好吧，经过一番调查，我没有找到一种干净的方法，看来position_dodge仅更改了x aes ，而没有更改xend aes 。 position_nudge在这里也不起作用，因为它会同时移动所有箭头。 所以我想到了一种肮脏的方式。我们所需要做的就是为geom_segment创建一个具有所需xend位置的新变量。我尝试并采用了半自动化的方式来进行着色变量的任何数量的级别，并且还创建了可重复使用的数据集，因为我相信有更多知识的人可以对此进行很多改进比我（如何，如何。 该代码具有内联注释，说明了步骤：

library(tidyverse)

# dummy data (tried to replicate your plot data more or less accurately)
df <- tibble(
  numAlleles = rep(c("1","2+"),each = 4),Outcome = rep(LETTERS[1:4],2),OR = c(1.4,1.5,1.45,2.3,3.8,4.2,4.0,1.55),OR.95U = c(1.9,2.1,1.9,12,12),OR.95L = c(0.9,0.9,0.8,NA,NA)
) %>%
  mutate(
    OR.95U_u = if_else(OR.95U > 10,10,NA_real_)
  )

# as it seems that position_dodge in a geom_segment only "dodge" the x aes and
# not the xend aes,we need to supply a custom xend. Also,we need to try
# to automatize the position,for more classes or different dodge widths.
# To do that,lets start with some parameters:
# position_dodge width
position_dodge_width <- 0.5
# number of bars per x axis class
bars_per_class <- length(unique(df$Outcome))
# total space available per class. In discrete vars,this is 1 au (arbitrary unit)
# for each class,but position_dodge only use the fraction of that unit
# indicated in the width parameter,so we need to calculate the real
# space available:
total_space_available <- 1 * position_dodge_width
# now we calculate the real bar width used by ggplot in these au,dividing the
# space available by the number of bars to plot for each class
bar_width_real <- (total_space_available / bars_per_class)
# position_dodge with discrete variables place bars to the left and to the right of the
# class au value,so we need to know when to place the xend to the left or
# to the right. Also,the number of bars has to be taken in to account,as
# in odd number of bars,one is located on the exact au value
if (bars_per_class%%2 == 0) {
  # we need an offset,as bars are wider than arrows,and we want them in the
  # middle of the bar
  offset_segment <- bar_width_real / 2
  # offset modifier to know when to substract or add the modifier
  offset_modifier <- c(rep(-1,bars_per_class%/%2),rep(1,bars_per_class%/%2))
  # we also need to know how meny bars to the left and how many to the right,# but,the first bar of each side is already taken in account with the offset,# so the bar modifier has to have one bar less for each side
  bar_width_modifier <- c(seq((bars_per_class%/%2-1),0),seq(0,(bars_per_class%/%2-1)))
} else {
  # when odd number of columns,the offset is the same as the bar width
  offset_segment <- bar_width_real
  # and the modifiers have to have a middle zero value for the middle bar
  offset_modifier <- c(rep(-1,bars_per_class%/%2))
  bar_width_modifier <- c(seq((bars_per_class%/%2-1),(bars_per_class%/%2-1)))
}

# finally we create the vector of xend values needed:
df %>%
  mutate(
    numAlleles_u = as.numeric(as.factor(numAlleles)) + offset_modifier*(offset_segment + (bar_width_modifier*bar_width_real))
  )

ggplot(df,aes(x = numAlleles,y = OR,fill = Outcome)) + 
  geom_bar(
    position = position_dodge(position_dodge_width),stat = "identity",width = 0.4,color = "black"
  ) +
  geom_hline(yintercept = 1,linetype = "dashed",color = "black") +
  ylim(0,10) +
  geom_errorbar(
    aes(ymin=OR.95L,ymax=OR.95U),width=.2,position=position_dodge(position_dodge_width)
  ) +
  theme(
    legend.key = element_blank(),text = element_text(size = 11.5),legend.title = element_blank()
  ) + 
  labs(x = "Number of rare alleles") +
  scale_fill_manual(values=c("chocolate1","coral1","red2","darkred")) + 
  geom_segment(
    aes(x = numAlleles,xend = numAlleles_u,yend = OR.95U_u),position = position_dodge(position_dodge_width),arrow = arrow(length = unit(0.3,"cm"))
  )

情节：

我们可以检查三个级别的离散变量是否也有效：

df_three_bars <- df %>% filter(Outcome != 'D')
bars_per_class <- length(unique(df_three_bars$Outcome))
total_space_available <- 1 * position_dodge_width
bar_width_real <- (total_space_available / bars_per_class)
if (bars_per_class%%2 == 0) {
  offset_segment <- bar_width_real / 2
  offset_modifier <- c(rep(-1,(bars_per_class%/%2-1)))
} else {
  offset_segment <- bar_width_real
  offset_modifier <- c(rep(-1,(bars_per_class%/%2-1)))
}
df_three_bars <- df_three_bars %>%
  mutate(
    numAlleles_u = as.numeric(as.factor(numAlleles)) + offset_modifier*(offset_segment + (bar_width_modifier*bar_width_real))
  )

ggplot(df_three_bars,"cm"))
  )