August 2018
Intermediate to advanced
227 pages
4h 16m
Chinese
Content preview from 高效R语言编程
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128
｜
第
6
章
introduction.html
）开始了解
dplyr
。
3.
在 wb_ineq 数据集上测试另外
dplyrverbs[
输入 vignette（package =
"
dplyr
"
）可找到更多的
vignette
名字
]
。
非标准计算
最后有一个关于
dplyr
包的话题不是关于数据的，而是关于该函数语法的。
请注意本节代码例程中的很多参数使用没有引号包裹的原始名字（例如
用的是 Country，而不是 " Country"）。
这称为非标准计算（
NSE
）（见
vignette(
"
nse
"
)）。
特意采用
NSE
，是为了更高效地交互使用函数。
NSE
减少了键盘输入并允许在
RStudio
中自动完成。
当使用交互式
R
时，这是非常好的。
但当你要非交互地使用
R
时，通常使
用标准计算更健壮，因为它将模糊范围之类的
bug
降到最低。
如果你将代码
包含在包中，使用标准计算也避免了不得不声明全局变量。
基于这个原因，
tidyr
与
dplyr
中的多数函数有两个版本：一个使用
NSE
（默认），另外一个
使用标准计算并要求引号包裹的变量名。
函数的标准计算版本使用
_
后缀标
识。下面代码中的 gather() 功能说明这点：
# 1:
默认的
NSE
函数
group_by(cars, cut(speed, c(0, 10, 100))) %>% summarize(mean(dist))
#> # A tibble: 2
×
2
#> `cut(speed, c(0, 10, 100))` `mean(dist)`
#> ...
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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.