
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.
complete(data, ..., fill = list(), explicit = TRUE)
A data frame.
<data-masking
> Specification of columns
to expand or complete. 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)
or complete(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()
and complete()
use 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.
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.
With grouped data frames created by dplyr::group_by()
, complete()
operates within each group. Because of this, you cannot complete a grouping
column.
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
# Combinations --------------------------------------------------------------
# Generate all possible combinations of `group`, `item_id`, and `item_name`
# (whether or not they appear in the data)
df %>% complete(group, item_id, item_name)
# Cross all possible `group` values with the unique pairs of
# `(item_id, item_name)` that already exist in the data
df %>% complete(group, nesting(item_id, item_name))
# Within each `group`, generate all possible combinations of
# `item_id` and `item_name` that occur in that group
df %>%
dplyr::group_by(group) %>%
complete(item_id, item_name)
# Supplying values for new rows ---------------------------------------------
# Use `fill` to replace NAs with some value. By default, affects both new
# (implicit) and pre-existing (explicit) missing values.
df %>%
complete(
group,
nesting(item_id, item_name),
fill = list(value1 = 0, value2 = 99)
)
# Limit the fill to only the newly created (i.e. previously implicit)
# missing values with `explicit = FALSE`
df %>%
complete(
group,
nesting(item_id, item_name),
fill = list(value1 = 0, value2 = 99),
explicit = FALSE
)
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