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Expand a CoverageExperiment object

Source: R/expand.R
expand-CoverageExperiment.Rd

A CoverageExperiment object can be coerced into a tibble using the tidySummarizedExperiment package, but this will not turn each coverage matrix into a "long" format. The expand function provided here allows one to coerce a CoverageExperiment object into a long data frame, and adds the ranges and seqnames to the resulting tibble.

Usage

# S3 method for class 'CoverageExperiment'
expand(data, ..., .name_repair = NULL)

Arguments

data

A data frame.

...

<data-masking> Specification of columns to expand or complete. Columns can be atomic vectors or lists.

  • To find all unique combinations of x, y and z, including those not present in the data, supply each variable as a separate argument: expand(df, x, y, z) or complete(df, x, y, z).

  • To find only the combinations that occur in the data, use nesting: expand(df, nesting(x, y, z)).

  • You can combine the two forms. For example, expand(df, nesting(school_id, student_id), date) would produce a row for each present school-student combination for all possible dates.

When used with factors, expand() and complete() use the full set of levels, not just those that appear in the data. If you want to use only the values seen in the data, use forcats::fct_drop().

When used with continuous variables, you may need to fill in values that do not appear in the data: to do so use expressions like year = 2010:2020 or year = full_seq(year,1).

.name_repair

One of "check_unique", "unique", "universal", "minimal", "unique_quiet", or "universal_quiet". See vec_as_names() for the meaning of these options.

Value

a tibble object

Grouped data frames

With grouped data frames created by dplyr::group_by(), expand() operates within each group. Because of this, you cannot expand on a grouping column.

See also

complete() to expand list objects. expand_grid() to input vectors rather than a data frame.

Examples

data(ce)
ce
#> class: CoverageExperiment 
#> dim: 1 2 
#> metadata(0):
#> assays(1): coverage
#> rownames(1): Scc1
#> rowData names(2): features n
#> colnames(2): RNA_fwd RNA_rev
#> colData names(1): track
#> width: 3000
expand(ce)
#> # A tibble: 368,400 × 8
#> # Groups:   track, features, ranges [1,228]
#>    track   features chr   ranges         strand coord coverage coord.scaled
#>    <chr>   <fct>    <chr> <chr>          <chr>  <dbl>    <dbl>        <dbl>
#>  1 RNA_fwd Scc1     II    II:4290-7289:+ +       4290   -0.257        -1500
#>  2 RNA_fwd Scc1     II    II:4290-7289:+ +       4300   -0.257        -1490
#>  3 RNA_fwd Scc1     II    II:4290-7289:+ +       4310   -0.257        -1480
#>  4 RNA_fwd Scc1     II    II:4290-7289:+ +       4320   -0.257        -1470
#>  5 RNA_fwd Scc1     II    II:4290-7289:+ +       4330   -0.257        -1460
#>  6 RNA_fwd Scc1     II    II:4290-7289:+ +       4340   -0.257        -1450
#>  7 RNA_fwd Scc1     II    II:4290-7289:+ +       4350   -0.257        -1440
#>  8 RNA_fwd Scc1     II    II:4290-7289:+ +       4360   -0.257        -1430
#>  9 RNA_fwd Scc1     II    II:4290-7289:+ +       4370   -0.257        -1420
#> 10 RNA_fwd Scc1     II    II:4290-7289:+ +       4380   -0.257        -1410
#> # ℹ 368,390 more rows