Pivot data from wide to long using a spec — pivot_longer

This is a low level interface to pivoting, inspired by the cdata package, that allows you to describe pivoting with a data frame.

Usage

pivot_longer_spec(
  data,
  spec,
  ...,
  cols_vary = "fastest",
  names_repair = "check_unique",
  values_drop_na = FALSE,
  values_ptypes = NULL,
  values_transform = NULL,
  error_call = current_env()
)

build_longer_spec(
  data,
  cols,
  ...,
  names_to = "name",
  values_to = "value",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = NULL,
  names_transform = NULL,
  error_call = current_env()
)

Arguments

data

A data frame to pivot.

spec

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

...

These dots are for future extensions and must be empty.

cols_vary

When pivoting cols into longer format, how should the output rows be arranged relative to their original row number?

"fastest", the default, keeps individual rows from cols close together in the output. This often produces intuitively ordered output when you have at least one key column from data that is not involved in the pivoting process.
"slowest" keeps individual columns from cols close together in the output. This often produces intuitively ordered output when you utilize all of the columns from data in the pivoting process.

names_repair

What happens if the output has invalid column names? The default, "check_unique" is to error if the columns are duplicated. Use "minimal" to allow duplicates in the output, or "unique" to de-duplicated by adding numeric suffixes. See vctrs::vec_as_names() for more options.

values_drop_na

If TRUE, will drop rows that contain only NAs in the value_to column. This effectively converts explicit missing values to implicit missing values, and should generally be used only when missing values in data were created by its structure.

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.

cols

<tidy-select> Columns to pivot into longer format.

names_to

A character vector specifying the new column or columns to create from the information stored in the column names of data specified by cols.

If length 0, or if NULL is supplied, no columns will be created.
If length 1, a single column will be created which will contain the column names specified by cols.
If length >1, multiple columns will be created. In this case, one of names_sep or names_pattern must be supplied to specify how the column names should be split. There are also two additional character values you can take advantage of:
- NA will discard the corresponding component of the column name.
- ".value" indicates that the corresponding component of the column name defines the name of the output column containing the cell values, overriding values_to entirely.

values_to

A string specifying the name of the column to create from the data stored in cell values. If names_to is a character containing the special .value sentinel, this value will be ignored, and the name of the value column will be derived from part of the existing column names.

names_prefix

A regular expression used to remove matching text from the start of each variable name.

names_sep, names_pattern

If names_to contains multiple values, these arguments control how the column name is broken up.

names_sep takes the same specification as separate(), and can either be a numeric vector (specifying positions to break on), or a single string (specifying a regular expression to split on).

names_pattern takes the same specification as extract(), a regular expression containing matching groups (()).

If these arguments do not give you enough control, use pivot_longer_spec() to create a spec object and process manually as needed.

names_ptypes, values_ptypes

Optionally, a list of column name-prototype pairs. Alternatively, a single empty prototype can be supplied, which will be applied to all columns. A prototype (or ptype for short) is a zero-length vector (like integer() or numeric()) that defines the type, class, and attributes of a vector. Use these arguments if you want to confirm that the created columns are the types that you expect. Note that if you want to change (instead of confirm) the types of specific columns, you should use names_transform or values_transform instead.

names_transform, values_transform

Optionally, a list of column name-function pairs. Alternatively, a single function can be supplied, which will be applied to all columns. Use these arguments if you need to change the types of specific columns. For example, names_transform = list(week = as.integer) would convert a character variable called week to an integer.

If not specified, the type of the columns generated from names_to will be character, and the type of the variables generated from values_to will be the common type of the input columns used to generate them.

Examples

# See vignette("pivot") for examples and explanation

# Use `build_longer_spec()` to build `spec` using similar syntax to `pivot_longer()`
# and run `pivot_longer_spec()` based on `spec`.
spec <- relig_income %>% build_longer_spec(
  cols = !religion,
  names_to = "income",
  values_to = "count"
)
spec
#> # A tibble: 10 × 3
#>    .name              .value income            
#>    <chr>              <chr>  <chr>             
#>  1 <$10k              count  <$10k             
#>  2 $10-20k            count  $10-20k           
#>  3 $20-30k            count  $20-30k           
#>  4 $30-40k            count  $30-40k           
#>  5 $40-50k            count  $40-50k           
#>  6 $50-75k            count  $50-75k           
#>  7 $75-100k           count  $75-100k          
#>  8 $100-150k          count  $100-150k         
#>  9 >150k              count  >150k             
#> 10 Don't know/refused count  Don't know/refused

pivot_longer_spec(relig_income, spec)
#> # A tibble: 180 × 3
#>    religion income             count
#>    <chr>    <chr>              <dbl>
#>  1 Agnostic <$10k                 27
#>  2 Agnostic $10-20k               34
#>  3 Agnostic $20-30k               60
#>  4 Agnostic $30-40k               81
#>  5 Agnostic $40-50k               76
#>  6 Agnostic $50-75k              137
#>  7 Agnostic $75-100k             122
#>  8 Agnostic $100-150k            109
#>  9 Agnostic >150k                 84
#> 10 Agnostic Don't know/refused    96
#> # ℹ 170 more rows

# Is equivalent to:
relig_income %>% pivot_longer(
  cols = !religion,
  names_to = "income",
  values_to = "count"
)
#> # A tibble: 180 × 3
#>    religion income             count
#>    <chr>    <chr>              <dbl>
#>  1 Agnostic <$10k                 27
#>  2 Agnostic $10-20k               34
#>  3 Agnostic $20-30k               60
#>  4 Agnostic $30-40k               81
#>  5 Agnostic $40-50k               76
#>  6 Agnostic $50-75k              137
#>  7 Agnostic $75-100k             122
#>  8 Agnostic $100-150k            109
#>  9 Agnostic >150k                 84
#> 10 Agnostic Don't know/refused    96
#> # ℹ 170 more rows