August 2018
Intermediate to advanced
227 pages
4h 16m
Chinese
Content preview from 高效R语言编程
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144
｜
第
7
章
result1 = ifelse(marks >= 40, "pass", "fail")
}
）
#> user system elapsed
#> 4.293 0.351 4.667
system.time({
result2 = rep("fail", length(marks))
result2[marks >= 40] = "pass"
}
）
#> user system elapsed
#> 0.192 0.052 0.244
identical(result1, result2)
#> [1] TRUE
R-devel
邮件列表中有一份关于基础
R
中加速 ifelse() 的讨论。一个简单办
法是使用
dplyr
中的 if_else()，尽管在相同系列中讨论，但不是所有情况下
它都能替代 ifelse()。根据我们测试结果，测试例子中 if_else() 表现很好，
并且也远快于基础
R
的实现（尽管它仍比硬编码方法慢大约
3
倍）：
system.time({
result3 = dplyr::if_else(marks >= 40, "pass", "fail")
}
）
#> user system elapsed
#> 1.065 0.188 1.253
identical(result1, result3)
#> [1] TRUE
排序和排名
排序一个向量是相对较快的；排序一个长度为
10
7
的向量大约花费
0.01
秒。
如果你只在一个脚本顶部排序一次向量，不需过于担心这个问题 ...
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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.