为什么dplyr重新编码在重新编码为NA而不是NaN时会产生错误 [英] Why does dplyr recode generate error when recoding to NA but not NaN

问题描述

我正在用dplyr进行编码.将值重新编码为NA而不是NaN时出现错误.这是一个示例:

I'm recoding with dplyr. I'm getting an error when I recode a value to NA, but not NaN. Here's an example:

df <- df %>% mutate(var=recode(var,`2`=0,`3`=NaN))

工作正常，而

df <- df %>% mutate(var=recode(var,`2`=0,`3`=NA))

给我以下错误:

Error: Vector 2 must be a double vector, not a logical vector

推荐答案

运行代码时，您会收到此错误

When running the code you get this error

tibble(var = rep(2:3, 4)) %>% 
 mutate(var=recode(var,`2`=0,`3`=NA)) 
# Error: Vector 2 must be a double vector, not a logical vector

这是因为 NA 是合乎逻辑的，但 recode 却希望加倍

This is because NA is logical, but recode is expecting a double

class(NA)
# [1] "logical"

您可以改用 NA_real _ ，因为那是双精度的

You can use NA_real_ instead, since that's a double

class(NA_real_)
# [1] "numeric"
is.double(NA_real_)
# [1] TRUE

tibble(var = rep(2:3, 4)) %>% 
 mutate(var=recode(var,`2`=0,`3`=NA_real_)) 
#     var
#   <dbl>
# 1     0
# 2    NA
# 3     0
# 4    NA
# 5     0
# 6    NA
# 7     0
# 8    NA

有关为什么期望加倍，请参见?recode

For why it's expecting a double, see ?recode

所有替换必须是相同类型，并且长度必须相同与.x相同或相同的长度.

All replacements must be the same type, and must have either length one or the same length as .x.

我认为这是出乎意料的原因是因为像 c 这样的基本函数并不关心元素的类型是否相同，反而只会向上转换.这样就可以了:

I think the reason this is unexpected is because base functions like c don't care if the elements are of the same type and will just convert upwards anyway. So this works:

c(1, NA, 3)

由于 c 函数:

根据组件的最高类型确定输出类型在层次结构中，NULL<原始<逻辑上整数<双重<复数<字符<清单<表达

The output type is determined from the highest type of the components in the hierarchy NULL < raw < logical < integer < double < complex < character < list < expression

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