使用 %in% 或 == 条件的 R 子集.应该使用哪一种? [英] R subset with condition using %in% or ==. Which one should be used?

问题描述

通常，如果我想对某些值的数据帧条件进行子集化，我使用的变量是子集和 %in%:

Usually, if I want to subset a dataframe conditioning of some values a variable I'm using subset and %in%:

x <- data.frame(u=1:10,v=LETTERS[1:10])
x
subset(x, v %in% c("A","D"))

现在，我发现 == 也给出了相同的结果:

Now, I found out that also == gives the same result:

subset(x, v == c("A","D"))

我只是想知道它们是否相同，或者是否有理由偏爱一个.感谢您的帮助.

I'm just wondering if they are identically or if there is a reason to prefere one over the other. Thanks for help.

编辑(@MrFlick):这个问题与不同this here 询问如何不包含多个值:(!x %in% c('a','b')).我问为什么如果我使用 == 或 %in% 会得到同样的结果.

Edit (@MrFlick): This question asks not the same as this here which asks how to not include several values: (!x %in% c('a','b')). I asked why I got the same if I use ==or %in%.

推荐答案

你应该使用第一个 %in% 因为你得到结果只是因为在示例数据集中，它是在顺序中A、D 的回收.这里是比较

You should use the first one %in% because you got the result only because in the example dataset, it was in the order of recycling of A, D. Here, it is comparing

rep(c("A", "D"), length.out= nrow(x))
# 1] "A" "D" "A" "D" "A" "D" "A" "D" "A" "D"

 x$v==rep(c("A", "D"), length.out= nrow(x))# only because of coincidence
 #[1]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE


subset(x, v == c("D","A"))
#[1] u v
#<0 rows> (or 0-length row.names)

虽然在上面

 x$v==rep(c("D", "A"), length.out= nrow(x))
 #[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

而 %in% 有效

subset(x, v %in% c("D","A"))
#  u v
#1 1 A
#4 4 D

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