这里有一些模拟数据显示了这个问题（问题是在summarise部分，它创建了count，所以在计数之前从一些新数据开始）。
在复制第二个错误计算的图表的顶部图表中，.groups = "drop"参数然后具有未分组的输出。第二个带有.groups = "drop_last"的图表删除member_casual分组，然后按start_mth计算百分比，我认为这是您打算做的？这些默认值可能在运行两个图表之间发生了变化，因此，如果您明确声明.groups = "drop_last"，则这将保持一致。

library(tidyverse)

set.seed(123)

df <-
  tibble(
    start_mth = sample(
      month.abb,
      size = 1000,
      replace = TRUE,
      prob = c(1:6, 6:1)
    ),
    member_casual = sample(
      c("member", "casual"),
      1000,
      replace = TRUE
    )
  ) %>% 
  mutate(start_mth = factor(start_mth, levels = month.abb))

df %>% 
group_by(start_mth, member_casual) %>% 
  summarise(count = n(), .groups = "drop") %>% 
  mutate(percent = count/sum(count)*100) %>% 
  ggplot(aes(start_mth, count, fill = member_casual)) + 
  geom_col(position = "stack") + 
  geom_text(aes(label = paste0(round(percent), "%")), 
            position = position_stack(vjust = 0.5), size = 3, color = "white") +
  scale_y_continuous(expand = expansion(add = c(0, 10))) + 
  labs(x = "Months, 2022", y = "Total Rentals", 
       title = "Ride Distribution by Month and Membership Type")+
  facet_wrap(~member_casual)

df %>% 
  group_by(start_mth, member_casual) %>% 
  summarise(count = n(), .groups = "drop_last") %>% 
  mutate(percent = count/sum(count)*100) %>% 
  ggplot(aes(start_mth, count, fill = member_casual)) + 
  geom_col(position = "stack") + 
  geom_text(aes(label = paste0(round(percent), "%")), 
            position = position_stack(vjust = 0.5), size = 3, color = "white") +
  scale_y_continuous(expand = expansion(add = c(0, 10))) + 
  labs(x = "Months, 2022", y = "Total Rentals", 
       title = "Ride Distribution by Month and Membership Type") +
  facet_wrap(~member_casual)

额外提示-让ggplot帮你计算

借助一些额外的ggplot2函数，您可以删除group_by、summarise和mutate行，并将未更改的 Dataframe 直接传递给ggplot。

• 第一个stat_count将按类别和返回条计数观测值
• 第二个stat_count通过计算每个x值的比例来构造标签：sapply(x, FUN = function(month) ...)在x轴上计算每个月的值

一般来说，最好的做法是将未经修改的、整洁的data.frame放入ggplot中，让它为您工作，但是如果您已经理解了可以工作的代码，那么这一点就不那么重要了！

df %>%
  ggplot(aes(start_mth, fill = member_casual)) +
  stat_count() +
  stat_count(aes(label = paste0(round(
                   after_stat(count) / 
                     sapply(x, FUN = function(month)
                       sum(count[x == month])
                   ) * 100
                 ), "%")),
             geom = "text",
             position = position_stack(vjust = 0.5),
             size = 3, color = "white") +
  scale_y_continuous(expand = expansion(add = c(0, 10))) + 
  labs(x = "Months, 2022", y = "Total Rentals", 
       title = "Ride Distribution by Month and Membership Type") +
  facet_wrap( ~ member_casual)

示例代码中的编辑-分组

要注意的关键是，当按月计算%s时，分组应该仅按start_mth进行。在生成计数和百分比之间的group_by中说明这一点将在所有情况下解决此问题：

library(tidyverse)
set.seed(123) 

start_mth <- rep(c("Jan", "Feb", "Mar"), each = 2)
member_casual <- rep(c("member", "casual"), times = 3)
count <- round(runif(6, 0, 900000))
Divvy_example <- data.frame(start_mth, member_casual, count)

# Calculate percentages
Divvy_example <- Divvy_example %>% 
  group_by(start_mth) %>%  # Group **only** by start_mth
  mutate(percent = count/sum(count) * 100)

# Plot 
ggplot(Divvy_example, aes(start_mth, count, fill = member_casual)) + 
  geom_col(position = "stack") + 
  geom_text(aes(label = paste0(round(percent), "%")), 
            position = position_stack(vjust = 0.5), size = 3, color = "white") + 
  scale_y_continuous(limits = c(0, 900000), expand = c(0, 0)) + 
  labs(x = "Months, 2022", y = "Total Rentals", 
       title = "Ride Distribution by Month and Membership Type") +
  facet_wrap(~member_casual)