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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())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).
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.
# NOT RUN {
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))
# You can also choose to fill in missing values
df %>% complete(group, nesting(item_id, item_name), fill = list(value1 = 0))
# }
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