Nest and un-nest an ir object

Nest and un-nest an ir object

Usage

nest.ir(.data, ..., .names_sep = NULL, .key = deprecated())

unnest.ir(
  data,
  cols,
  ...,
  keep_empty = FALSE,
  ptype = NULL,
  names_sep = NULL,
  names_repair = "check_unique",
  .drop = deprecated(),
  .id = deprecated(),
  .sep = deprecated(),
  .preserve = deprecated()
)

Source

tidyr::nest()

Arguments

.data

An object of class ir.

...

<tidy-select> Columns to nest, specified using name-variable pairs of the form new_col = c(col1, col2, col3). The right hand side can be any valid tidy select expression.

: previously you could write df %>% nest(x, y, z) and df %>% unnest(x, y, z). Convert to df %>% nest(data = c(x, y, z)). and df %>% unnest(c(x, y, z)).

If you previously created new variable in unnest() you'll now need to do it explicitly with mutate(). Convert df %>% unnest(y = fun(x, y, z)) to df %>% mutate(y = fun(x, y, z)) %>% unnest(y).

.key

: No longer needed because of the new new_col = c(col1, col2, col3) syntax.

data

A data frame.

cols

<tidy-select> Columns to unnest.

If you unnest() multiple columns, parallel entries must be of compatible sizes, i.e. they're either equal or length 1 (following the standard tidyverse recycling rules).

keep_empty

By default, you get one row of output for each element of the list your unchopping/unnesting. This means that if there's a size-0 element (like NULL or an empty data frame), that entire row will be dropped from the output. If you want to preserve all rows, use keep_empty = TRUE to replace size-0 elements with a single row of missing values.

ptype

Optionally, a named list of column name-prototype pairs to coerce cols to, overriding the default that will be guessed from combining the individual values. Alternatively, a single empty ptype can be supplied, which will be applied to all cols.

names_sep, .names_sep

If NULL, the default, the names will be left as is. In nest(), inner names will come from the former outer names; in unnest(), the new outer names will come from the inner names.

If a string, the inner and outer names will be used together. In unnest(), the names of the new outer columns will be formed by pasting together the outer and the inner column names, separated by names_sep. In nest(), the new inner names will have the outer names + names_sep automatically stripped. This makes names_sep roughly symmetric between nesting and unnesting.

names_repair

Used to check that output data frame has valid names. Must be one of the following options:

"minimal": no name repair or checks, beyond basic existence,
"unique": make sure names are unique and not empty,
"check_unique": (the default), no name repair, but check they are unique,
"universal": make the names unique and syntactic
a function: apply custom name repair.
tidyr_legacy: use the name repair from tidyr 0.8.
a formula: a purrr-style anonymous function (see rlang::as_function())

See vctrs::vec_as_names() for more details on these terms and the strategies used to enforce them.

.drop, .preserve

: all list-columns are now preserved; If there are any that you don't want in the output use select() to remove them prior to unnesting.

.id

: convert df %>% unnest(x, .id = "id") to df %>% mutate(id = names(x)) %>% unnest(x)).

.sep

: use names_sep instead.

Value

.data with nested or unnested columns. 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

## nest
ir_sample_data %>%
  tidyr::nest(
    contents = c(holocellulose, klason_lignin)
  )
#> # A tibble: 58 × 6
#>    id_measurement id_sample sample_type sample_comment         spectra  contents
#>  *          <int> <chr>     <chr>       <chr>                  <named > <list>  
#>  1              1 GN 11-389 needles     Abies Firma Momi fir   <tibble> <tibble>
#>  2              2 GN 11-400 needles     Cupressocyparis leyla… <tibble> <tibble>
#>  3              3 GN 11-407 needles     Juniperus chinensis C… <tibble> <tibble>
#>  4              4 GN 11-411 needles     Metasequoia glyptostr… <tibble> <tibble>
#>  5              5 GN 11-416 needles     Pinus strobus Torulosa <tibble> <tibble>
#>  6              6 GN 11-419 needles     Pseudolarix amabili G… <tibble> <tibble>
#>  7              7 GN 11-422 needles     Sequoia sempervirens … <tibble> <tibble>
#>  8              8 GN 11-423 needles     Taxodium distichum Ca… <tibble> <tibble>
#>  9              9 GN 11-428 needles     Thuja occidentalis Ea… <tibble> <tibble>
#> 10             10 GN 11-434 needles     Tsuga caroliniana Car… <tibble> <tibble>
#> # … with 48 more rows


## unnest
ir_sample_data %>%
  tidyr::nest(
    contents = c(holocellulose, klason_lignin)
  ) %>%
  tidyr::unnest("contents")
#> # 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]

Nest and un-nest an `ir` object

Usage

Source

Arguments

Value

See also

Examples