另一种选择：

library(tidyverse)  

iris %>% 
  mutate_at("Species", as.character) %>%
  list(group_by(.,Species), .) %>%
  map(~summarize(.,mean_s_length = mean(Sepal.Length),
                 max_s_width = max(Sepal.Width))) %>%
  bind_rows() %>%
  replace_na(list(Species="Overall"))
#> # A tibble: 4 x 3
#>   Species    mean_s_length max_s_width
#>   <chr>              <dbl>       <dbl>
#> 1 setosa              5.01         4.4
#> 2 versicolor          5.94         3.4
#> 3 virginica           6.59         3.8
#> 4 Overall             5.84         4.4

您可以编写一个函数，对ungroup艾德tibble执行相同的summarize操作，并将其绑定到末尾。

summarize2 <- function(df, ...){
 bind_rows(summarise(df, ...), summarize(ungroup(df), ...))
}

iris %>% 
  group_by(Species) %>%
  summarize2(
    mean_s_length = mean(Sepal.Length),
    max_s_width = max(Sepal.Width)
  )

# # A tibble: 4 x 3
#   Species    mean_s_length max_s_width
#   <fct>              <dbl>       <dbl>
# 1 setosa              5.01         4.4
# 2 versicolor          5.94         3.4
# 3 virginica           6.59         3.8
# 4 NA                  5.84         4.4

如果需要，可以为“总体”组的命名添加一些逻辑

summarize2 <- function(df, ...){
  s1 <- summarise(df, ...)
  s2 <- summarize(ungroup(df), ...)
  for(v in group_vars(s1)){
    if(is.factor(s1[[v]]))
      s1[[v]] <- as.character(s1[[v]])
    if(is.character(s1[[v]])) 
     s2[[v]] <- 'Overall'
    else if(is.numeric(s1[[v]])) 
     s2[[v]] <- -Inf
  }
  bind_rows(s1, s2)
}

iris %>% 
  group_by(Species, g = Petal.Length %/% 1) %>%
  summarize2(
    mean_s_length = mean(Sepal.Length),
    max_s_width = max(Sepal.Width)
  )

# # Groups:   Species [4]
#   Species        g mean_s_length max_s_width
#   <chr>      <dbl>         <dbl>       <dbl>
# 1 setosa         1          5.01         4.4
# 2 versicolor     3          5.35         2.9
# 3 versicolor     4          6.09         3.4
# 4 versicolor     5          6.35         3  
# 5 virginica      4          5.85         3  
# 6 virginica      5          6.44         3.4
# 7 virginica      6          7.43         3.8
# 8 Overall     -Inf          5.84         4.4

library(dplyr)

iris %>% 
  group_by(Species) %>%
  summarize(mean_s_length = mean(Sepal.Length),
            max_s_width = max(Sepal.Width)) %>%
  ungroup() %>% 
  mutate_at(vars(Species), as.character) %>% 
  {rbind(.,c("Overal",mean(.$mean_s_length),max(.$max_s_width)))} %>%
  mutate_at(vars(-Species), as.double) %>% 
  mutate_at(vars(Species), as.factor)
#> # A tibble: 4 x 3
#>   Species    mean_s_length max_s_width
#>   <fct>              <dbl>       <dbl>
#> 1 setosa              5.01         4.4
#> 2 versicolor          5.94         3.4
#> 3 virginica           6.59         3.8
#> 4 Overal              5.84         4.4

创建于2019-06-21由reprex package（v0.3.0）

一种方法是将第二个summarise指令放在bind_rows中，这种方法也很繁琐，但在一个较长的管道中。
as.character调用避免了警告：
警告消息：
1：在bind_rows_（x，.id）中：
绑定因子和特征向量，强制转换为特征向量
2：在bind_rows_（x，.id）中：
绑定字符和因子向量，强制为字符向量

library(tidyverse)

summary_grouped <- iris %>% 
  mutate(Species = as.character(Species)) %>%
  group_by(Species) %>%
  summarize(mean_s_length = mean(Sepal.Length),
            max_s_width = max(Sepal.Width)) %>%
  bind_rows(iris %>% 
              summarize(mean_s_length = mean(Sepal.Length),
                        max_s_width = max(Sepal.Width)) %>%
              mutate(Species = "Overall"))
## A tibble: 4 x 3
#  Species    mean_s_length max_s_width
#  <chr>              <dbl>       <dbl>
#1 setosa              5.01         4.4
#2 versicolor          5.94         3.4
#3 virginica           6.59         3.8
#4 Overall             5.84         4.4

也许是这样的：
当你想对同一个输入（iris）执行不同的操作时，最好在不同的汇总函数上执行map并应用于数据。map_dfr使用bind_rows组合列表输出

library(dplyr)
library(purrr)

pipe <- . %>%
  group_by(Species) %>%
  summarize(
    mean_s_length = mean(Sepal.Length),
    max_s_width   = max(Sepal.Width))

map_dfr(
  list(pipe, . %>% mutate(Species = "Overall") %>% pipe),
  exec, 
  iris)
#> Warning in bind_rows_(x, .id): binding factor and character vector,
#> coercing into character vector
#> Warning in bind_rows_(x, .id): binding character and factor vector,
#> coercing into character vector
#> # A tibble: 4 x 3
#>   Species    mean_s_length max_s_width
#>   <chr>              <dbl>       <dbl>
#> 1 setosa              5.01         4.4
#> 2 versicolor          5.94         3.4
#> 3 virginica           6.59         3.8
#> 4 Overall             5.84         4.4

解决方案，您需要在双数据集上仅应用所需函数一次：

library(tidyverse)
iris %>%
  rbind(mutate(., Species = "Overall")) %>%
  group_by(Species) %>%
  summarize(
    mean_s_length = mean(Sepal.Length),
    max_s_width = max(Sepal.Width)
  )

# A tibble: 4 x 3
  Species    mean_s_length max_s_width
  <chr>              <dbl>       <dbl>
1 Overall             5.84         4.4
2 setosa              5.01         4.4
3 versicolor          5.94         3.4
4 virginica           6.59         3.8

技巧是用一个新的组ID（即Species）传递原始数据集：mutate(iris, Species = "Overall")