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dplyr::ungroup

dplyr::ungroup は、tidyverse コレクションの dplyr パッケージに含まれる関数で、データフレームのグループ化を解除するために使用される。

クイックリファレンス​

library(tidyverse)

grouped_df %>%
  ungroup()
penguins %>%
  group_by(island) %>%
  ungroup()

# # A tibble: 344 × 7
#    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex
#    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> <fct>
#  1 Adelie  Torgersen           39.1          18.7               181        3750 male
#  2 Adelie  Torgersen           39.5          17.4               186        3800 female
#  3 Adelie  Torgersen           40.3          18                 195        3250 female
#  4 Adelie  Torgersen           NA            NA                  NA          NA NA
#  5 Adelie  Torgersen           36.7          19.3               193        3450 female
#  6 Adelie  Torgersen           39.3          20.6               190        3650 male
#  7 Adelie  Torgersen           38.9          17.8               181        3625 female
#  8 Adelie  Torgersen           39.2          19.6               195        4675 male
#  9 Adelie  Torgersen           34.1          18.1               193        3475 NA
# 10 Adelie  Torgersen           42            20.2               190        4250 NA
# # ℹ 334 more rows
# # ℹ Use `print(n = ...)` to see more rows

基本構文​

ungroup(x, ...)
引数説明
xtibble。
...グループ化から削除する列名を指定。

使用例​

1. グループ化の解除​

penguins %>%
  group_by(species) %>%
  summarise(avg_body_mass = mean(body_mass_g, na.rm = TRUE)) %>%
  ungroup()

# # A tibble: 3 × 2
#   species   avg_body_mass
#   <fct>             <dbl>
# 1 Adelie            3701.
# 2 Chinstrap         3733.
# 3 Gentoo            5076.

2. グループのレベルを下げる​

penguins %>%
  group_by(species, island) %>%
  ungroup(island)

# # A tibble: 344 × 7
# # Groups:   species [3]
#    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex
#    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> <fct>
#  1 Adelie  Torgersen           39.1          18.7               181        3750 male
#  2 Adelie  Torgersen           39.5          17.4               186        3800 female
#  3 Adelie  Torgersen           40.3          18                 195        3250 female
#  4 Adelie  Torgersen           NA            NA                  NA          NA NA
#  5 Adelie  Torgersen           36.7          19.3               193        3450 female
#  6 Adelie  Torgersen           39.3          20.6               190        3650 male
#  7 Adelie  Torgersen           38.9          17.8               181        3625 female
#  8 Adelie  Torgersen           39.2          19.6               195        4675 male
#  9 Adelie  Torgersen           34.1          18.1               193        3475 NA
# 10 Adelie  Torgersen           42            20.2               190        4250 NA
# # ℹ 334 more rows
# # ℹ Use `print(n = ...)` to see more rows