These methods extend phyloseq's constructor functions to construct phyloseq components from tibbles (objects with class "tbl_df").
# S4 method for tbl_df otu_table(object, taxa_are_rows) # S4 method for tbl_df sample_data(object) # S4 method for tbl_df tax_table(object)
| object | A tibble whose first column contains the sample or taxa ids |
|---|---|
| taxa_are_rows | Logical; |
Since tibbles cannot have row names, the sample or taxon identifiers must be
contained in a regular column. Speedyseq currently always uses the first
column for the identifiers that would normally be taken from the row names
by phyloseq's built-in constructors. Thus the first column is assumed to
contain the sample names for sample_data() and the OTU/taxa names for
tax_table(); for otu_table(), the first column is assumed to contain the
sample names if taxa_are_rows = TRUE and the taxa names if taxa_are_rows = FALSE, with the other identifier being taken from the remaining column
names.
if (FALSE) { # Read a .csv file with readr, which creates an object of class `tbl_df` tbl <- readr::read_csv("path/to/otu_table.csv") # Inspect and check if taxa are rows and that the first column contains the # sample names or the taxa/OTU names head(tbl) # Create a phyloseq `otu_table` object otu <- otu_table(tbl, taxa_are_rows = FALSE) # Read a .csv file with readr, which creates an object of class `tbl_df` tbl <- readr::read_csv("path/to/sample_data.csv") # Inspect and check that the first column contains the sample names head(tbl) # Create a phyloseq `sample_data` object sam <- sample_data(tbl) }