bdply: Apply a function to each subset of an rbiom object.

In cmmr/rbiom: Read/Write, Analyze, and Visualize 'BIOM' Data

Apply a function to each subset of an rbiom object.

Description

blply() and bdply() let you divide your biom dataset into smaller pieces, run a function on those smaller rbiom objects, and return the results as a data.frame or list.

Usage

bdply(biom, vars, FUN, ..., iters = list(), prefix = FALSE)

blply(biom, vars, FUN, ..., iters = list(), prefix = FALSE)

Arguments

biom	An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.
vars	A character vector of metadata fields. Each unique combination of values in these columns will be used to create a subsetted rbiom object to pass to FUN. If NULL, biom will be passed to FUN unaltered. Unambiguous abbreviations of metadata fields are also accepted.
FUN	The function to execute on each subset of biom. For bdply(), the returned value will be coerced to a data.frame. For blply(), any returned value is unmodified.
...	Additional arguments to pass on to FUN.
iters	A named list of values to pass to FUN. Unlike ..., these will be iterated over in all combinations. Default: list()
prefix	When TRUE, prefixes the names in in iters with a '.' in the final data.frame or 'split_labels' attribute. Default: FALSE

Details

You can also specify additional variables for your function to iterate over in unique combinations.

Calls plyr::ddply() or plyr::dlply() internally.

Value

For bdply(), a tibble data.frame comprising the accumulated outputs of FUN, along with the columns specified by vars and iters. For blply(), a named list that has details about vars and iters in attr(,"split_labels").

See Also

Other metadata: glimpse.rbiom()

Other biom: biom_merge()

Examples

    library(rbiom)
    
    # bdply ----------------------------------------------------
    
    bdply(hmp50, "Sex", `$`, 'n_samples')
    
    blply(hmp50, "Sex", `$`, 'n_samples') %>% unlist()
    
    bdply(hmp50, c("Body Site", "Sex"), function (b) {
      adm <- adiv_matrix(b)[,c("Shannon", "Simpson")]
      apply(adm, 2L, mean)
    })
    
    iters <- list(w = c(TRUE, FALSE), d = c("bray", "euclid"))
    bdply(hmp50, "Sex", iters = iters, function (b, w, d) {
      r <- range(bdiv_distmat(biom = b, bdiv = d, weighted = w))
      round(data.frame(min = r[[1]], max = r[[2]]))
    })

cmmr/rbiom documentation built on April 28, 2024, 6:38 a.m.