pivot_wider() "widens" data, increasing the number of columns and decreasing the number of rows. The inverse transformation is pivot_longer(). Learn more in vignette("pivot", "tidyr").

Note that pivot_wider() is not and cannot be lazy because we need to look at the data to figure out what the new column names will be.

## Usage

# S3 method for tbl_lazy
pivot_wider(
data,
...,
id_cols = NULL,
id_expand = FALSE,
names_from = name,
names_prefix = "",
names_sep = "_",
names_glue = NULL,
names_sort = FALSE,
names_vary = "fastest",
names_expand = FALSE,
names_repair = "check_unique",
values_from = value,
values_fill = NULL,
values_fn = ~max(.x, na.rm = TRUE),
unused_fn = NULL
)

## Arguments

data

A lazy data frame backed by a database query.

...

Unused; included for compatibility with generic.

id_cols

A set of columns that uniquely identifies each observation.

id_expand

Unused; included for compatibility with the generic.

names_from, values_from

A pair of arguments describing which column (or columns) to get the name of the output column (names_from), and which column (or columns) to get the cell values from (values_from).

If values_from contains multiple values, the value will be added to the front of the output column.

names_prefix

String added to the start of every variable name.

names_sep

If names_from or values_from contains multiple variables, this will be used to join their values together into a single string to use as a column name.

names_glue

Instead of names_sep and names_prefix, you can supply a glue specification that uses the names_from columns (and special .value) to create custom column names.

names_sort

Should the column names be sorted? If FALSE, the default, column names are ordered by first appearance.

names_vary

When names_from identifies a column (or columns) with multiple unique values, and multiple values_from columns are provided, in what order should the resulting column names be combined?

• "fastest" varies names_from values fastest, resulting in a column naming scheme of the form: value1_name1, value1_name2, value2_name1, value2_name2. This is the default.

• "slowest" varies names_from values slowest, resulting in a column naming scheme of the form: value1_name1, value2_name1, value1_name2, value2_name2.

names_expand

Should the values in the names_from columns be expanded by expand() before pivoting? This results in more columns, the output will contain column names corresponding to a complete expansion of all possible values in names_from. Additionally, the column names will be sorted, identical to what names_sort would produce.

names_repair

What happens if the output has invalid column names?

values_fill

Optionally, a (scalar) value that specifies what each value should be filled in with when missing.

values_fn

A function, the default is max(), applied to the value in each cell in the output. In contrast to local data frames it must not be NULL.

unused_fn

Optionally, a function applied to summarize the values from the unused columns (i.e. columns not identified by id_cols, names_from, or values_from).

The default drops all unused columns from the result.

This can be a named list if you want to apply different aggregations to different unused columns.

id_cols must be supplied for unused_fn to be useful, since otherwise all unspecified columns will be considered id_cols.

This is similar to grouping by the id_cols then summarizing the unused columns using unused_fn.

## Details

The big difference to pivot_wider() for local data frames is that values_fn must not be NULL. By default it is max() which yields the same results as for local data frames if the combination of id_cols and value column uniquely identify an observation. Mind that you also do not get a warning if an observation is not uniquely identified.

The translation to SQL code basically works as follows:

1. Get unique keys in names_from column.

2. For each key value generate an expression of the form:

value_fn(
CASE WHEN (names from column == key value)
THEN (value column)
END
) AS output column

3. Group data by id columns.

4. Summarise the grouped data with the expressions from step 2.

## Examples

memdb_frame(
id = 1,
key = c("x", "y"),
value = 1:2
) %>%
tidyr::pivot_wider(
id_cols = id,
names_from = key,
values_from = value
)
#> # Source:   SQL [1 x 3]
#> # Database: sqlite 3.40.1 [:memory:]
#>      id     x     y
#>   <dbl> <int> <int>
#> 1     1     1     2