Pivot data from long to wide — pivot_wider.tbl

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. If you have a long running query you have two options:

(temporarily) store the result of the query via compute().
Create a spec before and use dbplyr_pivot_wider_spec() - dbplyr's version of tidyr::pivot_wider_spec(). Note that this function is only a temporary solution until pivot_wider_spec() becomes a generic. It will then be removed soon afterwards.

Usage

# S3 method for class '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
)

dbplyr_pivot_wider_spec(
  data,
  spec,
  ...,
  names_repair = "check_unique",
  id_cols = NULL,
  id_expand = FALSE,
  values_fill = NULL,
  values_fn = ~max(.x, na.rm = TRUE),
  unused_fn = NULL,
  error_call = current_env()
)

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.

spec

A specification data frame. This is useful for more complex pivots because it gives you greater control on how metadata stored in the columns become column names in the result.

Must be a data frame containing character .name and .value columns. Additional columns in spec should be named to match columns in the long format of the dataset and contain values corresponding to columns pivoted from the wide format. The special .seq variable is used to disambiguate rows internally; it is automatically removed after pivoting.

error_call

The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.

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:

Get unique keys in names_from column.

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`

Group data by id columns.
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 3.47.1 [:memory:]
#>      id     x     y
#>   <dbl> <int> <int>
#> 1     1     1     2