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Coerce an CoverageExperiment or AggregatedCoverage object into a tibble

Usage

# S3 method for class 'AggregatedCoverage'
as_tibble(x, ...)

Arguments

x

A data frame, list, matrix, or other object that could reasonably be coerced to a tibble.

...

Unused, for extensibility.

Value

tibble

Row names

The default behavior is to silently remove row names.

New code should explicitly convert row names to a new column using the rownames argument.

For existing code that relies on the retention of row names, call pkgconfig::set_config("tibble::rownames" = NA) in your script or in your package's .onLoad() function.

Life cycle

Using as_tibble() for vectors is superseded as of version 3.0.0, prefer the more expressive as_tibble_row() and as_tibble_col() variants for new code.

See also

tibble() constructs a tibble from individual columns. enframe() converts a named vector to a tibble with a column of names and column of values. Name repair is implemented using vctrs::vec_as_names().

Examples

data(ac)
as_tibble(ac)
#> # A tibble: 6,000 × 13
#>    .sample .feature track features coord    mean median   min   max    sd     se
#>    <chr>   <fct>    <chr> <fct>    <dbl>   <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
#>  1 RNA_fwd Scc1     RNA_… Scc1     -1500 -0.0921 -0.611 -3.25  10.5  1.42 0.0572
#>  2 RNA_fwd Scc1     RNA_… Scc1     -1499 -0.0915 -0.609 -3.25  10.5  1.42 0.0572
#>  3 RNA_fwd Scc1     RNA_… Scc1     -1498 -0.0898 -0.609 -3.25  10.5  1.42 0.0573
#>  4 RNA_fwd Scc1     RNA_… Scc1     -1497 -0.0914 -0.609 -3.25  10.5  1.42 0.0573
#>  5 RNA_fwd Scc1     RNA_… Scc1     -1496 -0.0915 -0.609 -3.25  10.5  1.42 0.0573
#>  6 RNA_fwd Scc1     RNA_… Scc1     -1495 -0.0912 -0.609 -3.25  10.5  1.42 0.0572
#>  7 RNA_fwd Scc1     RNA_… Scc1     -1494 -0.0912 -0.609 -3.25  10.5  1.42 0.0572
#>  8 RNA_fwd Scc1     RNA_… Scc1     -1493 -0.0915 -0.609 -3.25  10.5  1.42 0.0572
#>  9 RNA_fwd Scc1     RNA_… Scc1     -1492 -0.0907 -0.609 -3.25  10.5  1.42 0.0573
#> 10 RNA_fwd Scc1     RNA_… Scc1     -1491 -0.0903 -0.609 -3.25  10.5  1.42 0.0572
#> # ℹ 5,990 more rows
#> # ℹ 2 more variables: ci_low <dbl>, ci_high <dbl>