可能还有更惯用的方法来做这件事，但这是一种完成它的方法。
简而言之，您可以快速计算连接直线的线性公式，即y = mx+c

df_withFormula <- df |>
  group_by(Prob) |>
  #This mutate command will create the needed slope and intercept for the geom_abline command in the plotting stage.
  mutate(increaseBill = Bill - lag(Bill),
         increaseWing = Wing - lag(Wing),
         slope = increaseWing/increaseBill,
         intercept = Wing - slope*Bill)
# The increaseBill, increaseWing and slope could all be combined into one calculation but I thought it was easier to understand this way.

ggplot(df_withFormula, aes(Bill, Wing, color = Prob)) +
  #Add in this just so it has something to plot ontop of. You could remove this and instead manually define all the limits (expand_limits would work).
  geom_point() +
  #This plots the three lines. The rows with NA are automatically ignored. More explicit handling of the NA could be done in the data prep stage
  geom_abline(aes(slope = slope, intercept = intercept, color = Prob)) +
  #This is the crucial part it lets you define what the range is for the plot window. As ablines are infite you can define whatever limits you want.
  expand_limits(y = c(105,125))

希望这能帮助你得到你想要的图表。
这在很大程度上取决于数据的结构，但可以对其进行更改以适应不同的形状。

类似于@James的方法，我根据给定数据计算斜率和截距，并使用geom_abline绘制直线，但使用

• summarise而不是mutate，以删除NA值
• 和geom_blank代替geom_point，以便只显示线而不显示点（注：拥有另一个geom对于设置scale或数据范围以及显示行至关重要）。

library(dplyr)
library(ggplot2)

df_line <- df |> 
  group_by(Prob) |> 
  summarise(slope = diff(Wing) / diff(Bill),
            intercept = first(Wing) - slope * first(Bill))

ggplot(df, aes(x = Bill, y = Wing)) +
  geom_blank() +
  geom_abline(data = df_line, aes(slope = slope, intercept = intercept, color = Prob)) +
  scale_y_continuous(limits = c(105, 125))