如何创建ggplot自定义stat计算分组数据范围？解决y美学警告

我想在geom_jitter等可视化图层之上添加分组数据的总范围可视化，希望不用预计算汇总数据到单独的data对象，这样可视化变量名称变化时还能复用，而且接口要像geom_boxplot一样简洁。参考ggplot2相关资料后，我尝试实现自定义ggplot stat来完成数据转换，只需要计算geom_linerange或geom_errorbar所需的ymin和ymax。已经写好了StatMinMax代码，能生成预期的图表，但运行时会出现「y美学在统计转换中被丢弃」的警告，请问问题出在哪？

附示例代码及结果：

library(ggplot2)
library(dplyr)

StatMinMax <- ggproto("StatMinMax", Stat,
  compute_group = function(data, scales) {
    data %>%
      summarise(
        ymin = min(y),
        ymax = max(y)
      )
  }
)

stat_minmax <- function(mapping = NULL, data = NULL, geom = "errorbar",
                       position = "identity", na.rm = FALSE, show.legend = NA,
                       inherit.aes = TRUE, ...) {
  layer(
    stat = StatMinMax, mapping = mapping, data = data, geom = geom,
    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
    params=list(na.rm = na.rm, ...)
  )
}

diamonds %>%
  ggplot(aes(x=clarity, y=price)) +
  stat_minmax() +
  geom_point()
#> Warning: The following aesthetics were dropped during statistical transformation: y.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?

mtcars %>%
  mutate(cyl=factor(cyl)) %>%
  ggplot(aes(x=cyl, y=mpg)) +
  stat_minmax() +
  geom_point()
#> Warning: The following aesthetics were dropped during statistical transformation: y.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?

警告出现的原因是：自定义的StatMinMax在compute_group返回的结果中，没有保留分组对应的x轴变量信息。ggplot在统计转换后，需要明确每个分组对应的x轴位置，否则会认为y美学没有关联的分组维度，从而抛出警告。

修改后的代码如下：

library(ggplot2)
library(dplyr)

StatMinMax <- ggproto("StatMinMax", Stat,
  # 声明stat必需的美学映射，明确x和y为必填项
  required_aes = c("x", "y"),
  compute_group = function(data, scales) {
    data %>%
      summarise(
        x = first(x),  # 保留当前分组的x值，每个分组内x值一致
        ymin = min(y, na.rm = TRUE),  # 处理缺失值
        ymax = max(y, na.rm = TRUE),
        .groups = "drop"  # 显式关闭分组状态，避免dplyr警告
      )
  }
)

stat_minmax <- function(mapping = NULL, data = NULL, geom = "errorbar",
                       position = "identity", na.rm = FALSE, show.legend = NA,
                       inherit.aes = TRUE, ...) {
  layer(
    stat = StatMinMax, mapping = mapping, data = data, geom = geom,
    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
    params=list(na.rm = na.rm, ...)
  )
}

# 验证示例1
diamonds %>%
  ggplot(aes(x=clarity, y=price)) +
  stat_minmax() +
  geom_point()

# 验证示例2
mtcars %>%
  mutate(cyl=factor(cyl)) %>%
  ggplot(aes(x=cyl, y=mpg)) +
  stat_minmax() +
  geom_point()

修改说明：

• 添加required_aes = c("x", "y")：明确告知ggplot该stat需要的美学变量，避免自动推断时的混淆。
• 在summarise中保留x = first(x)：每个分组内的x值一致，取第一个值即可，让返回结果包含x轴位置信息，ggplot就能正确关联分组与y范围数据，不再抛出警告。
• 给min和max添加na.rm = TRUE：配合参数中的na.rm处理缺失值，提升结果稳健性。
• 添加.groups = "drop"：显式指定分组处理后的结果格式，避免dplyr的分组状态警告。

修改后代码可正常生成预期图表，且不会出现“y美学被丢弃”的警告，同时保留了无需预计算汇总数据的灵活性，变量名称变化时可直接复用。

内容的提问来源于stack exchange，提问作者mac