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).
expand(data, ..., .name_repair = "check_unique")
crossing(..., .name_repair = "check_unique")
nesting(..., .name_repair = "check_unique")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).
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.
With grouped data frames created by dplyr::group_by(), expand() operates
within each group. Because of this, you cannot expand on a grouping column.
complete() to expand list objects. expand_grid()
to input vectors rather than a data frame.
# 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)
#> # A tibble: 2 × 1
#> type
#> <chr>
#> 1 apple
#> 2 orange
fruits %>% expand(size)
#> # A tibble: 4 × 1
#> size
#> <fct>
#> 1 XS
#> 2 S
#> 3 M
#> 4 L
fruits %>% expand(type, size)
#> # A tibble: 8 × 2
#> type size
#> <chr> <fct>
#> 1 apple XS
#> 2 apple S
#> 3 apple M
#> 4 apple L
#> 5 orange XS
#> 6 orange S
#> 7 orange M
#> 8 orange L
fruits %>% expand(type, size, year)
#> # A tibble: 24 × 3
#> type size year
#> <chr> <fct> <dbl>
#> 1 apple XS 2010
#> 2 apple XS 2011
#> 3 apple XS 2012
#> 4 apple S 2010
#> 5 apple S 2011
#> 6 apple S 2012
#> 7 apple M 2010
#> 8 apple M 2011
#> 9 apple M 2012
#> 10 apple L 2010
#> # ℹ 14 more rows
# Only combinations that already appear in the data
fruits %>% expand(nesting(type))
#> # A tibble: 2 × 1
#> type
#> <chr>
#> 1 apple
#> 2 orange
fruits %>% expand(nesting(size))
#> # A tibble: 3 × 1
#> size
#> <fct>
#> 1 XS
#> 2 S
#> 3 M
fruits %>% expand(nesting(type, size))
#> # A tibble: 4 × 2
#> type size
#> <chr> <fct>
#> 1 apple XS
#> 2 apple M
#> 3 orange S
#> 4 orange M
fruits %>% expand(nesting(type, size, year))
#> # A tibble: 5 × 3
#> type size year
#> <chr> <fct> <dbl>
#> 1 apple XS 2010
#> 2 apple M 2012
#> 3 orange S 2010
#> 4 orange S 2011
#> 5 orange M 2012
# Other uses ----------------------------------------------------------------
# Use with `full_seq()` to fill in values of continuous variables
fruits %>% expand(type, size, full_seq(year, 1))
#> # A tibble: 24 × 3
#> type size `full_seq(year, 1)`
#> <chr> <fct> <dbl>
#> 1 apple XS 2010
#> 2 apple XS 2011
#> 3 apple XS 2012
#> 4 apple S 2010
#> 5 apple S 2011
#> 6 apple S 2012
#> 7 apple M 2010
#> 8 apple M 2011
#> 9 apple M 2012
#> 10 apple L 2010
#> # ℹ 14 more rows
fruits %>% expand(type, size, 2010:2013)
#> # A tibble: 32 × 3
#> type size `2010:2013`
#> <chr> <fct> <int>
#> 1 apple XS 2010
#> 2 apple XS 2011
#> 3 apple XS 2012
#> 4 apple XS 2013
#> 5 apple S 2010
#> 6 apple S 2011
#> 7 apple S 2012
#> 8 apple S 2013
#> 9 apple M 2010
#> 10 apple M 2011
#> # ℹ 22 more rows
# Use `anti_join()` to determine which observations are missing
all <- fruits %>% expand(type, size, year)
all
#> # A tibble: 24 × 3
#> type size year
#> <chr> <fct> <dbl>
#> 1 apple XS 2010
#> 2 apple XS 2011
#> 3 apple XS 2012
#> 4 apple S 2010
#> 5 apple S 2011
#> 6 apple S 2012
#> 7 apple M 2010
#> 8 apple M 2011
#> 9 apple M 2012
#> 10 apple L 2010
#> # ℹ 14 more rows
all %>% dplyr::anti_join(fruits)
#> Joining with `by = join_by(type, size, year)`
#> # A tibble: 19 × 3
#> type size year
#> <chr> <fct> <dbl>
#> 1 apple XS 2011
#> 2 apple XS 2012
#> 3 apple S 2010
#> 4 apple S 2011
#> 5 apple S 2012
#> 6 apple M 2010
#> 7 apple M 2011
#> 8 apple L 2010
#> 9 apple L 2011
#> 10 apple L 2012
#> 11 orange XS 2010
#> 12 orange XS 2011
#> 13 orange XS 2012
#> 14 orange S 2012
#> 15 orange M 2010
#> 16 orange M 2011
#> 17 orange L 2010
#> 18 orange L 2011
#> 19 orange L 2012
# Use with `right_join()` to fill in missing rows (like `complete()`)
fruits %>% dplyr::right_join(all)
#> Joining with `by = join_by(type, year, size)`
#> # A tibble: 25 × 4
#> type year size weights
#> <chr> <dbl> <fct> <dbl>
#> 1 apple 2010 XS 1.60
#> 2 orange 2010 S 4.26
#> 3 apple 2012 M 2.56
#> 4 orange 2010 S 3.99
#> 5 orange 2011 S 4.62
#> 6 orange 2012 M 6.15
#> 7 apple 2011 XS NA
#> 8 apple 2012 XS NA
#> 9 apple 2010 S NA
#> 10 apple 2011 S NA
#> # ℹ 15 more rows
# Use with `group_by()` to expand within each group
fruits %>%
dplyr::group_by(type) %>%
expand(year, size)
#> # A tibble: 20 × 3
#> # Groups: type [2]
#> type year size
#> <chr> <dbl> <fct>
#> 1 apple 2010 XS
#> 2 apple 2010 S
#> 3 apple 2010 M
#> 4 apple 2010 L
#> 5 apple 2012 XS
#> 6 apple 2012 S
#> 7 apple 2012 M
#> 8 apple 2012 L
#> 9 orange 2010 XS
#> 10 orange 2010 S
#> 11 orange 2010 M
#> 12 orange 2010 L
#> 13 orange 2011 XS
#> 14 orange 2011 S
#> 15 orange 2011 M
#> 16 orange 2011 L
#> 17 orange 2012 XS
#> 18 orange 2012 S
#> 19 orange 2012 M
#> 20 orange 2012 L