Import window based statistics

Source: R/hypogen_import.R

hypo_import_windows imports window based statistics (such as VCFtools output).

hypo_import_windows(file, gz = FALSE, run, ...)

Arguments

file

string scalar (mandatory), the input file
gz

logical scalar (optional), is the input file gz compressed?
run

string scalar (optional), appends a column RUN as ID if several files should be merged later
...

parameters passed to vroom::vroom()

Details

This function imports window based statistics. The input is expected to be tab separated, to contain a CHROM, BIN_START and BIN END column and can optionally be gz compressed. After import, the CHROM based position (center of the window) is transposed to a genome wide position for continuous visualization.

See also

hypo_import_snps

Examples


file_windowed <- system.file("extdata", "example.windowed.weir.fst.gz", package = "hypogen")

hypo_import_windows(file = file_windowed, gz = TRUE)

#> Rows: 49
#> Columns: 6
#> Delimiter: "\t"
#> chr [1]: CHROM
#> dbl [5]: BIN_START, BIN_END, N_VARIANTS, WEIGHTED_FST, MEAN_FST
#> 
#> Use `spec()` to retrieve the guessed column specification
#> Pass a specification to the `col_types` argument to quiet this message
#> # A tibble: 49 x 9
#>    CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST MEAN_FST GSTART    POS   GPOS
#>    <chr>     <dbl>   <dbl>      <dbl>        <dbl>    <dbl>  <dbl>  <dbl>  <dbl>
#>  1 LG01          1   10000         24     -0.00552 -0.00422      0  5000.  5000.
#>  2 LG01       1001   11000         27     -0.00577 -0.00454      0  6000.  6000.
#>  3 LG01       2001   12000         22     -0.00649 -0.00363      0  7000.  7000.
#>  4 LG01       3001   13000         22     -0.00649 -0.00363      0  8000.  8000.
#>  5 LG01       4001   14000         18     -0.00797 -0.00710      0  9000.  9000.
#>  6 LG01       5001   15000         14     -0.00962 -0.00828      0 10000. 10000.
#>  7 LG01       6001   16000         12     -0.0101  -0.00850      0 11000. 11000.
#>  8 LG01       7001   17000         12     -0.0101  -0.00850      0 12000. 12000.
#>  9 LG01       8001   18000         15     -0.00890 -0.00402      0 13000. 13000.
#> 10 LG01       9001   19000         16     -0.00882 -0.00418      0 14000. 14000.
#> # … with 39 more rows