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下面是我正在使用的数据的代表。这些geom_segment调用使渲染非常缓慢。是否有其他方法可以更快地达到相同的结果？

library(ggplot2)
library(ggridges)

n <- 5000; l <- c(2, 5, 7, 9); sd_27 <- c(5.9, 11, 14, 17)
df <- data.frame(name = c(rep("A", n), rep("B", n), 
                          rep("C", n), rep("D", n)),
                 value = c(rpois(n, l[1]), rpois(n, l[2]),
                           rpois(n, l[3]), rpois(n, l[4])))

ggplot(df, aes(x = value, y = name, fill = name)) + geom_density_ridges(alpha = 0.8) +
  geom_segment(aes(x = l[[1]], y = "A", xend = l[[1]], yend = 2, color = "mean")) +
  geom_segment(aes(x = l[[2]], y = "B", xend = l[[2]], yend = 3, color = "mean")) +
  geom_segment(aes(x = l[[3]], y = "C", xend = l[[3]], yend = 4, color = "mean")) +
  geom_segment(aes(x = l[[4]], y = "D", xend = l[[4]], yend = 5, color = "mean")) +
  geom_segment(aes(x = sd_27[[1]], y = "A", xend = sd_27[[1]], yend = 2, color = "sd_27")) +
  geom_segment(aes(x = sd_27[[2]], y = "B", xend = sd_27[[2]], yend = 3, color = "sd_27")) +
  geom_segment(aes(x = sd_27[[3]], y = "C", xend = sd_27[[3]], yend = 4, color = "sd_27")) +
  geom_segment(aes(x = sd_27[[4]], y = "D", xend = sd_27[[4]], yend = 5, color = "sd_27"))

在此处输入图像描述

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1 回答 1

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geom_segment您可以将段的所有数据放在一个数据帧中，然后通过一个数据帧添加段，而不是通过单独的层添加每个段，geom_segment这microbenchmark可以将渲染时间减少到大约五分之一：

geom_segment

library(ggplot2)
library(ggridges)

set.seed(42)

n <- 5000; l <- c(2, 5, 7, 9); sd_27 <- c(5.9, 11, 14, 17)
df <- data.frame(name = c(rep("A", n), rep("B", n), 
                          rep("C", n), rep("D", n)),
                 value = c(rpois(n, l[1]), rpois(n, l[2]),
                           rpois(n, l[3]), rpois(n, l[4])))

dl <- data.frame(x = l, y = LETTERS[1:4], yend = 2:5, color = "mean")
dsd <- data.frame(x = sd_27, y = LETTERS[1:4], yend = 2:5, color = "sd_27")

d <- do.call(rbind, list(dl, dsd))

p1 <- function() {
  ggplot(df, aes(x = value, y = name, fill = name)) + 
    geom_density_ridges(alpha = 0.8) +
    geom_segment(data = d, aes(x = x, y = y, xend = x, yend = yend, color = color), inherit.aes = FALSE)
}

p2 <- function() {
  ggplot(df, aes(x = value, y = name, fill = name)) + geom_density_ridges(alpha = 0.8) +
    geom_segment(aes(x = l[[1]], y = "A", xend = l[[1]], yend = 2, color = "mean")) +
    geom_segment(aes(x = l[[2]], y = "B", xend = l[[2]], yend = 3, color = "mean")) +
    geom_segment(aes(x = l[[3]], y = "C", xend = l[[3]], yend = 4, color = "mean")) +
    geom_segment(aes(x = l[[4]], y = "D", xend = l[[4]], yend = 5, color = "mean")) +
    geom_segment(aes(x = sd_27[[1]], y = "A", xend = sd_27[[1]], yend = 2, color = "sd_27")) +
    geom_segment(aes(x = sd_27[[2]], y = "B", xend = sd_27[[2]], yend = 3, color = "sd_27")) +
    geom_segment(aes(x = sd_27[[3]], y = "C", xend = sd_27[[3]], yend = 4, color = "sd_27")) +
    geom_segment(aes(x = sd_27[[4]], y = "D", xend = sd_27[[4]], yend = 5, color = "sd_27"))
}

# Check plot
p1()
#> Picking joint bandwidth of 0.381


# Compare running time
microbenchmark::microbenchmark(p1()) 
#> Unit: milliseconds
#>  expr      min       lq     mean   median      uq      max neval
#>  p1() 1.859514 1.917135 2.162416 1.936781 2.42122 5.056147   100
microbenchmark::microbenchmark(p2())
#> Unit: milliseconds
#>  expr     min       lq     mean   median       uq      max neval
#>  p2() 9.37298 9.669749 10.20821 9.774624 10.17852 22.42459   100
于 2021-04-14T06:04:15.003 回答