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Summarize each group in a ir object to fewer rows

Source: R/tidyverse.R
summarize.Rd

Summarize each group in a ir object to fewer rows

Usage

summarize.ir(.data, ..., .groups = NULL)

summarise.ir(.data, ..., .groups = NULL)

Source

dplyr::summarize()

Arguments

.data

An object of class ir.

...

<data-masking> Name-value pairs of summary functions. The name will be the name of the variable in the result.

The value can be:

  • A vector of length 1, e.g. min(x), n(), or sum(is.na(y)).

  • A vector of length n, e.g. quantile().

  • A data frame, to add multiple columns from a single expression.

.groups

[Experimental] Grouping structure of the result.

  • "drop_last": dropping the last level of grouping. This was the only supported option before version 1.0.0.

  • "drop": All levels of grouping are dropped.

  • "keep": Same grouping structure as .data.

  • "rowwise": Each row is its own group.

When .groups is not specified, it is chosen based on the number of rows of the results:

  • If all the results have 1 row, you get "drop_last".

  • If the number of rows varies, you get "keep".

In addition, a message informs you of that choice, unless the result is ungrouped, the option "dplyr.summarise.inform" is set to FALSE, or when summarise() is called from a function in a package.

Value

.data with summarized 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.

See also

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

Examples

## summarize

# select in each sample_type groups the first spectrum
ir_sample_data %>%
  dplyr::group_by(sample_type) %>%
  dplyr::summarize(spectra = spectra[[1]])
#> `summarise()` has grouped output by 'sample_type'. You can override using the
#> `.groups` argument.
#> # A tibble: 26,808 × 2
#> # Groups:   sample_type [8]
#>    sample_type spectra$x        $y
#>  * <chr>           <int>     <dbl>
#>  1 hardwood         4000 0.000193 
#>  2 hardwood         3999 0.000214 
#>  3 hardwood         3998 0.000179 
#>  4 hardwood         3997 0.000106 
#>  5 hardwood         3996 0.0000386
#>  6 hardwood         3995 0.0000354
#>  7 hardwood         3994 0.000102 
#>  8 hardwood         3993 0.000210 
#>  9 hardwood         3992 0.000294 
#> 10 hardwood         3991 0.000312 
#> # … with 26,798 more rows