`na_if()`中的错误：！无法将 `y` <character> 转换为匹配 `x` <double>

Question

我有一个数据帧 df_3，我想从中突变以 Team_ 开头的多个列。我想用 NA 替换列中包含的 0。我使用了之前成功使用过的代码，但现在出现以下错误：

Error in `mutate()`:
ℹ In argument: `across(starts_with("Team_"), ~na_if(., "0"))`.
Caused by error in `across()`:
! Can't compute column `Team_Num_1`.
Caused by error in `na_if()`:
! Can't convert `y` <character> to match type of `x` <double>.
Backtrace:
  1. df_3 %>% mutate(across(starts_with("Team_"), ~na_if(., "0")))
 10. dplyr::na_if(Team_Num_1, "0")

知道为什么会这样或者我该如何解决它吗？我没有更改原始 df 中的任何内容以及之前运行的代码，不确定发生了什么变化。

可复制代码：

structure(list(Team_1 = c("0", "werg", "sdf"), Team_Desc_1 = c("wer", 
"wtrb", "wergt"), Team_URL_1 = c("ewrg", "werg", "asd"), Team_Ver_1 = c("25", 
"2523", "342"), Team_Num_1 = c(0, 23, 12), Team_Value_1 = c("aed", 
"jfsa", "vsf"), Name_1 = c("etwbv", "werg", "sdfg"), Txt_1 = c("abc", 
"bfh", "fse"), Head_1 = c("abc1", "bfh", "fse"), Team_2 = c("werh", 
"wtt", "qwe"), Team_Desc_2 = c("sdfg", "wer", "sdfgv"), Team_URL_2 = c("qwe", 
"gvre", "vrw"), Team_Ver_2 = c("4123", "5133", "4126"), Team_Num_2 = c(3, 
0, 123), Team_Value_2 = c("aewed", "jfsbwa", "vsbf"), Name_2 = c("qwreg", 
"gvr", "wref"), Txt_2 = c("rege", "wer", "vwr"), Head_2 = c("rege1", 
"wer", "vwr")), row.names = c(NA, -3L), class = c("tbl_df", "tbl", 
"data.frame"))

Answer 1

根据 dplyr 1.1.0 的 changelog，

na_if()

现在使用 vctrs 包，该包对类型稳定性更加严格：

na_if()

(#6329) 现在在比较之前将

y

转换为

x

的类型，这使得更清楚地表明该函数在

x

上的类型和大小是稳定的。

所以，请使用

na_if(x, "0")

:

library(dplyr)

dat %>%
  mutate(across(starts_with("Team_"), ~ na_if(.x, "0")))
# # A tibble: 3 × 18
#   Team_1 Team_Desc_1 Team_UR…¹ Team_…² Team_…³ Team_…⁴ Name_1 Txt_1 Head_1 Team_2
#   <chr>  <chr>       <chr>     <chr>   <chr>   <chr>   <chr>  <chr> <chr>  <chr> 
# 1 NA     wer         ewrg      25      aed     aed     etwbv  abc   abc1   werh  
# 2 werg   wtrb        werg      2523    jfsa    jfsa    werg   bfh   bfh    wtt   
# 3 sdf    wergt       asd       342     vsf     vsf     sdfg   fse   fse    qwe   
# # … with 8 more variables: Team_Desc_2 <chr>, Team_URL_2 <chr>,
# #   Team_Ver_2 <chr>, Team_Num_2 <chr>, Team_Value_2 <chr>, Name_2 <chr>,
# #   Txt_2 <chr>, Head_2 <chr>, and abbreviated variable names ¹​Team_URL_1,
# #   ²​Team_Ver_1, ³​Team_Num_1, ⁴​Team_Value_1

如果您混合使用字符和数字列，您可以这样做：

# example data
dat2 <- tibble(
  Team_1 = c("0", "werg", "sdf"), 
  Team_Desc_1 = c(0, 3, 4), 
  Name_1 = c("etwbv", "werg", "sdfg")
)

dat2 %>% 
  mutate(
    across(starts_with("Team_") & where(is.character), ~ na_if(.x, "0")),
    across(starts_with("Team_") & where(is.numeric), ~ na_if(.x, 0)),
  )
# # A tibble: 3 × 3
#   Team_1 Team_Desc_1 Name_1
#   <chr>        <dbl> <chr> 
# 1 NA              NA etwbv 
# 2 werg             3 werg  
# 3 sdf              4 sdfg 

Answer 2

我遇到了同样的问题，为了简单起见，选择了以下方法，无论不同列的数据类如何，它都应该有效。

all_data[all_data %in% c(-Inf, Inf)] <- NA