rbiom (version 2.1.2)

sample_sums: Summarize the taxa observations in each sample.

Description

Summarize the taxa observations in each sample.

Usage

sample_sums(biom, rank = -1, sort = NULL, unc = "singly")
sample_apply(biom, FUN, rank = -1, sort = NULL, unc = "singly", ...)

Value

For sample_sums, A named numeric vector of the number of observations in each sample. For sample_apply, a named vector or list with the results of FUN. The names are the taxa IDs.

Arguments

biom

An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.

rank

What rank(s) of taxa to display. E.g. "Phylum", "Genus", ".otu", etc. An integer vector can also be given, where 1 is the highest rank, 2 is the second highest, -1 is the lowest rank, -2 is the second lowest, and 0 is the OTU "rank". Run biom$ranks to see all options for a given rbiom object. Default: -1.

sort

Sort the result. Options: NULL - don't sort; "asc" - in ascending order (smallest to largest); "desc" - in descending order (largest to smallest). Ignored when the result is not a simple numeric vector. Default: NULL

unc

How to handle unclassified, uncultured, and similarly ambiguous taxa names. Options are:

"singly" -

Replaces them with the OTU name.

"grouped" -

Replaces them with a higher rank's name.

"drop" -

Excludes them from the result.

"asis" -

To not check/modify any taxa names.

Abbreviations are allowed. Default: "singly"

FUN

The function to apply to each column of taxa_matrix().

...

Optional arguments to FUN.

See Also

Other samples: pull.rbiom()

Other rarefaction: rare_corrplot(), rare_multiplot(), rare_stacked(), rarefy(), rarefy_cols()

Other taxa_abundance: taxa_boxplot(), taxa_clusters(), taxa_corrplot(), taxa_heatmap(), taxa_stacked(), taxa_stats(), taxa_sums(), taxa_table()

Examples

    library(rbiom)
    library(ggplot2)
    
    sample_sums(hmp50, sort = 'asc') %>% head()
    
    # Unique OTUs and "cultured" classes per sample
    nnz <- function (x) sum(x > 0) # number of non-zeroes
    sample_apply(hmp50, nnz, 'otu') %>% head()
    sample_apply(hmp50, nnz, 'class', unc = 'drop') %>% head()
    
    # Number of reads in each sample's most abundant family
    sample_apply(hmp50, base::max, 'f', sort = 'desc') %>% head()
    
    ggplot() + geom_histogram(aes(x=sample_sums(hmp50)), bins = 20)