tidyr (version 1.3.1)

expand: Expand data frame to include all possible combinations of values

Description

expand() generates all combination of variables found in a dataset. It is paired with nesting() and crossing() helpers. crossing() is a wrapper around expand_grid() that de-duplicates and sorts its inputs; nesting() is a helper that only finds combinations already present in the data.

expand() is often useful in conjunction with joins:

• use it with right_join() to convert implicit missing values to explicit missing values (e.g., fill in gaps in your data frame).

• use it with anti_join() to figure out which combinations are missing (e.g., identify gaps in your data frame).

Usage

expand(data, ..., .name_repair = "check_unique")
crossing(..., .name_repair = "check_unique")
nesting(..., .name_repair = "check_unique")

Arguments

data

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).

.name_repair

Treatment of problematic column names:

• "minimal": No name repair or checks, beyond basic existence,

• "unique": Make sure names are unique and not empty,

• "check_unique": (default value), no name repair, but check they are unique,

• "universal": Make the names unique and syntactic

• a function: apply custom name repair (e.g., .name_repair = make.names for names in the style of base R).

• A purrr-style anonymous function, see rlang::as_function()

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

Grouped data frames

With grouped data frames created by dplyr::group_by(), expand() operates within each group. Because of this, you cannot expand on a grouping column.

See Also

complete() to expand list objects. expand_grid() to input vectors rather than a data frame.

Examples

# Finding combinations ------------------------------------------------------
fruits <- tibble(
  type = c("apple", "orange", "apple", "orange", "orange", "orange"),
  year = c(2010, 2010, 2012, 2010, 2011, 2012),
  size = factor(
    c("XS", "S", "M", "S", "S", "M"),
    levels = c("XS", "S", "M", "L")
  ),
  weights = rnorm(6, as.numeric(size) + 2)
)

# All combinations, including factor levels that are not used
fruits %>% expand(type)
fruits %>% expand(size)
fruits %>% expand(type, size)
fruits %>% expand(type, size, year)

# Only combinations that already appear in the data
fruits %>% expand(nesting(type))
fruits %>% expand(nesting(size))
fruits %>% expand(nesting(type, size))
fruits %>% expand(nesting(type, size, year))

# Other uses ----------------------------------------------------------------
# Use with `full_seq()` to fill in values of continuous variables
fruits %>% expand(type, size, full_seq(year, 1))
fruits %>% expand(type, size, 2010:2013)

# Use `anti_join()` to determine which observations are missing
all <- fruits %>% expand(type, size, year)
all
all %>% dplyr::anti_join(fruits)

# Use with `right_join()` to fill in missing rows (like `complete()`)
fruits %>% dplyr::right_join(all)

# Use with `group_by()` to expand within each group
fruits %>%
  dplyr::group_by(type) %>%
  expand(year, size)