dplyr错误：组合group_by，mutate和ifelse时出现奇怪的问题。是bug吗 [英] dplyr error: strange issue when combining group_by, mutate and ifelse. Is it a bug?

问题描述

我在dplyr和group_by，mutate和ifelse的组合中有奇怪的问题。考虑以下data.frame

 > df1 
 crawl.id group.id hits.diff 
 1 1 1 NA 
 2 1 2 NA 
 3 2 2 0 
 4 1 3 NA 
 5 1 3 NA 
 6 1 3 NA 
  

当我使用它时， p>

 库（dplyr）
 df1％>％
 group_by（group.id）％>％
 mutate（hits.consumed = ifelse（hits.diff <= 0，-hits.diff，0））
  

由于某些原因，我得到

 错误：不兼容的类型，期望一个逻辑向量** 
  

但是，删除 group_by（）或 ifelse 一切正常工作：

  df1％>％
 mutate .consumed = ifelse（hits.diff <= 0，-hits.diff，0））
 
 crawl.id group.id hits.diff hits.consumed 
 1 1 1 NA NA 
 2 1 2 NA NA 
 3 2 2 0 0 
 4 1 3 NA NA 
 5 1 3 NA NA 
 6 1 3 NA NA 
 
 df1％>％ 
 group_by（group.id）％>％
 mutate（hits.consumed = -hits.diff）
 
 crawl.id group.id hits.diff hits.consumed 
 1 1 1 NA NA 
 2 1 2 NA NA 
 3 2 2 0 0 
 4 1 3 NA NA 
 5 1 3 NA NA 
 6 1 3 NA NA 
  

是错误还是功能？任何人都可以复制这个吗？
关于group_by，mutate和ifelse的特定组合使它失败的特别之处是什么？

我自己的研究让我在这里：
https://github.com/hadley/dplyr/issues/464
这表明现在应该修复它。

这里是 dput（df1）：

 结构（list（crawl.id = c（1，1，2，1，1，1），group.id = 
 2L，2L，3L，3L，3L），.Label = c（1，2，3），class =factor），
 hits.diff = c NA，NA，0，NA，NA，NA）），.Names = c（crawl.id，
group.id，hits.diff），row.names = c（NA， -6L），class =data.frame）
  

解决方案

将其全部包含在 as.numeric 中以强制输出格式，以便 NA s，这些 logical 默认情况下，不要覆盖输出变量的类：

  df1％ >％
 group_by（group.id）％>％
 mutate（hits.consumed = as.numeric（ifelse（hits.diff <= 0，-hits.diff，0）））
 
 ＃crawl.id group.id hits.diff hits.consumed 
＃1 1 1 NA NA 
＃2 1 2 NA NA 
＃3 2 2 0 0 
＃4 1 3 NA NA 
＃5 1 3 NA NA 
＃6 1 3 NA NA 
  

很确定这是与以下相同的问题：自定义总和在dplyr中的函数返回不一致的结果，因为这个结果表明：

  out<  -  df1 [1：2， ]％>％mutate（hits.consumed = ifelse（hits.diff <= 0，-hits.diff，0））
 class（out $ hits.consumed）
＃[1]逻辑
 out<  -  df1 [1：3，]％>％mutate（hits.consumed = ifelse（hits.diff < = 0，-hits.diff，0））
 class（out $ hits.consumed）
＃[1]numeric
  

I am having strange issues with dplyr and combination of group_by, mutate and ifelse. Consider the following data.frame

> df1
  crawl.id group.id hits.diff
1        1        1        NA
2        1        2        NA
3        2        2         0
4        1        3        NA
5        1        3        NA
6        1        3        NA

When I use it the following code

library(dplyr)
df1 %>%
  group_by(group.id) %>% 
  mutate( hits.consumed = ifelse(hits.diff<=0,-hits.diff,0) )

For some reason I get

Error: incompatible types, expecting a logical vector**

However, removing either group_by() or ifelse everything works as expected:

df1 %>%
  mutate( hits.consumed = ifelse(hits.diff<=0,-hits.diff,0) )

crawl.id group.id hits.diff hits.consumed
1        1        1        NA            NA
2        1        2        NA            NA
3        2        2         0             0
4        1        3        NA            NA
5        1        3        NA            NA
6        1        3        NA            NA

df1 %>%
  group_by( group.id ) %>%
  mutate( hits.consumed = -hits.diff )

  crawl.id group.id hits.diff hits.consumed
1        1        1        NA            NA
2        1        2        NA            NA
3        2        2         0             0
4        1        3        NA            NA
5        1        3        NA            NA
6        1        3        NA            NA

Is it a bug or a feature? Can anyone replicate this? What's so special about that specific combination of group_by, mutate and ifelse that makes it fail?

My own research led me here: https://github.com/hadley/dplyr/issues/464 which suggests that it should be fixed by now.

Here is dput(df1):

structure(list(crawl.id = c(1, 1, 2, 1, 1, 1), group.id = structure(c(1L, 
2L, 2L, 3L, 3L, 3L), .Label = c("1", "2", "3"), class = "factor"), 
    hits.diff = c(NA, NA, 0, NA, NA, NA)), .Names = c("crawl.id", 
"group.id", "hits.diff"), row.names = c(NA, -6L), class = "data.frame")

解决方案

Wrap it all in as.numeric to force the output format so the NAs, which are logical by default, don't override the class of the output variable:

df1 %>%
  group_by(group.id) %>% 
  mutate( hits.consumed = as.numeric(ifelse(hits.diff<=0,-hits.diff,0)) )

#  crawl.id group.id hits.diff hits.consumed
#1        1        1        NA            NA
#2        1        2        NA            NA
#3        2        2         0             0
#4        1        3        NA            NA
#5        1        3        NA            NA
#6        1        3        NA            NA

Pretty sure this is the same issue as here: Custom sum function in dplyr returns inconsistent results , as this result suggests:

out <- df1[1:2,] %>%  mutate( hits.consumed = ifelse(hits.diff <= 0, -hits.diff, 0))
class(out$hits.consumed)
#[1] "logical"
out <- df1[1:3,] %>%  mutate( hits.consumed = ifelse(hits.diff <= 0, -hits.diff, 0))
class(out$hits.consumed)
#[1] "numeric"

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