为什么ifelse要将一个数据.frame转换为list: ifelse(TRUE, data.frame(1)， 0)) != data.frame(1)?

I want to return a data.frame from a function if TRUE, else return NA using return(ifelse(condition, mydf, NA))

我想要返回一个data.frame，如果是TRUE，则返回NA使用return(ifelse(条件，mydf, NA))

However, ifelse strips the column names from the data.frame.

但是，ifelse会从data.frame中删除列名。

Why are these results different?

为什么这些结果不同?

> data.frame(1)
  X1
1  1
> ifelse(TRUE, data.frame(1), NA)
[[1]]
[1] 1

Some additional insight from dput():

来自dput()的一些附加见解:

> dput(ifelse(TRUE, data.frame(1), 0))
list(1)
> dput(data.frame(1))
structure(list(X1 = 1), .Names = "X1", row.names = c(NA, -1L), 
          class = "data.frame")

1 个解决方案

#1

ifelse is generally intended for vectorized comparisons, and has side-effects such as these: as it says in ?ifelse,

ifelse通常用于矢量化比较，并且有如下副作用:如它在?ifelse中所说，

‘ifelse’ returns a value with the same shape as ‘test’ ...

so in this case (test is a vector of length 1) it tries to convert the data frame to a 'vector' (list in this case) of length 1 ...

所以在这种情况下(test是一个长度为1的向量)它试图将数据帧转换成长度为1的“向量”(在这种情况下是列表)。

return(if (condition) mydf else NA)

As a general design point I try to return objects of the same structure no matter what, so I might prefer

作为一个通用的设计点，无论如何我都试图返回相同结构的对象，所以我可能更喜欢

if (!condition) mydf[] <- NA
return(mydf)

As a general rule, I find that R users (especially coming from other programming languages) start by using if exclusively, take a while to discover ifelse, then overuse it for a while, discovering later that you really want to use if in logical contexts. A similar thing happens with & and &&.

作为一般规则，我发现R用户(特别是来自其他编程语言的用户)首先使用if专有，花一些时间来发现ifelse，然后过度使用它一段时间，然后发现在逻辑上下文中确实需要使用if。&和&&也有类似的情况。

#1