Pivot an ir object from wide to long — pivot

Pivot an ir object from wide to long

Usage

pivot_wider.ir(
  data,
  id_cols = NULL,
  names_from = "name",
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_repair = "check_unique",
  values_from = "value",
  values_fill = NULL,
  values_fn = NULL,
  ...
)

Source

tidyr::pivot_wider()

Arguments

data

An object of class ir.

id_cols

<tidy-select> A set of columns that uniquely identifies each observation. Defaults to all columns in data except for the columns specified in names_from and values_from. Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.

names_from, values_from

<tidy-select> 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. This is particularly useful if names_from is a numeric vector and you want to create syntactic variable names.

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_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_fill

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

This can be a named list if you want to apply different fill values to different value columns.

values_fn

Optionally, a function applied to the value in each cell in the output. You will typically use this when the combination of id_cols and names_from columns does not uniquely identify an observation.

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

...

Additional arguments passed on to methods.

Value

data in a wide 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.

Examples

## pivot_wider
ir_sample_data %>%
  tidyr::pivot_longer(
    cols = dplyr::any_of(c("holocellulose", "klason_lignin"))
  ) %>%
  tidyr::pivot_wider(names_from = "name", values_from = "value")
#> # A tibble: 58 × 7
#>    id_measurement id_sample sample_type sample_comment    spectra  holocellulose
#>             <int> <chr>     <chr>       <chr>             <named >           [1]
#>  1              1 GN 11-389 needles     Abies Firma Momi… <tibble>         0.308
#>  2              2 GN 11-400 needles     Cupressocyparis … <tibble>         0.250
#>  3              3 GN 11-407 needles     Juniperus chinen… <tibble>         0.336
#>  4              4 GN 11-411 needles     Metasequoia glyp… <tibble>         0.184
#>  5              5 GN 11-416 needles     Pinus strobus To… <tibble>         0.309
#>  6              6 GN 11-419 needles     Pseudolarix amab… <tibble>         0.335
#>  7              7 GN 11-422 needles     Sequoia sempervi… <tibble>         0.241
#>  8              8 GN 11-423 needles     Taxodium distich… <tibble>         0.125
#>  9              9 GN 11-428 needles     Thuja occidental… <tibble>         0.252
#> 10             10 GN 11-434 needles     Tsuga carolinian… <tibble>         0.349
#> # … with 48 more rows, and 1 more variable: klason_lignin [1]

Pivot an `ir` object from wide to long

Usage

Source

Arguments

Value

See also

Examples