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Estimate missingness at each locus

Source: R/loci_missingness.R
loci_missingness.Rd

Estimate the rate of missingness at each locus. This function has an efficient method to support grouped gen_tibble objects, which can return a tidied tibble, a list, or a matrix.

Usage

loci_missingness(
  .x,
  .col = "genotypes",
  as_counts = FALSE,
  n_cores = bigstatsr::nb_cores(),
  block_size,
  type,
  ...
)

# S3 method for class 'tbl_df'
loci_missingness(
  .x,
  .col = "genotypes",
  as_counts = FALSE,
  n_cores = n_cores,
  block_size = bigstatsr::block_size(nrow(.x), 1),
  ...
)

# S3 method for class 'vctrs_bigSNP'
loci_missingness(
  .x,
  .col = "genotypes",
  as_counts = FALSE,
  n_cores = n_cores,
  block_size = bigstatsr::block_size(length(.x), 1),
  ...
)

# S3 method for class 'grouped_df'
loci_missingness(
  .x,
  .col = "genotypes",
  as_counts = FALSE,
  n_cores = bigstatsr::nb_cores(),
  block_size = bigstatsr::block_size(nrow(.x), 1),
  type = c("tidy", "list", "matrix"),
  ...
)

Arguments

.x

a vector of class vctrs_bigSNP (usually the genotypes column of a gen_tibble object), or a gen_tibble.

.col

the column to be used when a tibble (or grouped tibble is passed directly to the function). This defaults to "genotypes" and can only take that value. There is no need for the user to set it, but it is included to resolve certain tidyselect operations.

as_counts

boolean defining whether the count of NAs (rather than the rate) should be returned. It defaults to FALSE (i.e. rates are returned by default).

n_cores

number of cores to be used, it defaults to bigstatsr::nb_cores()

block_size

maximum number of loci read at once.

type

type of object to return, if using grouped method. One of "tidy", "list", or "matrix". Default is "tidy".

...

other arguments passed to specific methods.

Value

a vector of frequencies, one per locus

Examples

example_gt <- example_gt("gen_tbl")

# For missingness
example_gt %>% loci_missingness()
#> [1] 0.0000000 0.1428571 0.0000000 0.1428571 0.1428571 0.1428571

# For missingness per locus per population
example_gt %>%
  group_by(population) %>%
  loci_missingness()
#> # A tibble: 18 × 3
#>    loci  group value
#>    <chr> <chr> <dbl>
#>  1 rs1   pop1  0    
#>  2 rs1   pop2  0    
#>  3 rs1   pop3  0    
#>  4 rs2   pop1  0    
#>  5 rs2   pop2  0.5  
#>  6 rs2   pop3  0    
#>  7 rs3   pop1  0    
#>  8 rs3   pop2  0    
#>  9 rs3   pop3  0    
#> 10 rs4   pop1  0.333
#> 11 rs4   pop2  0    
#> 12 rs4   pop3  0    
#> 13 rs5   pop1  0    
#> 14 rs5   pop2  0    
#> 15 rs5   pop3  0.5  
#> 16 rs6   pop1  0    
#> 17 rs6   pop2  0    
#> 18 rs6   pop3  0.5  
# alternatively, return a list of populations with their missingness
example_gt %>%
  group_by(population) %>%
  loci_missingness(type = "list")
#> [[1]]
#> [1] 0.0000000 0.0000000 0.0000000 0.3333333 0.0000000 0.0000000
#> 
#> [[2]]
#> [1] 0.0 0.5 0.0 0.0 0.0 0.0
#> 
#> [[3]]
#> [1] 0.0 0.0 0.0 0.0 0.5 0.5
#> 
# or a matrix with populations in columns and loci in rows
example_gt %>%
  group_by(population) %>%
  loci_missingness(type = "matrix")
#>          pop1 pop2 pop3
#> rs1 0.0000000  0.0  0.0
#> rs2 0.0000000  0.5  0.0
#> rs3 0.0000000  0.0  0.0
#> rs4 0.3333333  0.0  0.0
#> rs5 0.0000000  0.0  0.5
#> rs6 0.0000000  0.0  0.5
# or within reframe (not recommended, as it much less efficient
# than using it directly as shown above)
example_gt %>%
  group_by(population) %>%
  reframe(missing = loci_missingness(genotypes))
#> # A tibble: 18 × 2
#>    population missing
#>    <chr>        <dbl>
#>  1 pop1         0    
#>  2 pop1         0    
#>  3 pop1         0    
#>  4 pop1         0.333
#>  5 pop1         0    
#>  6 pop1         0    
#>  7 pop2         0    
#>  8 pop2         0.5  
#>  9 pop2         0    
#> 10 pop2         0    
#> 11 pop2         0    
#> 12 pop2         0    
#> 13 pop3         0    
#> 14 pop3         0    
#> 15 pop3         0    
#> 16 pop3         0    
#> 17 pop3         0.5  
#> 18 pop3         0.5