complete(data, ..., fill = list())
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.
A named list that for each variable supplies a single value to
use instead of
NA for missing combinations.
If you supply
fill, these values will also replace existing
explicit missing values in the data set.
complete_ for a version that uses regular evaluation
and is suitable for programming with.
library(dplyr)#> #>#>#> #>#>#> #>#>#> #>#>#> #>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