以有一些机会来改善…
在想要保存图片的情况下，必须将呼叫包装到设备打开/closures中：
png(...) PlotWithFudgeAxis(tmpPlot, FudgeAxis) dev.off()
下面的文章帮助我将由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graphics（ggplot2）
这里是代码在这种情况下可能看起来像什么：
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)
对我来说，棘手的部分是搞清楚两轴之间的转换函数。 我使用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, 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, 271.173853699836, 272.175873251122, 273.177524313334, 274.182946177105, 275.188959464989, 276.189675339937, 277.198250244799, 278.204619457189, 279.212562800009, 270.164199199177, 271.168527215152, 272.173072994958, 273.179210429715, 274.184351047337, 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0.46510628979591, 0.93442584181035, 0.99894450514543, 0.99890078483692, 0.76933812306423), receivedPower_dbm = c(-93.023492290586, -92.388378035287, -92.205716340607, -93.816400586752, -95.023489422885, -100.86308557253, -98.464763536915, -96.175707680373, -102.06189538385, -99.716653422746, -93.023492290586, -92.384760627397, -92.205716340607, -93.816400586752, -95.023489422885, -100.86308557253, -98.464201120719, -96.175707680373, -102.06189538385, -99.717150021506, -93.022927803442, -92.404017215549, -92.204561341714, -93.814319484729, -95.016990717792, -102.01669022332, -98.558088145955, -96.173817001483, -102.07406915124, -99.71517574876, -93.021813165972, -92.409586309743, -92.20229160243, -93.805335867418, -96.184419849593, -102.01709540787, -99.728735187547, -96.163233028048, -99.772547164798, -99.706399753853, -93.024204617071, -92.745813384859, -92.206884754512, -93.818508150122, -95.027018807793, -100.87000577258, -98.467607232407, -95.005311380324, -102.04157607608, -99.724619517, -93.022927803442, -92.404017215549, -92.204561341714, -93.813803344588, -95.015606885523, -102.0157405687, -98.556982278361, -96.172566862738, -103.21871579865, -99.714687230796, -93.022787428238, -92.404017215549, -92.204274688493, -93.813803344588, -95.015606885523, -102.0157405687, -98.556982278361, -96.172566862738, -103.21784988098, -99.714687230796, -93.021813165972, -92.409950613665, -92.20229160243, -93.805838770576, -96.184419849593, -102.02042267497, -99.728735187547, -96.163233028048, -99.768774335378, -99.706399753853, -93.022228914406, -92.411048503835, -92.203136463155, -93.807357409082, -95.012865008237, -102.00985717796, -99.730352912911, -96.165675535906, -100.92744056572, -99.708301333236, -92.735781110993, -92.408137395049, -92.119533319039, -94.982938427575, -96.181073124017, -102.03018610927, -99.721633629806, -97.32940323644, -97.347613268692, -100.87007386786), snr = 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), frameId = c(15051, 15106, 15165, 15220, 15279, 15330, 15385, 15452, 15511, 15566, 15019, 15074, 15129, 15184, 15239, 15298, 15353, 15412, 15471, 15526, 14947, 14994, 15057, 15112, 15171, 15226, 15281, 15332, 15391, 15442, 14971, 15030, 15085, 15144, 15203, 15262, 15321, 15380, 15435, 15490, 14915, 14978, 15033, 15092, 15147, 15198, 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" ))
Finding the transformation function
- y1 –> y2 This function is used to transform the data of the secondary y axis to be "normalized" according to the first y axis
transformation function: f(y1) = 0.025*x + 2.75
- y2 –> y1 This function is used to transform the break points of the first y axis to the values of the second y axis. Note that the axis are swapped now.
transformation function: f(y1) = 40*x - 110
Plotting
Note how the transformation functions are used in the ggplot call to transform the data "on-the-fly"
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") )
The first stat_summary call is the one that sets the base for the first y axis. The second stat_summary call is called to transform the data. Remember that all of the data will take as base the first y axis. So that data needs to be normalized for the first y axis. To do that I use the transformation function on the data: y=packetOkSinr*40 - 110
Now to transform the second axis I use the opposite function within the scale_y_continuous call: sec.axis=sec_axis(~.*0.025+2.75, name="y_second") .
We definitely could build a plot with dual Y-axises using base R funtion 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))
You can use facet_wrap(~ variable, ncol= ) on a variable to create a new comparison. It's not on the same axis, but it is similar.
The answer by Hadley gives an interesting reference to Stephen Few's report Dual-Scaled Axes in Graphs Are They Ever the Best Solution? 。
I do not know what the OP means with "counts" and "rate" but a quick search gives me Counts and Rates , so I get some data about Accidents in North American Mountaineering 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
And then I tried to do the graph as Few suggested at page 7 of the aforementioned report (and following the request of OP to graph the counts as a bar chart and the rates as a line chart) :
The other less obvious solution, which works only for time series, is to convert all sets of values to a common quantitative scale by displaying percentage differences between each value and a reference (or index) value. For instance, select a particular point in time, such as the first interval that appears in the graph, and express each subsequent value as the percentage difference between it and the initial value. This is done by dividing the value at each point in time by the value for the initial point in time and then multiplying it by 100 to convert the rate to a percentage, as illustrated below.
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)) )
这就是结果： 
But I do not like it a lot and I am not able to easily put a legend on it…
1 WILLIAMSON, Jed, et al. Accidents in North American Mountaineering 2005. The Mountaineers Books, 2005.
I acknowledge and agree with hadley (and others), that separate y-scales are "fundamentally flawed". Having said that – I often wish ggplot2 had the feature – particularly, when the data is in wide-format and I quickly want to visualise or check the data (ie for personal use only).
While the tidyverse library makes it fairly easy to convert the data to long-format (such that facet_grid() will work), the process is still not trivial, as seen below:
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")
