dplyr if_else() 与基础 R ifelse() [英] dplyr if_else() vs base R ifelse()

问题描述

我对 Tidyverse 相当精通，但一直使用 ifelse() 而不是 dplyr if_else().我想切换此行为并默认始终使用 dplyr::if_else() 并从我的代码中弃用 ifelse().

I am fairly proficient within the Tidyverse, but have always used ifelse() instead of dplyr if_else(). I want to switch this behavior and default to always using dplyr::if_else() and deprecating ifelse() from my code.

有什么理由不这样做吗?这可能会给我带来麻烦吗?我会告诉你细节，但最近，当我在我的数据分析中不知不觉地创建了一列字符矩阵时，不使用 if_else() 把我搞砸了.如果我切换到始终使用 if_else()，我希望将来避免这个问题.

Is there any reason not to do this? Would this likely get me into trouble? I'll spare you the details, but recently, not using if_else() screwed me up, when I unknowingly created a column of character matrices in my data analysis. If I switch to always using if_else() I hope to avoid this issue in the future.

推荐答案

if_else 更严格.它检查两个选项是否属于相同类型，否则会抛出错误，而 ifelse 将根据需要提升类型.这在某些情况下可能是一个好处，但如果您不检查错误或明确强制类型转换，则可能会破坏脚本.例如:

if_else is more strict. It checks that both alternatives are of the same type and otherwise throws an error, while ifelse will promote types as necessary. This may be a benefit in some circumstances, but may otherwise break scripts if you don't check for errors or explicitly force type conversion. For example:

ifelse(c(TRUE,TRUE,FALSE),"a",3)
[1] "a" "a" "3"
if_else(c(TRUE,TRUE,FALSE),"a",3)
Error: `false` must be type character, not double

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