August 2018
Intermediate to advanced
227 pages
4h 16m
Chinese
Content preview from 高效R语言编程
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150
｜
第
7
章
2.
查看下面的函数定义，说下你对整数用法的理解：
• tail.matrix()
• lm()
3.
创建一个整数矩阵与数值矩阵，使用 pryr::object_size() 比较两个矩阵。
4.
用于
tail.matrix() 的 seq.int() 函数与标准 seq() 函数有何差异？
一个相对节约内存空间的思想是使用
bit
包的向量替换逻辑向量，仅占用
1/300
的空间（但你不能使用
NAs
）。
例子：优化
move_square()
函数
图
7-2
展示了在模拟
Monopoly
游戏中，我们的主要瓶颈是 move_squre() 函数。
在这个函数中，我们花费了大约
50%
的时间创建一个数据框，
20%
的时间计
算行的和，剩余的是比较操作。这段代码可通过下列方法非常简单地进行优
化（同时保持相同的整体结构）：
注
2
•
使用
1:6
替换了原有的
seq(1,6)
生成骰子的
6
个可能值。另外，使用矩阵
替换数据集，并调用一次
sample()
函数。
matrix
（
sample
（
1:6, 6, replace = TRUE
）
, ncol = 2
）
总体来说，修改后的这行代码大约有
25
倍的速度提升；速度提升主要来
自转换成矩阵。
•
使用
rowSums()
替换了
apply()
。
apply()
函数调用已经是很快的，不过我
们从数据集切换到矩阵。使用矩阵的
rowSums()
带来了
10
倍的速度提升。
•
在
if
条件语句中使用
&&
，这大约是
&
的两倍速度。
重构后代码居然比原来版本速度提升了
20
倍，吃惊不？比较图
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.