dplyr中na_if()类型不匹配错误：替换Team_列的0为NA失败

我有数据框df_3，需要对所有以Team_开头的列执行mutate操作，将列中的0替换为NA。此前可用的代码如今报错，错误信息如下：

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 to match type of x .
Backtrace:

1. df_3 %>% mutate(across(starts_with("Team_"), ~na_if(., "0")))
2. dplyr::na_if(Team_Num_1, "0")

未修改原始数据框，不清楚报错原因，求问原因及解决方法。

可复现代码：

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"))

核心问题是数据类型不匹配：

• 你使用na_if(., "0")传入的是字符串"0"，但部分Team_开头的列（比如Team_Num_1、Team_Num_2）是数值型（double）。na_if要求两个参数的类型必须一致，字符串无法与数值类型匹配，因此抛出转换错误。
• 之前代码能运行，大概率是当时所有Team_列均为字符型，现在数据中混入了数值型列，导致类型不统一。

针对不同类型的列分别处理，确保替换值与列类型一致，以下是两种可行方案：

方案1：根据列类型动态匹配替换值 #

利用if_else结合is.character判断列类型，分别用字符串"0"或数值0执行替换：

library(dplyr)

df_3 <- df_3 %>%
  mutate(across(starts_with("Team_"), ~ if_else(is.character(.), na_if(., "0"), na_if(., 0))))

方案2：统一转换为字符型后替换（适合无需保留数值型的场景） #

先将所有Team_列转换为字符型，再替换"0"为NA：

df_3 <- df_3 %>%
  mutate(across(starts_with("Team_"), as.character)) %>%
  mutate(across(starts_with("Team_"), ~ na_if(., "0")))

内容的提问来源于stack exchange，提问作者Soph2010