R: Examples for 'tidyr::complete'

### ** Examples

library(dplyr, warn.conflicts = FALSE)

df <- tibble(
  group = c(1:2, 1, 2),
  item_id = c(1:2, 2, 3),
  item_name = c("a", "a", "b", "b"),
  value1 = c(1, NA, 3, 4),
  value2 = 4:7
)
df

# A tibble: 4 × 5
  group item_id item_name value1 value2
  <dbl>   <dbl> <chr>      <dbl>  <int>
1     1       1 a              1      4
2     2       2 a             NA      5
3     1       2 b              3      6
4     2       3 b              4      7

# Generate all possible combinations of `group`, `item_id`, and `item_name`
# (whether or not they appear in the data)
complete(df, group, item_id, item_name)

# A tibble: 12 × 5
   group item_id item_name value1 value2
   <dbl>   <dbl> <chr>      <dbl>  <int>
 1     1       1 a              1      4
 2     1       1 b             NA     NA
 3     1       2 a             NA     NA
 4     1       2 b              3      6
 5     1       3 a             NA     NA
 6     1       3 b             NA     NA
 7     2       1 a             NA     NA
 8     2       1 b             NA     NA
 9     2       2 a             NA      5
10     2       2 b             NA     NA
11     2       3 a             NA     NA
12     2       3 b              4      7

# Cross all possible `group` values with the unique pairs of
# `(item_id, item_name)` that already exist in the data
complete(df, group, nesting(item_id, item_name))

# A tibble: 8 × 5
  group item_id item_name value1 value2
  <dbl>   <dbl> <chr>      <dbl>  <int>
1     1       1 a              1      4
2     1       2 a             NA     NA
3     1       2 b              3      6
4     1       3 b             NA     NA
5     2       1 a             NA     NA
6     2       2 a             NA      5
7     2       2 b             NA     NA
8     2       3 b              4      7

# Within each `group`, generate all possible combinations of
# `item_id` and `item_name` that occur in that group
df %>%
  group_by(group) %>%
  complete(item_id, item_name)

# A tibble: 8 × 5
# Groups:   group [2]
  group item_id item_name value1 value2
  <dbl>   <dbl> <chr>      <dbl>  <int>
1     1       1 a              1      4
2     1       1 b             NA     NA
3     1       2 a             NA     NA
4     1       2 b              3      6
5     2       2 a             NA      5
6     2       2 b             NA     NA
7     2       3 a             NA     NA
8     2       3 b              4      7

# You can also choose to fill in missing values. By default, both implicit
# (new) and explicit (pre-existing) missing values are filled.
complete(
  df,
  group,
  nesting(item_id, item_name),
  fill = list(value1 = 0, value2 = 99)
)

# A tibble: 8 × 5
  group item_id item_name value1 value2
  <dbl>   <dbl> <chr>      <dbl>  <int>
1     1       1 a              1      4
2     1       2 a              0     99
3     1       2 b              3      6
4     1       3 b              0     99
5     2       1 a              0     99
6     2       2 a              0      5
7     2       2 b              0     99
8     2       3 b              4      7

# You can limit the fill to only implicit missing values by setting
# `explicit` to `FALSE`
complete(
  df,
  group,
  nesting(item_id, item_name),
  fill = list(value1 = 0, value2 = 99),
  explicit = FALSE
)

# A tibble: 8 × 5
  group item_id item_name value1 value2
  <dbl>   <dbl> <chr>      <dbl>  <int>
1     1       1 a              1      4
2     1       2 a              0     99
3     1       2 b              3      6
4     1       3 b              0     99
5     2       1 a              0     99
6     2       2 a             NA      5
7     2       2 b              0     99
8     2       3 b              4      7

Examples for 'tidyr::complete'

Complete a data frame with missing combinations of data