Remove single dplyr group_by group

Question

In the case where a tibble is grouped by multiple variables in dplyr, is there a way to remove a single grouping variable other than re-specifying the groups without that variable? I'm thinking it would be something like group_by(df, -var, add = TRUE), though that doesn't work.

Example:

library(dplyr)

# Works
mtcars %>%
  # Original groups
  group_by(cyl, gear, carb) %>%
  # New groups
  group_by(cyl, gear) %>%
  group_vars() 
# [1] "cyl"  "gear"

# Doesn't work
mtcars %>%
  # Original groups
  group_by(cyl, gear, carb) %>%
  # New groups
  group_by(-carb, add = TRUE) %>%
  group_vars() 
# [1] "cyl"   "gear"  "carb"  "-carb"

This is clearly a trivial example - my actual use case has lots of conditional groupings based on user input and I'd like to just drop one grouping at some point in the function and leave the rest.

I m not sure this can be done directly from `group_by` or even `ungroup()` (because note that `ungroup(carb)` will not work either). However, you may be able to use `group_by_at()` or `group_by_if()` to handle your case — Sotos, Mar 02 '18 at 08:20
Thanks - I'll take a look at those. I think I can save the grouping variables with `group_vars()` and probably pass an edited version of that to `group_by_at()` — MeetMrMet, Mar 02 '18 at 08:33
Do you guys know if the `...` arguments of `ungroup` is used at all ? It's not consistent with the relevant help file. — moodymudskipper, Mar 02 '18 at 09:36
It doesn't seem that way for `ungroup` - I didn't even know `ungroup` had `...` arguments. I've never seen it used with any arguments except the tbl — MeetMrMet, Mar 02 '18 at 15:05

moodymudskipper · Answer 1 · 2018-03-02T09:27:43.843

You could make a custom function using dplyr::groups or dplyr::group_vars :

ungroup_some <- function(x,...){
  grps <- setdiff(group_vars(x),unlist(list(...)))
  group_by(x,.dots= grps)
}

mtcars %>%
  group_by(cyl, gear, carb) %>%
  ungroup_some("carb")

# # A tibble: 32 x 11
# # Groups:   cyl, gear [8]
#     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#  * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#  1  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
#  2  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
#  3  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
#  4  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
#  5  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2
#  6  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
#  7  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
#  8  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
#  9  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
# 10  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
# # ... with 22 more rows

score 2 · Accepted Answer · answered Mar 02 '18 at 09:24

2

One can use also .dots specification and group by all except some. E.g.

library(dplyr)
ungroup_by <- function(x,...){
  group_by_(x, .dots = group_vars(x)[!group_vars(x) %in% ...])
}

mtcars %>%
  group_by(cyl, gear, carb) %>%
  ungroup_by('cyl') %>%
  group_vars() 
[1] "gear" "carb"

Similar information can be found at this post.

answered Mar 02 '18 at 09:24

Volodymyr

888
10
21

1

Great idea. Seems like with `dplyr 0.7` perhaps using `group_by_at` rather than the older SE `group_by_` would be appropriate. – MeetMrMet Mar 02 '18 at 15:02

score 1 · Answer 3 · answered Mar 10 '22 at 13:36

ungroup works directly in dplyr 1.0.8

library(dplyr)
mtcars %>%
  group_by(cyl, gear, carb) %>%
  ungroup(cyl) 
# # A tibble: 32 x 11
# # Groups:   gear, carb [11]
#      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
# 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4

Remove single dplyr group_by group

3 Answers3