问题：
我有一个仅由数字数据类型的变量组成的 DataFrame。我有一个例程，过去在检查 DataFrame 中的每个变量是否有统计异常值并用 NA 值替换任何已识别的异常值方面做得很好。然而，这个例程利用了最近被软弃用的 funs()。
研究过这个问题后，我知道你应该能够基本上用 list(~ example_func()) 替换 funs() 例如：
>funs(mean(., trim = .2), median(., na.rm = TRUE))
>
>Would become:
>
>list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
不幸的是，这种补救措施在我的用例中不起作用。
功能正常但现已软弃用的代码：
以下代码有效，如下所示（对于具有离群值的变量，离群值被替换为 NA 值）；但是，它会触发有关现已软弃用的 funs() 的警告：
> # Which variables have missing values
> sapply(training_imptd, function(x) sum(is.na(x)))
INDEX TARGET_WINS TEAM_BATTING_H TEAM_BATTING_2B TEAM_BATTING_3B
0 0 0 0 0
TEAM_BATTING_HR TEAM_BATTING_BB TEAM_BATTING_SO TEAM_BASERUN_SB TEAM_BASERUN_CS
0 0 102 131 772
TEAM_BATTING_HBP TEAM_PITCHING_H TEAM_PITCHING_HR TEAM_PITCHING_BB TEAM_PITCHING_SO
2085 0 0 0 102
TEAM_FIELDING_E TEAM_FIELDING_DP
0 286
>
> # Identify outliers and set them to NA (NAs to be fixed in next step by mice)
> training_imptd <- training_imptd %>%
+ mutate_all(
+ funs(ifelse(. %in% boxplot.stats(training_imptd$.)$out, NA, .))
+ )
>
> Warning: funs() is soft deprecated as of dplyr 0.8.0
> Please use a list of either functions or lambdas:
>
> # Simple named list:
> list(mean = mean, median = median)
>
> # Auto named with `tibble::lst()`:
> tibble::lst(mean, median)
>
> # Using lambdas
> list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
> This warning is displayed once per session.
>
> # Which variables have missing values (after imputing NA for outliers)
> sapply(training_imptd, function(x) sum(is.na(x)))
INDEX TARGET_WINS TEAM_BATTING_H TEAM_BATTING_2B TEAM_BATTING_3B
0 32 67 15 29
TEAM_BATTING_HR TEAM_BATTING_BB TEAM_BATTING_SO TEAM_BASERUN_SB TEAM_BASERUN_CS
0 129 102 252 827
TEAM_BATTING_HBP TEAM_PITCHING_H TEAM_PITCHING_HR TEAM_PITCHING_BB TEAM_PITCHING_SO
2086 213 4 90 140
TEAM_FIELDING_E TEAM_FIELDING_DP
303 318
修复后的代码应该可以工作，但没有：
根据我读到的有关用 list(~ example_func()) 替换 funs() 的内容，我希望以下代码的执行与上面利用 funs() 的代码完全相同，但事实并非如此（对于具有异常值的变量） ，异常值不会被 NA 值替换）：
> # Which variables have missing values
> sapply(training_imptd, function(x) sum(is.na(x)))
INDEX TARGET_WINS TEAM_BATTING_H TEAM_BATTING_2B TEAM_BATTING_3B
0 0 0 0 0
TEAM_BATTING_HR TEAM_BATTING_BB TEAM_BATTING_SO TEAM_BASERUN_SB TEAM_BASERUN_CS
0 0 102 131 772
TEAM_BATTING_HBP TEAM_PITCHING_H TEAM_PITCHING_HR TEAM_PITCHING_BB TEAM_PITCHING_SO
2085 0 0 0 102
TEAM_FIELDING_E TEAM_FIELDING_DP
0 286
>
> # Identify outliers and set them to NA (NAs to be fixed in next step by mice)
> training_imptd <- training_imptd %>%
+ mutate_all(
+ list(~ ifelse(. %in% boxplot.stats(training_imptd$.)$out, NA, .))
+ )
>
> # Which variables have missing values (after imputing NA for outliers)
> sapply(training_imptd, function(x) sum(is.na(x)))
INDEX TARGET_WINS TEAM_BATTING_H TEAM_BATTING_2B TEAM_BATTING_3B
0 0 0 0 0
TEAM_BATTING_HR TEAM_BATTING_BB TEAM_BATTING_SO TEAM_BASERUN_SB TEAM_BASERUN_CS
0 0 102 131 772
TEAM_BATTING_HBP TEAM_PITCHING_H TEAM_PITCHING_HR TEAM_PITCHING_BB TEAM_PITCHING_SO
2085 0 0 0 102
TEAM_FIELDING_E TEAM_FIELDING_DP
0 286
