高效率代码课程目录
Chapter1. Benchmarking
Chapter2. R语言高效化基础
Chapter3. 查看Code内部
Chapter4. 多线程计算
使用profvis
通过使用profvis包可以记录和可视化每一步计算需要的资源。具体用法如下，
# Load the data set
data(movies, package = "ggplot2movies")
# Load the profvis package
library(profvis)
# Profile the following code with the profvis function
profvis({
# Load and select data
comedies <- movies[movies$Comedy == 1, ]
# Plot data of interest
plot(comedies$year, comedies$rating)
# Loess regression line
model <- loess(rating ~ year, data = comedies)
j <- order(comedies$year)
# Add fitted line to the plot
lines(comedies$year[j], model$fitted[j], col = "red")
}) ## Remember the closing brackets!
有点像复习上一章节的内容，创建一样格式的dataframe和matrix，比较一下各自需要花费的计算时间。
# Load the microbenchmark package
library(microbenchmark)
# The previous data frame solution is defined
# d() Simulates 6 dices rolls
d <- function() {
data.frame(
d1 = sample(1:6, 3, replace = TRUE),
d2 = sample(1:6, 3, replace = TRUE)
)
}
# Complete the matrix solution
m <- function() {
matrix(sample(1:6, 6, replace = TRUE), ncol=2)
}
# Use microbenchmark to time m() and d()
microbenchmark(
data.frame_solution = d(),
matrix_solution = m()
)
Unit: microseconds
expr min lq mean median uq max
data.frame_solution 102.613 122.4190 179.33370 140.9915 171.0330 2420.308
matrix_solution 4.764 5.7765 32.10798 7.5525 10.4335 2346.358
neval
100
100
rowSums比apply更加快捷。
# Define the new solution
r_sum <- function(x) {
rowSums(x)
}
# Compare the methods
microbenchmark(
app_sol = app(rolls),
r_sum_sol = r_sum(rolls)
)
Unit: microseconds
expr min lq mean median uq max neval
app_sol 20.394 22.6620 44.38427 23.5745 25.788 1953.120 100
r_sum_sol 5.078 5.9265 20.99668 6.4335 7.164 1381.345 100
&和&&的差别
A&B: 不管A是不是FALSE，B都会被计算，结果返回FLASE
A&&B: 如果A是FALSE，B就会被跳过，结果返回FALSE
这样就节省了很多计算资源。
# Example data
is_double
[1] FALSE TRUE TRUE
# Define the previous solution
move <- function(is_double) {
if (is_double[1] & is_double[2] & is_double[3]) {
current <- 11 # Go To Jail
}
}
# Define the improved solution
improved_move <- function(is_double) {
if (is_double[1] && is_double[2] && is_double[3]) {
current <- 11 # Go To Jail
}
}
# microbenchmark both solutions
# Very occassionally the improved solution is actually a little slower
# This is just random chance
microbenchmark(move, improved_move, times = 1e5)
Unit: nanoseconds
expr min lq mean median uq max neval
move 20 24 29.39710 25 27 67893 1e+05
improved_move 20 24 27.30683 25 26 18635 1e+05
