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

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

Arguments

data

A data frame.

...

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

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 = \link{full_seq}(year,1).

fill

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

Details

If you supply fill, these values will also replace existing explicit missing values in the data set.

Examples

library(dplyr, warn.conflicts = FALSE) df <- tibble( group = c(1:2, 1), item_id = c(1:2, 2), item_name = c("a", "b", "b"), value1 = 1:3, value2 = 4:6 ) df %>% complete(group, nesting(item_id, item_name))
#> # A tibble: 4 x 5 #> group item_id item_name value1 value2 #> <dbl> <dbl> <chr> <int> <int> #> 1 1 1 a 1 4 #> 2 1 2 b 3 6 #> 3 2 1 a NA NA #> 4 2 2 b 2 5
# You can also choose to fill in missing values df %>% complete(group, nesting(item_id, item_name), fill = list(value1 = 0))
#> # A tibble: 4 x 5 #> group item_id item_name value1 value2 #> <dbl> <dbl> <chr> <dbl> <int> #> 1 1 1 a 1 4 #> 2 1 2 b 3 6 #> 3 2 1 a 0 NA #> 4 2 2 b 2 5