这里有一种添加upper和lower的平滑版本的方法。我们将upper和lower的LOESS预测添加到 Dataframe 中，然后使用geom_ribbon绘制这些预测。如果这一切都可以在对ggplot的调用中完成，那将更加优雅。这可能通过向stat_summary提供专用函数来实现，希望其他人也会用这种方法来回答。

# Expand the scale of the upper and lower values so that the difference
# is visible in the plot
data$upper = data$value + 10
data$lower = data$value - 10

# Order data by category and num
data = data[order(data$category, data$num),]

# Create LOESS predictions for the values of upper and lower 
# and add them to the data frame. I'm sure there's a better way to do this,
# but my attempts with dplyr and tapply both failed, so I've resorted to the clunky 
# method below.
data$upperLoess = unlist(lapply(LETTERS[1:4], 
                  function(x) predict(loess(data$upper[data$category==x] ~ 
                                                  data$num[data$category==x]))))
data$lowerLoess = unlist(lapply(LETTERS[1:4], 
                  function(x) predict(loess(data$lower[data$category==x] ~ 
                                                  data$num[data$category==x]))))

# Use geom_ribbon to add a prediction band bounded by the LOESS predictions for 
# upper and lower
ggplot(data, aes(num, value, colour=category, fill=category)) +
  geom_smooth(method="loess", se=FALSE) +
  geom_ribbon(aes(x=num, y=value, ymax=upperLoess, ymin=lowerLoess), 
              alpha=0.2)

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结果是这样的：

的数据