Subset to a specific number of samples.

Usage

# S3 method for class 'rbiom'
slice(.data, ..., .by = NULL, .preserve = FALSE, clone = TRUE)

# S3 method for class 'rbiom'
slice_head(.data, n, prop, by = NULL, clone = TRUE, ...)

# S3 method for class 'rbiom'
slice_tail(.data, n, prop, by = NULL, clone = TRUE, ...)

# S3 method for class 'rbiom'
slice_min(
  .data,
  order_by,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE,
  clone = TRUE,
  ...
)

# S3 method for class 'rbiom'
slice_max(
  .data,
  order_by,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = FALSE,
  clone = TRUE,
  ...
)

# S3 method for class 'rbiom'
slice_sample(
  .data,
  n,
  prop,
  by = NULL,
  weight_by = NULL,
  replace = FALSE,
  clone = TRUE,
  ...
)

Arguments

.data

An rbiom object, such as from as_rbiom().

...

For slice(), integer row indexes. For other slice_*() functions, not used. See dplyr::slice().

.by, by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

.preserve

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

clone

Create a copy of biom before modifying. If FALSE, biom is modified in place as a side-effect. See speed ups for use cases. Default: TRUE

n, prop

Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used. If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. prop will be rounded towards zero to generate an integer number of rows.

A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows.

order_by

<data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.

na_rm

Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in arrange()), so they will only be included if there are insufficient non-missing values to reach n/prop.

weight_by

<data-masking> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.

replace

Should sampling be performed with (TRUE) or without (FALSE, the default) replacement.

Value

An rbiom object.

Examples

    library(rbiom)
    
    # The last 3 samples in the metadata table.
    biom <- slice_tail(hmp50, n = 3)
#> Warning: ℹ Dropping 47 samples from biom object since they are not in the new metadata:
#>   "HMP01", "HMP02", "HMP03", "HMP04", "HMP05", "HMP06", "HMP07", "HMP08",
#>   "HMP09", "HMP10", "HMP11", "HMP12", "HMP13", "HMP14", "HMP15", "HMP16",
#>   "HMP17", "HMP18", …, "HMP46", and "HMP47".
    biom$metadata
#> # A tibble: 3 × 5
#>   .sample   Age   BMI `Body Site`    Sex   
#> * <chr>   <dbl> <dbl> <fct>          <fct> 
#> 1 HMP48      24    29 Anterior nares Male  
#> 2 HMP49      21    22 Mid vagina     Female
#> 3 HMP50      31    20 Mid vagina     Female
    
    # The 3 oldest subjects sampled.
    biom <- slice_max(hmp50, Age, n = 3)
#> Warning: ℹ Dropping 47 samples from biom object since they are not in the new metadata:
#>   "HMP01", "HMP02", "HMP03", "HMP04", "HMP05", "HMP06", "HMP07", "HMP08",
#>   "HMP09", "HMP10", "HMP11", "HMP12", "HMP13", "HMP14", "HMP15", "HMP16",
#>   "HMP17", "HMP18", …, "HMP49", and "HMP50".
    biom$metadata
#> # A tibble: 3 × 5
#>   .sample   Age   BMI `Body Site`    Sex   
#> * <chr>   <dbl> <dbl> <fct>          <fct> 
#> 1 HMP34      40    27 Anterior nares Female
#> 2 HMP37      38    31 Mid vagina     Female
#> 3 HMP44      38    19 Mid vagina     Female
    
    # Pick 3 samples at random.
    biom <- slice_sample(hmp50, n = 3)
#> Warning: ℹ Dropping 47 samples from biom object since they are not in the new metadata:
#>   "HMP01", "HMP02", "HMP03", "HMP04", "HMP05", "HMP06", "HMP08", "HMP09",
#>   "HMP10", "HMP11", "HMP12", "HMP13", "HMP14", "HMP15", "HMP16", "HMP17",
#>   "HMP18", "HMP19", …, "HMP49", and "HMP50".
    biom$metadata
#> # A tibble: 3 × 5
#>   .sample   Age   BMI `Body Site`    Sex   
#> * <chr>   <dbl> <dbl> <fct>          <fct> 
#> 1 HMP46      27    19 Mid vagina     Female
#> 2 HMP32      28    27 Anterior nares Female
#> 3 HMP07      26    22 Buccal mucosa  Male

Subset to a specific number of samples.

Usage

Arguments

Value

See also

Examples