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Turns implicit missing values into explicit missing values. This is a wrapper around expand(), dplyr::full_join() and replace_na() that's useful for completing missing combinations of data.

Usage

complete(data, ..., fill = list(), explicit = TRUE)

Arguments

data

A data frame.

...

Specification of columns to expand. Columns can be atomic vectors or lists.

  • To find all unique combinations of x, y and z, including those not present in the data, supply each variable as a separate argument: expand(df, x, y, z).

  • To find only the combinations that occur in the data, use nesting: expand(df, nesting(x, y, z)).

  • You can combine the two forms. For example, expand(df, nesting(school_id, student_id), date) would produce a row for each present school-student combination for all possible dates.

When used with factors, expand() uses the full set of levels, not just those that appear in the data. If you want to use only the values seen in the data, use forcats::fct_drop().

When used with continuous variables, you may need to fill in values that do not appear in the data: to do so use expressions like year = 2010:2020 or year = full_seq(year,1).

fill

A named list that for each variable supplies a single value to use instead of NA for missing combinations.

explicit

Should both implicit (newly created) and explicit (pre-existing) missing values be filled by fill? By default, this is TRUE, but if set to FALSE this will limit the fill to only implicit missing values.

Details

With grouped data frames, complete() operates within each group. Because of this, you cannot complete a grouping column.

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