Restore Groups After Applying a Function to a Data Frame

Groups are sometimes removed by dplyr functions, such as dplyr::summarize(). However, it may be necessary to ensure that the data output by a function retains as many of the original groups as are available after applying the function. with_retain_groups() applies a function to a grouped data frame and restores the original group as much as possible.

with_retain_groups(.data, .f, ...)

Arguments

.data	A grouped tbl, tibble, or data.frame
.f	A function, formula, or vector (not necessarily atomic). If a function, it is used as is. If a formula, e.g. ~ .x + 2, it is converted to a function. There are three ways to refer to the arguments: For a single argument function, use . For a two argument function, use .x and .y For more arguments, use ..1, ..2, ..3 etc This syntax allows you to create very compact anonymous functions. If character vector, numeric vector, or list, it is converted to an extractor function. Character vectors index by name and numeric vectors index by position; use a list to index by position and name at different levels. If a component is not present, the value of .default will be returned.
...	Additional arguments passed on to methods.

See also

Other Group Utilities: group_drop, with_ungroup

Examples

# with_retain_groups() applies inner function to grouped data frame
# and restores grouping on output
tidyr::table1 %>%
  dplyr::group_by(country, year) %>%
  with_retain_groups(~ dplyr::summarize(., cases = sum(cases)))
#> `summarise()` regrouping output by 'country' (override with `.groups` argument)
#> # A tibble: 6 x 3
#> # Groups:   country, year [6]
#>   country      year  cases
#>   <chr>       <int>  <int>
#> 1 Afghanistan  1999    745
#> 2 Afghanistan  2000   2666
#> 3 Brazil       1999  37737
#> 4 Brazil       2000  80488
#> 5 China        1999 212258
#> 6 China        2000 213766

# Groups that "disappear" are implicitly dropped, with a warning
tidyr::table1 %>%
  dplyr::group_by(country, year) %>%
  with_retain_groups(~ {
    dplyr::summarize(., r = cases / population) %>%
      dplyr::summarize(r = mean(r))
  })
#> `summarise()` regrouping output by 'country' (override with `.groups` argument)
#> `summarise()` ungrouping output (override with `.groups` argument)
#> Warning: groups were implicitly dropped: `year`
#> # A tibble: 3 x 2
#> # Groups:   country [3]
#>   country             r
#>   <chr>           <dbl>
#> 1 Afghanistan 0.0000834
#> 2 Brazil      0.000340 
#> 3 China       0.000167 

# Works like "normal" if no groupings are present
tidyr::table1 %>%
  with_retain_groups(~ dplyr::mutate(., r = cases / population))
#> # A tibble: 6 x 5
#>   country      year  cases population         r
#>   <chr>       <int>  <int>      <int>     <dbl>
#> 1 Afghanistan  1999    745   19987071 0.0000373
#> 2 Afghanistan  2000   2666   20595360 0.000129 
#> 3 Brazil       1999  37737  172006362 0.000219 
#> 4 Brazil       2000  80488  174504898 0.000461 
#> 5 China        1999 212258 1272915272 0.000167 
#> 6 China        2000 213766 1280428583 0.000167