在dplyr管道中使用which.min函数遇问题，求更简洁优雅解法

问题说明 #

使用dplyr管道调用which.min提取每行最小值对应的列索引时，全NA行会触发报错（which.min返回长度为0的向量）。现有条件判断的解决方案较为繁琐，以下提供更简洁的实现方式。

可复现示例 #

library(dplyr)

data <- data.frame(s1=c(10,NA,5,NA,NA), s2=c(8,NA,NA,4,20), s3=c(NA,NA,2,NA,10))
data
#>   s1 s2 s3
#> 1 10  8 NA
#> 2 NA NA NA
#> 3  5 NA  2
#> 4 NA  4 NA
#> 5 NA 20 10

提取最小值（正常工作） #

min(x, na.rm=TRUE)可正常提取每行最小值，仅全NA行返回Inf并给出警告：

data %>%
  rowwise() %>%
  mutate(Min_s = min(c(s1,s2,s3), na.rm=TRUE))
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `Min_s = min(c(s1, s2, s3), na.rm = TRUE)`.
#> ℹ In row 2.
#> Caused by warning in `min()`:
#> ! no non-missing arguments to min; returning Inf
#> # A tibble: 5 × 4
#> # Rowwise: 
#>      s1    s2    s3 Min_s
#>   <dbl> <dbl> <dbl> <dbl>
#> 1    10     8    NA     8
#> 2    NA    NA    NA   Inf
#> 3     5    NA     2     2
#> 4    NA     4    NA     4
#> 5    NA 20    10    10

直接调用which.min的报错问题 #

全NA行中which.min返回长度为0的向量，不符合dplyr对列长度的要求，触发报错：

data %>%
  rowwise() %>%
  mutate(which_s = which.min(c(s1,s2,s3)))
#> Error in `mutate()`:
#> ℹ In argument: `which_s = which.min(c(s1, s2, s3))`.
#> ℹ In row 2.
#> Caused by error:
#> ! `which_s` must be size 1, not 0.
#> ℹ Did you mean: `which_s = list(which.min(c(s1, s2, s3)))` ?

现有繁琐解决方案 #

通过手动条件判断规避全NA行报错：

data %>%
  rowwise() %>%
  mutate(which_s = if(!is.na(s1)|!is.na(s2)|!is.na(s3)) {which.min(c(s1,s2,s3))} else NA )
#> # A tibble: 5 × 4
#> # Rowwise: 
#>      s1    s2    s3 which_s
#>   <dbl> <dbl> <dbl>   <int>
#> 1    10     8    NA       2
#> 2    NA    NA    NA      NA
#> 3     5    NA     2       3
#> 4    NA     4    NA       2
#> 5    NA 20    10       3

更简洁的优雅实现 #

方法1：用if_all简化条件判断 #

用if_all批量检查是否全为NA，替代手动多列判断：

data %>%
  rowwise() %>%
  mutate(which_s = if(!if_all(c(s1, s2, s3), is.na)) which.min(c(s1, s2, s3)) else NA)

方法2：用purrr::map_int替代rowwise #

避免rowwise的性能开销，通过map_int逐行处理：

library(purrr)

data %>%
  mutate(which_s = pmap_int(across(s1:s3), ~{
    vals <- c(...)
    all(is.na(vals)) %>% ifelse(NA_integer_, which.min(vals))
  }))

方法3：用replace处理全NA情况 #

直接在which.min结果上替换全NA行的值：

data %>%
  rowwise() %>%
  mutate(which_s = replace(which.min(c(s1,s2,s3)), all(is.na(c(s1,s2,s3))), NA))

内容的提问来源于stack exchange，提问作者Wael