Group rows in ir objects by one or more variables

Group rows in ir objects by one or more variables

Usage

group_by.ir(
  .data,
  ...,
  .add = FALSE,
  .drop = dplyr::group_by_drop_default(.data)
)

ungroup.ir(.data, ...)

Source

dplyr::group_by()

Arguments

.data

An object of class ir.

...

In group_by(), variables or computations to group by. Computations are always done on the ungrouped data frame. To perform computations on the grouped data, you need to use a separate mutate() step before the group_by(). Computations are not allowed in nest_by(). In ungroup(), variables to remove from the grouping.

.add

When FALSE, the default, group_by() will override existing groups. To add to the existing groups, use .add = TRUE.

This argument was previously called add, but that prevented creating a new grouping variable called add, and conflicts with our naming conventions.

.drop

Drop groups formed by factor levels that don't appear in the data? The default is TRUE except when .data has been previously grouped with .drop = FALSE. See group_by_drop_default() for details.

Value

.data with grouped rows (group_by.ir()) or ungrouped rows (ungroup.ir()).

See also

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

Examples

## group_by
dplyr::group_by(ir_sample_data, sample_type)
#> # A tibble: 58 × 7
#> # Groups:   sample_type [8]
#>    id_measurement id_sample sample_type sample_comment             klason_lignin
#>  *          <int> <chr>     <chr>       <chr>                      <units>      
#>  1              1 GN 11-389 needles     Abies Firma Momi fir       0.359944     
#>  2              2 GN 11-400 needles     Cupressocyparis leylandii… 0.339405     
#>  3              3 GN 11-407 needles     Juniperus chinensis Chine… 0.267552     
#>  4              4 GN 11-411 needles     Metasequoia glyptostroboi… 0.350016     
#>  5              5 GN 11-416 needles     Pinus strobus Torulosa     0.331100     
#>  6              6 GN 11-419 needles     Pseudolarix amabili Golde… 0.279360     
#>  7              7 GN 11-422 needles     Sequoia sempervirens Cali… 0.329672     
#>  8              8 GN 11-423 needles     Taxodium distichum Cascad… 0.356950     
#>  9              9 GN 11-428 needles     Thuja occidentalis Easter… 0.369360     
#> 10             10 GN 11-434 needles     Tsuga caroliniana Carolin… 0.289050     
#> # … with 48 more rows, and 2 more variables: holocellulose <units>,
#> #   spectra <named list>


## ungroup
dplyr::ungroup(dplyr::group_by(ir_sample_data, sample_type))
#> # A tibble: 58 × 7
#>    id_measurement id_sample sample_type sample_comment             klason_lignin
#>  *          <int> <chr>     <chr>       <chr>                      <units>      
#>  1              1 GN 11-389 needles     Abies Firma Momi fir       0.359944     
#>  2              2 GN 11-400 needles     Cupressocyparis leylandii… 0.339405     
#>  3              3 GN 11-407 needles     Juniperus chinensis Chine… 0.267552     
#>  4              4 GN 11-411 needles     Metasequoia glyptostroboi… 0.350016     
#>  5              5 GN 11-416 needles     Pinus strobus Torulosa     0.331100     
#>  6              6 GN 11-419 needles     Pseudolarix amabili Golde… 0.279360     
#>  7              7 GN 11-422 needles     Sequoia sempervirens Cali… 0.329672     
#>  8              8 GN 11-423 needles     Taxodium distichum Cascad… 0.356950     
#>  9              9 GN 11-428 needles     Thuja occidentalis Easter… 0.369360     
#> 10             10 GN 11-434 needles     Tsuga caroliniana Carolin… 0.289050     
#> # … with 48 more rows, and 2 more variables: holocellulose <units>,
#> #   spectra <named list>