Y在R中发生规模变化 [英] Y-break with scale change in R
问题描述
我对R还是个新手。我同意帮助一位朋友重新绘制他的图表，但是他的一个情节设计很难重现。这是因为他插入了一个Y轴中断，然后在一个barplot上进行了比例改变。这由下面的示例图来说明。
意外的是，这证明难以实施。我尝试过使用：
barplot（）＃很难用所有元素实现，无法做到这一点
gap.barplot（）＃不允许分组的barlotots
ggplot（）＃在相当长的时间学习基础知识后发现它不会破坏轴线
请任何人有一个直观的方式来绘制这个R？
注意：我知道显示这些信息的最好方法是通过对数据进行对数转换来使其符合比例尺，但我希望通过手中的两个阴谋选项来提出这个建议。
$ b
以下给出一些汇总数据，如果任何人想要测试：
AAP性别分钟最大平均值sd
1 12d女性100.97 702.36 444.07389 197.970342
2 12d男性24.69 1090.15 469.48200 262.893780
3 18d女性195.01 4204.68 1273.72000 1105.568111
4 18d男性487.75 4941.30 1452.37937 1232.659688
5 24d女248.58 3556.11 1583.09958 925.263382
6 24d男556.60 4463.22 1589.50318 973.225661
7 3d女4.87 16.93 12.86571 4.197987
8 3d男3.23 16.35 8.13000 5.364383
9 6d女3.20 37.63 15.07500 11.502331
10 6d男4.64 94.93 28.39300 30.671206
所涉及的基本步骤与您使用的任何图形软件包相同：
所以ggplot中的一个例子可能看起来像
library （ggplot2）
dput（dat）
#structure（list（AAP = structure（c（1L，1L，2L，2L，3L，3L，4L，
＃4L，5L，5L ），.Label = c（12d，18d，24d，3d，6d），class =factor），
#Sex = structure（c（1L，2L， 1L，2L，1L，2L，1L，2L，1L，2L），。标签= c（女，
＃男），class =因子），min = c（100.97,24.69 ，195.01，
＃487.75,248.58,556.6,4.87,3.23,3.2,4.64），max = c（702.36，
＃1090.15,4204.68,4941,3356.11,4463.22,16.93,16.35，
＃37.63,94.93），平均值= c（444.07389,469.482,1273.72,1452.37937，
＃1583.09958,1589.50318,12.86571,8.13,15.075,28.393），
#sd = c（197.970342,262.89378,1105.568111,1232.659688,925.263382，
＃973.225661,4.197987,5.364383,11.502331,30.671206）），.Names = c（AAP，
＃Sex，＃ min，max，mean，sd），class =data.frame，row.names = c（NA，
＃-10L））
＃函数将数据转换为y位置
trans < - function（x）{pmin（x，40）+ 0.05 * pmax（x-40,0）}
yticks< - c（0，20，40，500，1000，1500，2000）
＃将数据转换为显示比例
dat $ mean_t < - trans（dat $ mean）
dat $ sd_up_t < - trans（dat $ mean + dat $ sd）
dat $ sd_low_t < - pmax（trans（dat $ mean - dat $ sd），1）＃
ggplot（data = dat，aes（x = AAP，y = mean_t，group = Sex，fill = Sex））+
geom_errorbar（aes（ymin = sd_low_t，ymax = sd_up_t），position =dodge ）+
geom_col（position =dodge）+
geom_rect（aes（xmin = 0，xmax = 6，ymin = 42，ymax = 48），fill =white）+
scale_y_continuous（limits = c（0，NA），breaks = trans（yticks），labels = yticks）+
labs（y =CLas的相对效价）
请注意，我没有像你的例子那样得到完全相同的错误条，并且得到的输出可能不会请ggplot2的作者Hadley Wickham。
I am still new to R. I agreed to help a friend replot his graphs however one of his plot designs is proving quite hard to reproduce. This is because he inserted a Y-axis break followed by a scale alteration on a barplot. This is illustrated by the example picture below.
Unexpectedly this is proving hard to implement. I have attempted using:
barplot() #very hard to implement with all elements, couldn't make it
gap.barplot() #does not allow for grouped barplots
ggplot() #after considerable time learning the basics found it will not allow breaking the axis
Please would anyone have an intuitive way of plotting this on R? NOTE: I know likely the best way to show this information is by log-transforming the data to make it fit the scale but I'd like to propose that with the two plot options in hands.
Some summarized data is given below if anyone would like to test with:
AAP Sex min max mean sd
1 12d Female 100.97 702.36 444.07389 197.970342
2 12d Male 24.69 1090.15 469.48200 262.893780
3 18d Female 195.01 4204.68 1273.72000 1105.568111
4 18d Male 487.75 4941.30 1452.37937 1232.659688
5 24d Female 248.58 3556.11 1583.09958 925.263382
6 24d Male 556.60 4463.22 1589.50318 973.225661
7 3d Female 4.87 16.93 12.86571 4.197987
8 3d Male 3.23 16.35 8.13000 5.364383
9 6d Female 3.20 37.63 15.07500 11.502331
10 6d Male 4.64 94.93 28.39300 30.671206
The basic steps involved are the same whichever graphics package you use:
- Transform the data into the Y scale that you want
- Provide some indication of the break in scale
- Update the y-axis to show the new scale
So an example in ggplot might look like
library(ggplot2)
dput (dat)
#structure(list(AAP = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L,
#4L, 5L, 5L), .Label = c("12d", "18d", "24d", "3d", "6d"), class = "factor"),
#Sex = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Female",
#"Male"), class = "factor"), min = c(100.97, 24.69, 195.01,
#487.75, 248.58, 556.6, 4.87, 3.23, 3.2, 4.64), max = c(702.36,
#1090.15, 4204.68, 4941.3, 3556.11, 4463.22, 16.93, 16.35,
#37.63, 94.93), mean = c(444.07389, 469.482, 1273.72, 1452.37937,
#1583.09958, 1589.50318, 12.86571, 8.13, 15.075, 28.393),
#sd = c(197.970342, 262.89378, 1105.568111, 1232.659688, 925.263382,
#973.225661, 4.197987, 5.364383, 11.502331, 30.671206)), .Names = c("AAP",
#"Sex", "min", "max", "mean", "sd"), class = "data.frame", row.names = c(NA,
#-10L))
#Function to transform data to y positions
trans <- function(x){pmin(x,40) + 0.05*pmax(x-40,0)}
yticks <- c(0, 20, 40, 500, 1000, 1500, 2000)
#Transform the data onto the display scale
dat$mean_t <- trans(dat$mean)
dat$sd_up_t <- trans(dat$mean + dat$sd)
dat$sd_low_t <- pmax(trans(dat$mean - dat$sd),1) #
ggplot(data=dat, aes(x=AAP, y=mean_t, group=Sex,fill=Sex)) +
geom_errorbar(aes(ymin=sd_low_t, ymax=sd_up_t),position="dodge") +
geom_col(position="dodge") +
geom_rect(aes(xmin=0, xmax=6, ymin=42, ymax=48), fill="white") +
scale_y_continuous(limits=c(0,NA), breaks=trans(yticks), labels=yticks) +
labs(y="Relative titer of CLas")
Note that I haven't got exactly the same error bars as you're example, and the resulting output would probably not please Hadley Wickham, the author of ggplot2.
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