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Subset an rbiom object by sample names, OTU names, metadata, or taxonomy.

Source: R/s3_methods.r, R/taxa_table.r
subset.Rd

Dropping samples or OTUs will lead to observations being removed from the OTU matrix (biom$counts). OTUs and samples with zero observations are automatically removed from the rbiom object.

Usage

# S3 method for class 'rbiom'
subset(x, subset, clone = TRUE, ...)

# S3 method for class 'rbiom'
x[i, j, ..., clone = TRUE, drop = FALSE]

# S3 method for class 'rbiom'
na.omit(object, fields = ".all", clone = TRUE, ...)

subset_taxa(x, subset, clone = TRUE, ...)

Arguments

x

An rbiom object, such as from as_rbiom().

subset

Logical expression for rows to keep. See base::subset().

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

...

Not used.

i, j

The sample or OTU names to keep. Or a logical/integer vector indicating which sample names from biom$samples or biom$otus to keep. Subsetting with [i] takes i as samples, whereas [i,j] takes i as otus and j as samples (corresponding to [rows, cols] in the underlying biom$counts matrix).

drop

Not used

object

An rbiom object, such as from as_rbiom().

fields

Which metadata field(s) to check for NAs, or ".all" to check all metadata fields.

Value

An rbiom object.

See also

Other transformations: modify_metadata, rarefy(), rarefy_cols(), slice_metadata, with()

Examples

    library(rbiom)
    library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:rbiom’:
#> 
#>     id
#> The following object is masked from ‘package:testthat’:
#> 
#>     matches
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
    
    # Subset to specific samples
    biom <- hmp50[c('HMP20', 'HMP42', 'HMP12')]
    biom$metadata
#> # A tibble: 3 × 5
#>   .sample   Age   BMI `Body Site` Sex   
#> * <chr>   <dbl> <dbl> <fct>       <fct> 
#> 1 HMP20      27    22 Stool       Female
#> 2 HMP42      34    19 Mid vagina  Female
#> 3 HMP12      35    26 Stool       Male  
    
    # Subset to specific OTUs
    biom <- hmp50[c('LtbAci52', 'UncO2012'),] # <- Trailing ,
    biom$taxonomy
#> # A tibble: 2 × 7
#>   .otu     Kingdom  Phylum        Class       Order           Family       Genus
#> * <chr>    <fct>    <fct>         <fct>       <fct>           <fct>        <fct>
#> 1 LtbAci52 Bacteria Firmicutes    Bacilli     Lactobacillales Lactobacill… Lact…
#> 2 UncO2012 Bacteria Bacteroidetes Bacteroidia Bacteroidales   Bacteroidac… Bact…
    
    # Subset to specific samples and OTUs
    biom <- hmp50[c('LtbAci52', 'UncO2012'), c('HMP20', 'HMP42', 'HMP12')]
    as.matrix(biom)
#>          HMP20 HMP42 HMP12
#> LtbAci52     0  1167     0
#> UncO2012   119     1  1196
    
    # Subset samples according to metadata
    biom <- subset(hmp50, `Body Site` %in% c('Saliva') & Age < 25)
    biom$metadata
#> # A tibble: 3 × 5
#>   .sample   Age   BMI `Body Site` Sex   
#> * <chr>   <dbl> <dbl> <fct>       <fct> 
#> 1 HMP27      24    30 Saliva      Female
#> 2 HMP28      23    19 Saliva      Female
#> 3 HMP30      24    21 Saliva      Female
    
    # Subset OTUs according to taxonomy
    biom <- subset_taxa(hmp50, Phylum == 'Cyanobacteria')
    biom$taxonomy
#> # A tibble: 4 × 7
#>   .otu     Kingdom  Phylum        Class           Order             Family Genus
#> * <chr>    <fct>    <fct>         <fct>           <fct>             <fct>  <fct>
#> 1 Hu4Lup30 Bacteria Cyanobacteria Chloroplast     o                 f      g    
#> 2 Unc02oth Bacteria Cyanobacteria Melainabacteria Obscuribacterales f      g    
#> 3 PinJeffr Bacteria Cyanobacteria Chloroplast     o                 f      g    
#> 4 Unc48787 Bacteria Cyanobacteria Melainabacteria Gastranaerophila… f      g    
    
    # Remove samples with NA metadata values
    biom <- mutate(hmp50, BS2 = na_if(`Body Site`, 'Saliva'))
    biom$metadata
#> # A tibble: 50 × 6
#>    .sample   Age   BMI `Body Site`    Sex    BS2           
#>  * <chr>   <dbl> <dbl> <fct>          <fct>  <fct>         
#>  1 HMP01      22    20 Buccal mucosa  Female Buccal mucosa 
#>  2 HMP02      24    23 Buccal mucosa  Male   Buccal mucosa 
#>  3 HMP03      28    26 Saliva         Male   NA            
#>  4 HMP04      25    23 Saliva         Male   NA            
#>  5 HMP05      27    24 Buccal mucosa  Female Buccal mucosa 
#>  6 HMP06      32    25 Saliva         Male   NA            
#>  7 HMP07      26    22 Buccal mucosa  Male   Buccal mucosa 
#>  8 HMP08      27    26 Saliva         Female NA            
#>  9 HMP09      33    32 Saliva         Male   NA            
#> 10 HMP10      22    20 Anterior nares Female Anterior nares
#> # ℹ 40 more rows
    biom <- na.omit(biom)
    biom$metadata
#> # A tibble: 40 × 6
#>    .sample   Age   BMI `Body Site`    Sex    BS2           
#>  * <chr>   <dbl> <dbl> <fct>          <fct>  <fct>         
#>  1 HMP01      22    20 Buccal mucosa  Female Buccal mucosa 
#>  2 HMP02      24    23 Buccal mucosa  Male   Buccal mucosa 
#>  3 HMP05      27    24 Buccal mucosa  Female Buccal mucosa 
#>  4 HMP07      26    22 Buccal mucosa  Male   Buccal mucosa 
#>  5 HMP10      22    20 Anterior nares Female Anterior nares
#>  6 HMP11      24    23 Buccal mucosa  Female Buccal mucosa 
#>  7 HMP12      35    26 Stool          Male   Stool         
#>  8 HMP13      24    21 Buccal mucosa  Female Buccal mucosa 
#>  9 HMP14      32    26 Buccal mucosa  Male   Buccal mucosa 
#> 10 HMP15      25    21 Anterior nares Female Anterior nares
#> # ℹ 30 more rows