带有 facet_grid 的百分比直方图：x 变量是一个因素

Question

我想使用facet_grid 将百分比直方图（积分为 100%）分成两个方面。但是，当拆分为构面时，每个构面本身不会集成到 100%。这种问题has been resolved here 过去，但我无法将该解决方案转换为我目前的情况，其中 x 是一个因素，因此使用stat(density) 的直方图不起作用。

我的数据

具有两列的数据框。 equipment 表示家庭是否有足够的设备进行家庭教育，children_n 表示孩子的数量。

library(tidyverse)
library(magrittr)

df <- 
structure(list(equipment = c(1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 
0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 
1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 
0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 
0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 
0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 
1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 
0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 
1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 
1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 
0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 
1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 
0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 
1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 
1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 
1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 
1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 
1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 
0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 
0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 
0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 
0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 
1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 
1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1), children_n = c(4, 
4, 2, 2, 2, 1, 1, 3, 2, 3, 3, 7, 3, 2, 1, 2, 1, 1, 3, 3, 3, 2, 
3, 3, 3, 2, 4, 3, 1, 2, 3, 4, 4, 1, 2, 5, 2, 8, 1, 2, 1, 2, 2, 
3, 4, 3, 3, 3, 3, 2, 3, 2, 2, 4, 3, 3, 3, 4, 3, 1, 1, 2, 1, 1, 
2, 1, 3, 3, 2, 3, 3, 3, 4, 2, 2, 2, 3, 5, 2, 2, 2, 2, 1, 2, 4, 
3, 4, 3, 3, 1, 2, 3, 3, 3, 2, 4, 4, 3, 1, 3, 2, 2, 2, 3, 1, 1, 
1, 3, 1, 2, 2, 2, 3, 6, 3, 2, 2, 6, 3, 4, 3, 2, 3, 3, 2, 2, 2, 
3, 2, 3, 3, 6, 3, 1, 4, 3, 4, 9, 1, 1, 3, 4, 2, 2, 1, 2, 3, 1, 
3, 3, 6, 4, 1, 3, 2, 2, 3, 2, 3, 2, 4, 3, 1, 3, 3, 2, 3, 2, 2, 
4, 2, 2, 3, 3, 3, 1, 3, 3, 2, 4, 2, 7, 3, 3, 3, 2, 2, 2, 4, 3, 
1, 1, 3, 4, 1, 4, 3, 4, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 
3, 1, 1, 2, 2, 4, 2, 3, 3, 2, 2, 1, 2, 5, 2, 2, 2, 5, 3, 2, 2, 
4, 2, 1, 3, 4, 4, 3, 3, 4, 3, 3, 1, 3, 2, 1, 8, 2, 3, 2, 3, 3, 
2, 3, 3, 1, 3, 3, 4, 2, 3, 3, 3, 2, 6, 1, 2, 2, 2, 2, 2, 2, 4, 
3, 5, 4, 1, 2, 2, 2, 4, 2, 3, 3, 1, 3, 2, 1, 2, 2, 3, 3, 3, 3, 
1, 3, 4, 2, 1, 3, 4, 2, 1, 3, 4, 3, 4, 2, 3, 3, 2, 7, 1, 2, 1, 
3, 2, 2, 2, 2, 3, 3, 3, 2, 3, 1, 2, 2, 3, 2, 4, 3, 2, 3, 3, 5, 
3, 5, 3, 5, 1, 2, 1, 4, 1, 4, 2, 2, 3, 2, 2, 2, 3, 2, 3, 3, 3, 
3, 4, 3, 8, 3, 1, 2, 3, 3, 2, 1, 3, 2, 2, 3, 3, 4, 4, 2, 2, 3, 
1, 2, 3, 2, 3, 3, 2, 1, 3, 3, 2, 3, 3, 3, 4, 1, 2, 3, 3, 3, 4, 
2, 1, 3, 4, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 1, 3, 3, 1, 1, 3, 
2, 1, 3, 2, 4, 1, 3, 2, 3, 2, 2, 2, 4, 1, 2, 3, 2, 3, 2, 2, 1, 
3, 1, 3, 1, 3, 3, 2, 1, 2, 3, 2, 3, 1, 2, 1, 2, 2, 3, 3, 4, 1, 
2, 4, 2, 4, 2, 2, 2, 1, 3, 2, 1, 1, 4, 3, 4, 3, 2, 2, 2, 3, 7, 
3, 1, 3, 3, 3, 2, 1, 3, 2, 3, 3, 2, 4, 1, 1, 1, 4, 3, 3, 4, 3, 
8, 2, 4, 5, 3, 2, 3, 1, 2, 1, 2, 2, 3, 1, 4, 3, 2, 2, 3, 3, 3, 
3, 1, 2, 1, 2, 3, 3, 2, 2, 2, 2, 3, 3, 4, 5, 3, 2, 2, 2, 3, 1, 
3, 3, 4, 2, 1, 3, 3, 3, 4, 2, 1, 2, 1, 2, 2, 3, 3, 4, 1, 1, 6, 
3, 2, 2, 2, 6, 3, 3, 2, 2, 1, 4, 2, 3, 3, 3, 2, 2, 3, 3, 2, 4, 
6, 1, 1, 1, 1, 3, 9, 4, 2, 3, 2, 2, 2, 4, 3, 3, 4, 1, 2, 6, 3, 
3, 3, 2, 2, 3, 4, 2, 3, 2, 2, 3, 2, 3, 4, 7, 2, 3, 3, 2, 3, 2, 
3, 4, 3, 3, 3, 2, 2, 2, 1, 3, 4, 2, 1, 3, 4, 1, 3, 4, 4, 3, 3, 
3, 3, 3, 2, 3, 3, 3, 5, 3, 3, 5, 2, 2, 1, 1, 2, 2, 2, 3, 1, 3, 
2, 2, 2, 4, 2, 2, 2, 4, 1, 3, 4, 3, 3, 4, 3, 2, 1, 3, 4, 8, 1, 
2, 3, 3, 3, 3, 2, 3, 3, 1, 3, 4, 2, 3, 2, 6, 3, 1, 2, 2, 2, 2, 
2, 4, 3, 5, 1, 2, 2, 2, 4, 2, 3, 3, 1, 1, 2, 2, 3, 3, 2, 3, 3, 
3, 3, 1, 4, 4, 2, 3, 3, 1, 4, 3, 4, 2, 3, 3, 2, 7, 1, 4, 1, 2, 
2, 3, 2, 5, 2, 3, 2, 3, 1, 3, 2, 2, 3, 2, 4, 2, 3, 3, 3, 3, 1, 
5, 5, 1, 1, 2, 3, 1, 4, 2, 2, 3, 2, 2, 2, 3, 3, 3, 3, 2, 3, 4, 
8, 3, 2, 3, 1, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 4, 2, 3, 2, 1, 3, 
2, 3, 3, 2, 3, 3, 2, 3, 2, 3, 3, 1, 1, 2, 4, 3, 4, 3, 1, 3, 4, 
2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 1, 3, 3, 2, 1, 1, 4, 1, 3, 2, 
1, 2, 3, 3, 2, 2, 2, 4, 2, 1, 3, 2, 3, 2, 1, 3, 1, 3, 1, 3, 3, 
2, 1, 2, 3, 2, 3, 1, 2, 2, 2, 3, 3, 2, 3, 1, 3, 3, 3, 3, 2, 4, 
2, 4, 4, 1, 2, 1, 2, 1, 3, 3, 3, 2, 3, 3, 4, 2, 2, 3, 2, 1, 2, 
2, 1, 1, 3, 1, 2, 3, 3, 3, 2, 1, 1, 1, 2, 1, 2, 5, 1, 2, 1, 4, 
2, 2, 2, 1, 4, 2, 3, 3, 3, 2, 4, 5, 4, 2, 4, 2, 3, 1, 4, 3, 3, 
2, 3, 3, 2, 3, 2, 1, 3, 2, 4, 2, 3, 4, 1, 2, 3, 1, 3, 3, 4, 2, 
2, 2, 3, 3, 2, 1, 2, 2, 1, 3, 1, 3, 1, 1, 1, 3, 2, 2, 4, 3, 4, 
3, 3, 4, 1, 1, 3, 3, 2, 3, 2, 3, 2, 1, 3, 3, 1, 5, 1, 1, 2, 4, 
2, 3, 5, 4, 1, 3, 2, 1, 2, 2, 4, 3, 4, 2, 2, 1, 3, 2, 4, 2, 3, 
3, 2, 3, 2, 1, 2, 3, 4)), row.names = c(NA, -1059L), class = c("tbl_df", 
"tbl", "data.frame"))


df

## # A tibble: 1,059 x 2
##    equipment children_n
##        <dbl>      <dbl>
##  1         1          4
##  2         0          4
##  3         1          2
##  4         1          2
##  5         0          2
##  6         1          1
##  7         1          1
##  8         1          3
##  9         1          2
## 10         1          3
## # ... with 1,049 more rows

如果孩子的数量超过 6 个，我想将这些案例合并为“6+”一类。

df %<>%
  mutate_at(vars(children_n), as.character) %>%
  mutate_at(vars(children_n), recode, "9" = "6_plus", "8" = "6_plus", "7" = "6_plus", "6" = "6_plus") %>%
  mutate_at(vars(children_n), fct_relevel, "1", "2", "3", "4", "5", "6_plus")

glimpse(df)

## Rows: 1,059
## Columns: 2
## $ equipment  <dbl> 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, ...
## $ children_n <fct> 4, 4, 2, 2, 2, 1, 1, 3, 2, 3, 3, 6_plus, 3, 2, 1, 2, 1, 1, 3, 3, 3, 2, 3, 3, 3, 2, 4, 3, 1, 2, 3, 4, 4, 1, 2, 5, 2, 6_plus, 1, 2, 1, 2,...

现在我想在两个单独的面板中绘制孩子数量的比例：一个面板用于有足够设备的家庭，另一个面板用于没有足够设备的家庭：

df %>%
  ggplot(data = ., aes(x = children_n, y = equipment)) + 
  geom_histogram(aes(y = (..count..)/sum(..count..)), stat = "count" , fill = "darkblue") +
  geom_text(aes(label = scales::percent(((..count..)/sum(..count..)), accuracy = 1),
                y = ((..count..)/sum(..count..)) ), stat= "count", vjust = -.5, color = "darkblue") +
  scale_y_continuous(labels = scales::percent) +
  facet_grid(~ equipment, labeller = as_labeller(c("1" = "have enough equipment", 
                                                   "0" = "don't have enough equipment")))

这给出了两个*不要*独立集成到 100% 的面板：

试图解决问题

我发现this question 描述了相同的意图和问题。选择的解决方案建议将geom_histogram 定义为密度，以便它集成到 100%。但这在我的情况下不起作用，因为stat(density) 要求 x 变量是连续的，这与我的情况不同，其中 x 是一个因素。

df %>%
  ggplot(data = ., aes(x = children_n, y = equipment)) + 
  geom_histogram(aes(y = stat(density) * 6), binwidth = 6, fill = "darkblue") +
  facet_grid(~ equipment, labeller = as_labeller(c("1" = "have enough equipment", 
                                                   "0" = "don't have enough equipment")))

错误：StatBin 需要一个连续的 x 变量：x 变量是 离散的。也许你想要 stat="count"？

其他方法建议使用..PANEL..，而其他方法则强烈反对。 如何以正确的方式让两个方面显示独立集成到 100% 的百分比？

Answer 1

这可以这样实现：

1. 在group aes 上映射构面变量
2. 使用例如tapply 获取每个组或方面的总数

顺便说一句：我已将规范化代码放在辅助函数中，以减少代码重复和可读性

library(tidyverse)
library(magrittr)

df %<>%
  mutate_at(vars(children_n), as.character) %>%
  mutate_at(vars(children_n), recode, "9" = "6_plus", "8" = "6_plus", "7" = "6_plus", "6" = "6_plus") %>%
  mutate_at(vars(children_n), fct_relevel, "1", "2", "3", "4", "5", "6_plus")

help <- function(count, group) {
  count / tapply(count, group, sum)[group]
}

df %>%
  ggplot(data = ., aes(x = children_n, y = equipment, group = equipment)) + 
  geom_histogram(aes(y = help(..count.., ..group..)), stat = "count" , fill = "darkblue") +
  geom_text(aes(label = scales::percent(help(..count.., ..group..), accuracy = 1),
                y = help(..count.., ..group..) ), stat= "count", vjust = -.5, color = "darkblue") +
  scale_y_continuous(labels = scales::percent) +
  facet_grid(~ equipment, labeller = as_labeller(c("1" = "have enough equipment", 
                                                   "0" = "don't have enough equipment")))
#> Warning: Ignoring unknown parameters: binwidth, bins, pad