在 R 中的 ggplot2 中一起使用 stat_function 和 facet_wrap [英] using stat_function and facet_wrap together in ggplot2 in R

问题描述

我正在尝试使用 ggplot2 绘制点阵类型的数据，然后在样本数据上叠加一个正态分布，以说明基础数据离正态有多远.我想让正常的 dist 在顶部具有与面板相同的均值和标准差.

I am trying to plot lattice type data with ggplot2 and then superimpose a normal distribution over the sample data to illustrate how far off normal the underlying data is. I would like to have the normal dist on top to have the same mean and stdev as the panel.

这是一个例子:

library(ggplot2)

#make some example data
dd<-data.frame(matrix(rnorm(144, mean=2, sd=2),72,2),c(rep("A",24),rep("B",24),rep("C",24)))
colnames(dd) <- c("x_value", "Predicted_value",  "State_CD")

#This works
pg <- ggplot(dd) + geom_density(aes(x=Predicted_value)) +  facet_wrap(~State_CD)
print(pg)

这一切都很好，并生成了一个漂亮的数据三面板图.如何在顶部添加正常的 dist?似乎我会使用 stat_function，但这失败了:

That all works great and produces a nice three panel graph of the data. How do I add the normal dist on top? It seems I would use stat_function, but this fails:

#this fails
pg <- ggplot(dd) + geom_density(aes(x=Predicted_value)) + stat_function(fun=dnorm) +  facet_wrap(~State_CD)
print(pg)

stat_function 似乎与 facet_wrap 功能不符.我如何让这两个玩得很好?

It appears that the stat_function is not getting along with the facet_wrap feature. How do I get these two to play nicely?

------------EDIT---------

我试图整合以下两个答案的想法，但我仍然没有做到:

I tried to integrate ideas from two of the answers below and I am still not there:

结合使用这两个答案，我可以解决这个问题:

using a combination of both answers I can hack together this:

library(ggplot)
library(plyr)

#make some example data
dd<-data.frame(matrix(rnorm(108, mean=2, sd=2),36,2),c(rep("A",24),rep("B",24),rep("C",24)))
colnames(dd) <- c("x_value", "Predicted_value",  "State_CD")

DevMeanSt <- ddply(dd, c("State_CD"), function(df)mean(df$Predicted_value)) 
colnames(DevMeanSt) <- c("State_CD", "mean")
DevSdSt <- ddply(dd, c("State_CD"), function(df)sd(df$Predicted_value) )
colnames(DevSdSt) <- c("State_CD", "sd")
DevStatsSt <- merge(DevMeanSt, DevSdSt)

pg <- ggplot(dd, aes(x=Predicted_value))
pg <- pg + geom_density()
pg <- pg + stat_function(fun=dnorm, colour='red', args=list(mean=DevStatsSt$mean, sd=DevStatsSt$sd))
pg <- pg + facet_wrap(~State_CD)
print(pg)

这真的很接近...除了正常的 dist 绘图有问题:

which is really close... except something is wrong with the normal dist plotting:

我在这里做错了什么?

推荐答案

stat_function 旨在覆盖每个面板中的相同功能.(没有明显的方法可以将函数的参数与不同的面板匹配).

stat_function is designed to overlay the same function in every panel. (There's no obvious way to match up the parameters of the function with the different panels).

正如伊恩建议的那样，最好的方法是自己生成法线曲线，并将它们绘制为单独数据集(这是您之前出错的地方 - 合并对于这个例子，如果你仔细看，你会发现这就是为什么你会得到奇怪的锯齿图案).

As Ian suggests, the best way is to generate the normal curves yourself, and plot them as a separate dataset (this is where you were going wrong before - merging just doesn't make sense for this example and if you look carefully you'll see that's why you're getting the strange sawtooth pattern).

以下是我解决问题的方法:

Here's how I'd go about solving the problem:

dd <- data.frame(
  predicted = rnorm(72, mean = 2, sd = 2),
  state = rep(c("A", "B", "C"), each = 24)
) 

grid <- with(dd, seq(min(predicted), max(predicted), length = 100))
normaldens <- ddply(dd, "state", function(df) {
  data.frame( 
    predicted = grid,
    density = dnorm(grid, mean(df$predicted), sd(df$predicted))
  )
})

ggplot(dd, aes(predicted))  + 
  geom_density() + 
  geom_line(aes(y = density), data = normaldens, colour = "red") +
  facet_wrap(~ state) 

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