如何在ggplot2中绘制函数族 [英] How to plot a function family in ggplot2

问题描述

我需要根据一组参数绘制一组变量函数，比如说一组依赖于均值和标准差的正态分布曲线。
我发现

我的问题是系列中的曲线数量可能是，因此我试图按照以下方式修改代码：

  aa<  - 列表（列表（0.2,0.1），列表（0.7,0.05），列表（0.45,0.2））
 p9 < -  ggplot（data.frame（x = c（0,1）），aes（x = x））
 for（i in 1：3）{
 p9 < -  p9 + stat_function（fun = dnorm，args = aa [[i]]，
 aes（color = paste（Group，i））
} 
 p9 < -  p9 + 
 scale_x_continuous name =Probability，
 breaks = seq（0,1,0.2），
 limits = c（0,1））+ 
 scale_y_continuous（name =Frequency）+ 
 ggtitle（概率的正常函数曲线）+ 
 scale_colour_brewer（palette =Accent）+ 
 labs（color =Groups）
 p9 
  

结果几乎成功这三条曲线除了它不区分颜色和图例外，如下所示：

我猜问题来自函数aes（）管理其参数的方式。关于如何重写我的代码，您有任何想法吗？

code>

快速编辑：我刚刚了解到我刚刚从@BrodieG那里学到的一个习惯用语，这在这里非常适用：您可以直接向 ggplot 调用添加一个geoms或stats列表，这可以让您为 lapply 甚至 Map 避免 Reduce 的卷积，它可以让你并行传递许多变量。结合@ JulioSergio的 aes _ 方法，您可以得到一个可读的代码很容易定制的体面情节：

  ggplot（data.frame（x = 0：1），aes（x））+ 
 Map（函数（params， name）{stat_function（mapping = aes_（color = name），
 fun = dnorm，args = params）}，
 params = aa，
 name = paste（'Group'，seq_along aa）））
  

---

减少

该结构可以很好地满足 Reduce ，其中 init 设置为最初的 ggplot 调用。可以通过索引一个调色板函数来添加颜色，它可以通过对象在这个点上的层数来获得：

x + stat_function（fun = dnorm，args = y，
 color = scales :: brewer_pal（'qual'，' （x，c（0，1））），（b $ b aa，
 init = ggplot（data.frame（x = c（0,1））， aes（x = x）））
  

这种方法的缺点是它不能制作出很好的图例，因为它是硬编码的颜色。

---

预先计算

绘图之前的计算，这使得绘图本身非常简单：

<$ c $ （列表（0.2,0.1），列表（0.7,0.05），列表（0.45,0.2））
$ b aa< - 列表（tidyverse）

aa ％>％set_names（paste（'Group'，1：3））％>％
map_df（〜dnorm（seq（0,1，length = 100），.x [[1]]）。 （x，seq（0，1，length = 100））％>％
gather（Group，y，-x）％> ;％
ggplot（aes（x，y，color = Group））+
geom_line（）

I need to plot a family of functions variying according to a set of parameters, say, a family of normal distribution curves that depend on the mean and standard deviation. I found here, a code snipet that almost do the task:

p9 <- ggplot(data.frame(x = c(0, 1)), aes(x = x)) +
    stat_function(fun = dnorm, args = list(0.2, 0.1),
                  aes(colour = "Group 1")) +
    stat_function(fun = dnorm, args = list(0.7, 0.05),
                  aes(colour = "Group 2")) +
    scale_x_continuous(name = "Probability",
                          breaks = seq(0, 1, 0.2),
                          limits=c(0, 1)) +
    scale_y_continuous(name = "Frequency") +
    ggtitle("Normal function curves of probabilities") +
    scale_colour_brewer(palette="Accent") +
    labs(colour = "Groups")
p9 

In this case, the code plots exactly two curves, as shown below:

My problem is that the number of curves in the family could be any, thus I tried to adapt the code as follows:

aa <- list(list(0.2, 0.1), list(0.7, 0.05), list(0.45, 0.2))
p9 <- ggplot(data.frame(x = c(0, 1)), aes(x = x))
for (i in 1:3) {
    p9 <- p9 + stat_function(fun = dnorm, args = aa[[i]],
                         aes(colour = paste("Group", i))
}
p9 <- p9 + 
  scale_x_continuous(name = "Probability",
                   breaks = seq(0, 1, 0.2),
                   limits=c(0, 1)) +
  scale_y_continuous(name = "Frequency") +
  ggtitle("Normal function curves of probabilities") +
  scale_colour_brewer(palette="Accent") +
  labs(colour = "Groups")
p9

The result is almost successful in that it depicts the three curves, except that it does not distinguish them by colors nor in the legend,as it is shown below:

I guess the problem arises from the manner the function aes() manages its arguments. Do you have any idea on how to rewrite my code?

解决方案

Add a list to ggplot

A quick edit: I just learned an idiom I just learned from @BrodieG that's very applicable here: you can add a list of geoms or stats directly to a ggplot call, which allows you to avoid the convolutions of Reduce for lapply or even Map, which lets you pass as many variables in parallel as you like. Combined with @JulioSergio's aes_ approach, you get a decent plot with readable code that's easily customizable:

ggplot(data.frame(x = 0:1), aes(x)) + 
    Map(function(params, name){stat_function(mapping = aes_(color = name), 
                                             fun = dnorm, args = params)}, 
        params = aa, 
        name = paste('Group', seq_along(aa)))

---

Reduce

The structure lends itself reasonably well to Reduce with init set to the initial ggplot call. Colors can be added by indexing a palette function by the number of layers in the object at that point:

Reduce(function(x, y){
    x + stat_function(fun = dnorm, args = y, 
                      colour = scales::brewer_pal('qual', 'Set1')(length(aa))[length(x$layers) + 1])}, 
    aa, 
    init = ggplot(data.frame(x = c(0, 1)), aes(x = x)))

The disadvantage of this approach is that it does not make a nice legend, as it's hardcoding colors.

---

Precalculate

One way around this is to simply do the calculations before plotting, which makes the plotting itself very simple:

library(tidyverse)

aa <- list(list(0.2, 0.1), list(0.7, 0.05), list(0.45, 0.2))

aa %>% set_names(paste('Group', 1:3)) %>% 
    map_df(~dnorm(seq(0, 1, length = 100), .x[[1]], .x[[2]])) %>% 
    mutate(x = seq(0, 1, length = 100)) %>% 
    gather(Group, y, -x) %>% 
    ggplot(aes(x, y, color = Group)) + 
    geom_line()

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