r - 根据组为直方图着色时防止错误的密度图

Question

根据一些虚拟数据，我创建了一个带有密度图的直方图

set.seed(1234)
wdata = data.frame(
  sex = factor(rep(c("F", "M"), each=200)),
  weight = c(rnorm(200, 55), rnorm(200, 58))
)
a <- ggplot(wdata, aes(x = weight))

a + geom_histogram(aes(y = ..density..,
                       # color = sex
                       ), 
                   colour="black",
                   fill="white",
                   position = "identity") +
  geom_density(alpha = 0.2,
               # aes(color = sex)
               ) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))

weight 的直方图应对应于sex着色，所以我使用 aes(y = ..density.., color = sex)对于 geom_histogram() :

a + geom_histogram(aes(y = ..density..,
                       color = sex
                       ), 
                   colour="black",
                   fill="white",
                   position = "identity") +
  geom_density(alpha = 0.2,
               # aes(color = sex)
               ) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))

如我所愿，密度图保持不变(两组的总体)，但直方图按比例放大(现在似乎被单独处理):

如何防止这种情况发生？我需要单独着色的直方图条，但需要所有着色组的联合密度图。

附言使用 aes(color = sex)对于 geom_density()将所有内容恢复到原始比例 - 但我不想要单独的密度图(如下所示):

a + geom_histogram(aes(y = ..density..,
                       color = sex
                       ), 
                   colour="black",
                   fill="white",
                   position = "identity") +
  geom_density(alpha = 0.2,
               aes(color = sex)
               ) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))

编辑:

正如建议的那样，除以 geom_histogram() 中的组数与y = ..density../2的美学可以近似解。然而，这仅适用于对称分布，如下面的第一个输出所示:

a + geom_histogram(aes(y = ..density../2,
                       color = sex
                       ), 
                   colour="black",
                   fill="white",
                   position = "identity") +
  geom_density(alpha = 0.2,
               ) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))

产生

但是，不太对称的分布可能会导致使用此方法出现问题。请参阅下面的内容，其中 5 组，y = ..density../5被使用了。首先是原始的，然后是操纵的(使用 position = "stack" ):

由于左边的分布很重，除以 5 左边低估，右边高估。

编辑 2:解决方案

按照 Andrew 的建议，下面的(完整的)代码解决了这个问题:

library(ggplot2)
set.seed(1234)
wdata = data.frame(
  sex = factor(rep(c("F", "M"), each = 200)),
  weight = c(rnorm(200, 55), rnorm(200, 58))
)

binwidth <- 0.25
a <- ggplot(wdata,
            aes(x = weight,
                # Pass binwidth to aes() so it will be found in
                # geom_histogram()'s aes() later
                binwidth = binwidth))

# Basic plot w/o colouring according to 'sex'
a + geom_histogram(aes(y = ..density..),
                   binwidth = binwidth,
                   colour = "black",
                   fill = "white",
                   position = "stack") +
  geom_density(alpha = 0.2) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF")) +
  # Use fixed scale for sake of comparability
  scale_x_continuous(limits = c(52, 61)) +
  scale_y_continuous(limits = c(0, 0.25))


# Plot w/ colouring according to 'sex'
a + geom_histogram(aes(x = weight,
                       # binwidth will only be found if passed to
                       # ggplot()'s aes() (as above)
                       y = ..count.. / (sum(..count..) * binwidth),
                       color = sex),
                   binwidth = binwidth,
                   fill="white",
                   position = "stack") +
  geom_density(alpha = 0.2) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF")) +
  # Use fixed scale for sake of comparability
  scale_x_continuous(limits = c(52, 61)) +
  scale_y_continuous(limits = c(0, 0.25)) +
  guides(color = FALSE)

注意: binwidth = binwidth需要传递给 ggplot()的 aes() ，否则预先指定 binwidth geom_histogram() 找不到的 aes() .此外，position = "stack"被指定，因此直方图的两个版本是可比较的。虚拟数据图和下面更复杂的分布:

已解决 - 感谢您的帮助!

Answer 1

我不认为你可以使用 y=..density.. 来做到这一点，但你可以像这样重新创建同样的东西......

binwidth <- 0.25 #easiest to set this manually so that you know what it is

a + geom_histogram(aes(y = ..count.. / (sum(..count..) * binwidth),
                       color = sex), 
                   binwidth = binwidth,
                   fill="white",
                   position = "identity") +
    geom_density(alpha = 0.2) +
    scale_color_manual(values = c("#868686FF", "#EFC000FF"))