Add margin row totals in dplyr chain

Question

I would like to add overall summary rows while also calculating summaries by group using dplyr. I have found various questions asking how to do this, e.g. here, here, and he

Answer 1

One option is with do

mtcars %>%
   count(cyl, gear) %>%
   ungroup() %>% 
   mutate(cyl=as.character(cyl)) %>% 
   do(bind_rows(., data.frame(cyl="Total", count(mtcars, gear)))) 
   #or replace the last 'do' step with 
   #bind_rows(cbind(cyl='Total', count(mtcars, gear))) #from  @JonnyPolonsky's comments

#      cyl  gear     n
#   <chr> <dbl> <int>
#1      4     3     1
#2      4     4     8
#3      4     5     2
#4      6     3     2
#5      6     4     4
#6      6     5     1
#7      8     3    12
#8      8     5     2
#9  Total     3    15
#10 Total     4    12
#11 Total     5     5

Answer 2

If you would like to have truly universal solution you could use a combination of purrr::map_df, base::c and base::sum mtcars %>% purrr::map_df(~c(.x, sum(.x, na.rm=TRUE))) %>% tail

P.S. All columns must be numeric!

Answer 3

With adorn_totals() from the janitor package:

library(janitor)
mtcars %>%
  tabyl(cyl, gear) %>%
  adorn_totals("row") 

   cyl  3  4 5
     4  1  8 2
     6  2  4 1
     8 12  0 2
 Total 15 12 5

To get from there to the "long" form in your post, add tidyr::gather() to the pipeline:

mtcars %>%
  tabyl(cyl, gear) %>%
  adorn_totals("row") %>%
  tidyr::gather(gear, n, 2:ncol(.), convert = TRUE)

     cyl gear  n
1      4    3  1
2      6    3  2
3      8    3 12
4  Total    3 15
5      4    4  8
6      6    4  4
7      8    4  0
8  Total    4 12
9      4    5  2
10     6    5  1
11     8    5  2
12 Total    5  5

Self-promotion alert, I authored this package - adding this answer b/c it's a genuinely efficient solution here.

Answer 4

Here's a take on the accepted answer, using new functions introduced in dplyr 1.0.0 and tidyr 1.0.0.

We pivot the counts using the new tidyr::pivot_wider. Then use the new dplyr::rowwise and dplyr::c_across to sum the counts for the total column.

We can also use tidyr::pivot_longer to get in desired long format.

library(dplyr, warn.conflicts = FALSE)
library(tidyr)

cyl_gear_sum <- mtcars %>%
  count(cyl, gear) %>%
  pivot_wider(names_from = gear, values_from = n, values_fill = list(n = 0)) %>%
  rowwise(cyl) %>%
  mutate(gear_total = sum(c_across()))

cyl_gear_sum
#> # A tibble: 3 x 5
#> # Rowwise:  cyl
#>     cyl   `3`   `4`   `5` gear_total
#>   <dbl> <int> <int> <int>      <int>
#> 1     4     1     8     2         11
#> 2     6     2     4     1          7
#> 3     8    12     0     2         14

# total as row
cyl_gear_sum %>% 
  pivot_longer(-cyl, names_to = "gear", values_to = "n")
#> # A tibble: 12 x 3
#>      cyl gear           n
#>    <dbl> <chr>      <int>
#>  1     4 3              1
#>  2     4 4              8
#>  3     4 5              2
#>  4     4 gear_total    11
#>  5     6 3              2
#>  6     6 4              4
#>  7     6 5              1
#>  8     6 gear_total     7
#>  9     8 3             12
#> 10     8 4              0
#> 11     8 5              2
#> 12     8 gear_total    14

^{Created on 2020-04-07 by the reprex package (v0.3.0)}

Answer 5

An addition to @arkrun's answer that is not easy to add as a comment:

Although a little more complex, this format allows for previous modifications in the data frame. Useful when there is a longer chain of verbs before the table is generated. (You want to change names, or select only specific variables)

mtcars %>%
   count(cyl, gear) %>%
   ungroup() %>% 
   mutate(cyl=as.character(cyl))
bind_rows(group_by(.,gear) %>%
              summarise(n=sum(n)) %>%
              mutate(cyl='Total')) %>%
spread(cyl)

## A tibble: 3 x 5
#   gear   `4`   `6`   `8` Total
#* <dbl> <dbl> <dbl> <dbl> <dbl>
#1     3     1     2    12    15
#2     4     8     4     0    12
#3     5     2     1     2     5

This can also be doubled up to generate a total row for the spread as well.

mtcars %>%
  count(cyl, gear) %>%
  ungroup() %>% 
  mutate(cyl=as.character(cyl),
         gear = as.character(gear)) %>%
  bind_rows(group_by(.,gear) %>%
              summarise(n=sum(n)) %>%
              mutate(cyl='Total')) %>%
  bind_rows(group_by(.,cyl) %>%
              summarise(n=sum(n)) %>%
              mutate(gear='Total')) %>%
  spread(cyl,n,fill=0)

# A tibble: 4 x 5
   gear   `4`   `6`   `8` Total
* <chr> <dbl> <dbl> <dbl> <dbl>
1     3     1     2    12    15
2     4     8     4     0    12
3     5     2     1     2     5
4 Total    11     7    14    32

Answer 6

Here is my suggestion.

1. Find the combination of relevant grouping variables via the powerSet function.
2. split the data frame into a list, grouped by the powerSet of the grouping variables
3. summarise the data frame using an appropriate summary function (e.g. mean)
4. bind_rows the result - summaries are now NA because these columns are dropped in step 3
5. replace NA values of the grouping variables using appropriate names.

Note. If grouping variables are numeric, they will not be dropped in step 3 - I therefore mutate them to character variables.

powerSetList <- function(df, ...) {
  rje::powerSet(x = c(...))[-1] %>% lapply(function(x, tdf = df) group_by(tdf, .dots=x)) %>% c(list(tibble(df)), .)
} 

mtcars %>% 
  mutate_at(vars("cyl", "gear"), as.character) %>%
  powerSetList("cyl", "gear") %>%
  map(~summarise_if(., is.numeric, .funs = mean)) %>%
  bind_rows() %>%
  replace_na(list(gear = "all gears",
                  cyl = "all cyls"))