geom_vline，图例和性能

Question

我想在我的情节上绘制几条垂直线，并为每个相应的 vline 绘制一个图例。

阅读这篇文章后，这是我实现的：

set.seed(99)
df.size <- 1e6
my.df <- data.frame(dist = rnorm(df.size, mean = 0, sd = 2))
library(ggplot2)
ggplot(my.df, aes(x=dist)) + geom_histogram(binwidth = 0.5)

vline1.threshold <- mean(my.df$dist)
vline2.threshold <- mean(my.df$dist) + 3*sd(my.df$dist)

现在的情节：

g <- ggplot(my.df, aes(x = dist)) +
  geom_histogram(binwidth = 0.5) +
  geom_vline(aes(color = "vline1", xintercept = vline1.threshold)) +
  geom_vline(aes(color = "vline2", xintercept = vline2.threshold)) +
  scale_color_manual("Threshold", values = c(vline1 = "red", vline2 = "blue"), labels = c("Mean", "Mean + 3*SD"))
system.time(print(g))

这很有效：

但它非常慢：

utilisateur     système      écoulé 
     51.667       1.883      53.652 

（对不起，我的系统是法语）

但是，当我这样做时（在 aes 之外使用 xintercept）：

g <- ggplot(my.df, aes(x = dist)) +
  geom_histogram(binwidth = 0.5) +
  geom_vline(aes(color = "vline1"), xintercept = vline1.threshold, color = "red") +
  geom_vline(aes(color = "vline2"), xintercept = vline2.threshold, color = "blue") +
  scale_color_manual("Threshold", values = c(vline1 = "red", vline2 = "blue"), labels = c("Mean", "Mean + 3*SD"))
system.time(print(g))

图例不显示：

但它要快得多：

utilisateur     système      écoulé 
      1.193       0.270       1.496 

我怎样才能两全其美，即一个传奇，快速显示？

Answer 1

您可以使用第一种方法，但将空 data.frame 作为data参数传递给geom_vline. 速度问题是由geom_vline为每一行绘制线条引起的my.df，data = data.frame()它只绘制一次。

g2 <- ggplot(my.df, aes(x = dist)) +
  geom_histogram(binwidth = 0.5) +
  # pass empty data.frame as data
  geom_vline(aes(color = "vline1", xintercept = vline1.threshold), data.frame()) +
  # pass empty data.frame as data
  geom_vline(aes(color = "vline2", xintercept = vline2.threshold), data.frame()) +
  scale_color_manual("Threshold", values = c(vline1 = "red", vline2 = "blue"), labels = c("Mean", "Mean + 3*SD"))

# OPs solution
# system.time(print(g))
#   user  system elapsed 
# 36.636   1.714  38.397 

# data.frame() solution
# system.time(print(g2))
#   user  system elapsed 
#  2.203   0.265   2.504