Compute relative distances between intervals.

Usage

bed_reldist(x, y, detail = FALSE)

Arguments

x: ivl_df
y: ivl_df
detail: report relative distances for each x interval.

Value

If detail = FALSE, a ivl_df that summarizes calculated .reldist values with the following columns:

.reldist relative distance metric
.counts number of metric observations
.total total observations
.freq frequency of observation

If detail = TRUE, the .reldist column reports the relative distance for each input x interval.

Details

Interval statistics can be used in combination with dplyr::group_by() and dplyr::do() to calculate statistics for subsets of data. See vignette('interval-stats') for examples.

Examples

genome <- read_genome(valr_example("hg19.chrom.sizes.gz"))

x <- bed_random(genome, seed = 1010486)
y <- bed_random(genome, seed = 9203911)

bed_reldist(x, y)
#> # A tibble: 51 × 4
#>    .reldist .counts .total  .freq
#>       <dbl>   <int>  <int>  <dbl>
#>  1     0      20202 999938 0.0202
#>  2     0.01   20031 999938 0.0200
#>  3     0.02   19977 999938 0.0200
#>  4     0.03   19871 999938 0.0199
#>  5     0.04   20129 999938 0.0201
#>  6     0.05   20197 999938 0.0202
#>  7     0.06   20020 999938 0.0200
#>  8     0.07   20063 999938 0.0201
#>  9     0.08   20053 999938 0.0201
#> 10     0.09   20021 999938 0.0200
#> # ℹ 41 more rows

bed_reldist(x, y, detail = TRUE)
#> # A tibble: 999,938 × 4
#>    chrom start   end .reldist
#>    <chr> <int> <int>    <dbl>
#>  1 chr1   5184  6184   0.270 
#>  2 chr1   7663  8663   0.226 
#>  3 chr1   9858 10858   0.317 
#>  4 chr1  13805 14805   0.361 
#>  5 chr1  14081 15081   0.402 
#>  6 chr1  16398 17398   0.253 
#>  7 chr1  17486 18486   0.0912
#>  8 chr1  22063 23063   0.107 
#>  9 chr1  22494 23494   0.207 
#> 10 chr1  29351 30351   0.400 
#> # ℹ 999,928 more rows