Pivot an ir object from wide to long

Pivot an ir object from wide to long

Usage

pivot_longer.ir(
  data,
  cols,
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = list(),
  names_transform = list(),
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = list(),
  values_transform = list(),
  ...
)

Source

tidyr::pivot_longer()

Arguments

data

An object of class ir.

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.

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.

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_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.

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.

...

Additional arguments passed on to methods.

Value

data in a long format. If the spectra column is dropped or invalidated (see ir_new_ir()), the ir class is dropped, else the object is of class ir.

See also

Other tidyverse: arrange.ir(), distinct.ir(), extract.ir(), filter-joins, filter.ir(), group_by, mutate, mutate-joins, nest, pivot_wider.ir(), rename, rowwise.ir(), select.ir(), separate.ir(), separate_rows.ir(), slice, summarize, unite.ir()

Examples

## pivot_longer
ir_sample_data |>
  tidyr::pivot_longer(
    cols = dplyr::any_of(c("holocellulose", "klason_lignin"))
  )
#> # A tibble: 116 × 7
#>    id_measurement id_sample sample_type sample_comment      spectra  name  value
#>  *          <int> <chr>     <chr>       <chr>               <named > <chr>   [1]
#>  1              1 GN 11-389 needles     Abies Firma Momi f… <tibble> holo… 0.308
#>  2              1 GN 11-389 needles     Abies Firma Momi f… <tibble> klas… 0.360
#>  3              2 GN 11-400 needles     Cupressocyparis le… <tibble> holo… 0.250
#>  4              2 GN 11-400 needles     Cupressocyparis le… <tibble> klas… 0.339
#>  5              3 GN 11-407 needles     Juniperus chinensi… <tibble> holo… 0.336
#>  6              3 GN 11-407 needles     Juniperus chinensi… <tibble> klas… 0.268
#>  7              4 GN 11-411 needles     Metasequoia glypto… <tibble> holo… 0.184
#>  8              4 GN 11-411 needles     Metasequoia glypto… <tibble> klas… 0.350
#>  9              5 GN 11-416 needles     Pinus strobus Toru… <tibble> holo… 0.309
#> 10              5 GN 11-416 needles     Pinus strobus Toru… <tibble> klas… 0.331
#> # ℹ 106 more rows