不如这样：

library(dplyr)
df = data.frame(group = c("A","A","A","A","B","B","B","C","C"),  
               col1 = c(1, 1, 1, 1, 2, 2, 2, 3, 3),
               col2 = c(12, 13, 14, 15, 21, 22, 23, 31, 32))
maxgp <- max(table(df$group))

df %>% 
  group_by(group) %>% 
  summarise(across(everything(), ~c(.x, rep(NA, maxgp-n()))))
#> `summarise()` has grouped output by 'group'. You can override using the
#> `.groups` argument.
#> # A tibble: 12 × 3
#> # Groups:   group [3]
#>    group  col1  col2
#>    <chr> <dbl> <dbl>
#>  1 A         1    12
#>  2 A         1    13
#>  3 A         1    14
#>  4 A         1    15
#>  5 B         2    21
#>  6 B         2    22
#>  7 B         2    23
#>  8 B        NA    NA
#>  9 C         3    31
#> 10 C         3    32
#> 11 C        NA    NA
#> 12 C        NA    NA

由reprex package（v2.0.1）于2023年2月1日创建

您可以为每个组创建行号，然后tidyr::complete：

library(dplyr)

df %>%
  group_by(group) %>%
  mutate(id = row_number()) %>%
  ungroup() %>% 
  tidyr::complete(group, id) %>%
  select(-id)

# # A tibble: 12 × 3
#    group  col1  col2
#    <chr> <dbl> <dbl>
#  1 A         1    12
#  2 A         1    13
#  3 A         1    14
#  4 A         1    15
#  5 B         2    21
#  6 B         2    22
#  7 B         2    23
#  8 B        NA    NA
#  9 C         3    31
# 10 C         3    32
# 11 C        NA    NA
# 12 C        NA    NA

更新（来自@Maël的回答）

在dplyr 1.1.0之后，mutate()、summarise()、filter()和slice()系列支持Per-operation grouping with .by/by。

df %>%
  mutate(id = row_number(), .by = group) %>%
  tidyr::complete(group, id) %>%
  select(-id)