Summarize data at different aggregate levels - R and tidyverse

Another alternative:

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

You can write a function which does the same summarize on an ungrouped tibble and rbinds that to the end.

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

You could add some logic for what the "Overall" groups should be named if you want

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

^{Created on 2019-06-21 by the reprex package (v0.3.0)}