你可以使用更新的dplyr::reframe（它取代了dplyr::summarize），并将这个组合汇总统计数据（comb）添加到你的函数列表中：

library(dplyr)
library(tidyr)

iris %>%
  group_by(Species) %>%
  reframe(across(everything(), 
                 list(Mean = ~ as.character(mean(.x)), 
                      dev.sd = ~ as.character(sd(.x)), 
                      comb = ~ paste(mean(.x), sd(.x), sep = " ± ")))) %>%
  pivot_longer(cols = -Species, names_to = c(".value", "variable"), 
               names_sep = "_")

# (from comment) if you only wanted the combined column and want 
# them at two significant digits, you could adjust:

iris %>%
  group_by(Species) %>%
  reframe(across(everything(), 
                 list(comb = ~ paste(sprintf("%.2f", mean(.x)), 
                                     sprintf("%.2f", sd(.x)), sep = " ± ")))) %>%
  pivot_longer(cols = -Species, names_to = c(".value", "variable"), 
               names_sep = "_")

#' In this case you get the exact same thing if you replace `reframe` with 
#' `summarize`, but the latter is being replaced by `reframe` 
#' by `dplyr` moving forward

注意与pivot_longer合并，所有元素需要在同一个类中，因此将它们转换为字符。如果保持宽，则不必在汇总统计中添加as.character()位。
输出

Species    variable Sepal.Length              Sepal.Width               Petal.Length              Petal.Width              
  <fct>      <chr>    <chr>                     <chr>                     <chr>                     <chr>                    
1 setosa     Mean     5.006                     3.428                     1.462                     0.246                    
2 setosa     dev.sd   0.352489687213451         0.379064369096289         0.173663996480184         0.105385589380046        
3 setosa     comb     5.006 ± 0.352489687213451 3.428 ± 0.379064369096289 1.462 ± 0.173663996480184 0.246 ± 0.105385589380046
4 versicolor Mean     5.936                     2.77                      4.26                      1.326                    
5 versicolor dev.sd   0.516171147063863         0.313798323378411         0.469910977239958         0.197752680004544        
6 versicolor comb     5.936 ± 0.516171147063863 2.77 ± 0.313798323378411  4.26 ± 0.469910977239958  1.326 ± 0.197752680004544
7 virginica  Mean     6.588                     2.974                     5.552                     2.026                    
8 virginica  dev.sd   0.635879593274432         0.322496638172637         0.551894695663983         0.274650055636667        
9 virginica  comb     6.588 ± 0.635879593274432 2.974 ± 0.322496638172637 5.552 ± 0.551894695663983 2.026 ± 0.274650055636667