如何找到性能瓶颈？

高效 R 代码写作

Colin Gillespie

Jumping Rivers & Newcastle University

代码分析（Profiling）

总体思路：

运行代码
每隔几毫秒记录当前正在执行的内容
Rprof() 随 R 提供，正是做这个的
- 使用较棘手
改用 profvis

IMDB 数据集

来自 ggplot2movies 包

data(movies, package = "ggplot2movies")
dim(movies)

58788    24

数据框：约 60,000 行、24 列
每行对应一部电影

勇敢的心

braveheart = movies[7288,]

年份	片长	评分
1995	177	8.3

示例：勇敢的心

# Load data
data(movies,
     package = "ggplot2movies")
braveheart <- movies[7288,]
movies <- movies[movies$Action==1,]

plot(movies$year, movies$rating,
     xlab = "Year", ylab = "Rating")

# local regression line
model <- loess(rating ~ year,
               data = movies)
j <- order(movies$year)
lines(movies$year[j],
      model$fitted[j],
      col = "forestgreen")

points(braveheart$year,
       braveheart$rating,
       pch = 21,
       bg = "steelblue")

Profvis

RStudio 集成了使用 profvis 的分析支持
- 选中要分析的代码
- Profile -> Profile Selected lines

命令行

library("profvis")

profvis({

data(movies, package = "ggplot2movies") # Load data
braveheart <- movies[7288,]
movies <- movies[movies$Action == 1,]
plot(movies$year, movies$rating, xlab = "Year", ylab="Rating")
model <- loess(rating ~ year, data = movies) # loess regression line
j <- order(movies$year)
lines(movies$year[j], model$fitted[j], col="forestgreen", lwd=2)
points(braveheart$year, braveheart$rating,
     pch = 21, bg = "steelblue", cex = 3)

})

您认为哪一行最慢？

让我们来练习！

高效 R 代码写作