Turns implicit missing values into explicit missing values. This is a wrapper around expand(), 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.

To find all unique combinations of x, y and z, including those not found in the data, supply each variable as a separate argument. To find only the combinations that occur in the data, use nest: 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 every student for each date.

For factors, the full set of levels (not just those that appear in the data) are used. For continuous variables, you may need to fill in values that don't appear in the data: to do so use expressions like year = 2010:2020 or year = full_seq(year).

Length-zero (empty) elements are automatically dropped.

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.

See also

complete_ for a version that uses regular evaluation and is suitable for programming with.

Examples

library(dplyr)
#> #> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:tidyr’: #> #> everything, id, regroup
#> The following objects are masked from ‘package:stats’: #> #> filter, lag
#> The following object is masked from ‘package:testthat’: #> #> matches
#> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union
df <- data_frame( 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 × 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 × 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