sample_sums | R Documentation |
Summarize the taxa observations in each sample.
sample_sums(biom, rank = -1, sort = NULL, unc = "singly")
sample_apply(biom, FUN, rank = -1, sort = NULL, unc = "singly", ...)
biom |
An rbiom object, such as from |
rank |
What rank(s) of taxa to display. E.g. |
sort |
Sort the result. Options: |
unc |
How to handle unclassified, uncultured, and similarly ambiguous taxa names. Options are:
Abbreviations are allowed. Default: |
FUN |
The function to apply to each column of |
... |
Optional arguments to |
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.
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()
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)
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