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pivot-wide.md

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error when overwriting existing column

Code
  (expect_error(pivot_wider(df, names_from = key, values_from = val)))
Output
  <error/vctrs_error_names_must_be_unique>
  Names must be unique.
  x These names are duplicated:
    * "a" at locations 1 and 2.
  i Use argument `names_repair` to specify repair strategy.

Code
  out <- pivot_wider(df, names_from = key, values_from = val, names_repair = "unique")
Message <simpleMessage>
  New names:
  * a -> a...1
  * a -> a...2

names_from must be supplied if name isn't in data (#1240)

Code
  (expect_error(pivot_wider(df, values_from = val)))
Output
  <error/vctrs_error_subscript_oob>
  Can't subset columns that don't exist.
  x Column `name` doesn't exist.

values_from must be supplied if value isn't in data (#1240)

Code
  (expect_error(pivot_wider(df, names_from = key)))
Output
  <error/vctrs_error_subscript_oob>
  Can't subset columns that don't exist.
  x Column `value` doesn't exist.

names_from must identify at least 1 column (#1240)

Code
  (expect_error(pivot_wider(df, names_from = starts_with("foo"), values_from = val))
  )
Output
  <error/rlang_error>
  `names_from` must select at least one column.

values_from must identify at least 1 column (#1240)

Code
  (expect_error(pivot_wider(df, names_from = key, values_from = starts_with("foo")))
  )
Output
  <error/rlang_error>
  `values_from` must select at least one column.

values_fn emits an informative error when it doesn't result in unique values (#1238)

Code
  (expect_error(pivot_wider(df, values_fn = list(value = ~.x))))
Output
  <error/rlang_error>
  Applying `values_fn` to `value` must result in a single summary value per key.
  x Applying `values_fn` resulted in a value with length 2.

names_vary is validated

Code
  (expect_error(build_wider_spec(df, names_vary = 1)))
Output
  <error/rlang_error>
  `names_vary` must be a character vector.
Code
  (expect_error(build_wider_spec(df, names_vary = "x")))
Output
  <error/rlang_error>
  `names_vary` must be one of "fastest" or "slowest".

duplicated keys produce list column with warning

Code
  pv <- pivot_wider(df, names_from = key, values_from = val)
Warning <warning>
  Values from `val` are not uniquely identified; output will contain list-cols.
  * Use `values_fn = list` to suppress this warning.
  * Use `values_fn = {summary_fun}` to summarise duplicates.
  * Use the following dplyr code to identify duplicates.
    {data} %>%
      dplyr::group_by(a, key) %>%
      dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
      dplyr::filter(n > 1L)

duplicated key warning mentions every applicable column

Code
  pivot_wider(df, names_from = key, values_from = c(a, b, c))
Warning <warning>
  Values from `a`, `b` and `c` are not uniquely identified; output will contain list-cols.
  * Use `values_fn = list` to suppress this warning.
  * Use `values_fn = {summary_fun}` to summarise duplicates.
  * Use the following dplyr code to identify duplicates.
    {data} %>%
      dplyr::group_by(key) %>%
      dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
      dplyr::filter(n > 1L)
Output
  # A tibble: 1 x 3
    a_x       b_x       c_x      
    <list>    <list>    <list>   
  1 <dbl [2]> <dbl [2]> <dbl [2]>

Code
  pivot_wider(df, names_from = key, values_from = c(a, b, c), values_fn = list(b = sum))
Warning <warning>
  Values from `a` and `c` are not uniquely identified; output will contain list-cols.
  * Use `values_fn = list` to suppress this warning.
  * Use `values_fn = {summary_fun}` to summarise duplicates.
  * Use the following dplyr code to identify duplicates.
    {data} %>%
      dplyr::group_by(key) %>%
      dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
      dplyr::filter(n > 1L)
Output
  # A tibble: 1 x 3
    a_x         b_x c_x      
    <list>    <dbl> <list>   
  1 <dbl [2]>     7 <dbl [2]>

duplicated key warning backticks non-syntactic names

Code
  pv <- pivot_wider(df, names_from = `the-key`, values_from = val)
Warning <warning>
  Values from `val` are not uniquely identified; output will contain list-cols.
  * Use `values_fn = list` to suppress this warning.
  * Use `values_fn = {summary_fun}` to summarise duplicates.
  * Use the following dplyr code to identify duplicates.
    {data} %>%
      dplyr::group_by(`a 1`, a2, `the-key`) %>%
      dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
      dplyr::filter(n > 1L)

values_fn is validated

Code
  (expect_error(pivot_wider(df, values_fn = 1)))
Output
  <error/rlang_error>
  Can't convert a double vector to function