Add or compute new phyloseq sample_data variables.
Uses dplyr::mutate() syntax.
ps_mutate(ps, ...)phyloseq object with modified sample_data
tax_mutate for manipulation of tax_table variables
library(phyloseq)
library(dplyr)
data("enterotype")
sample_data(enterotype)[1:10, ]
#> Enterotype Sample_ID SeqTech SampleID Project Nationality Gender
#> AM.AD.1 <NA> AM.AD.1 Sanger AM.AD.1 gill06 american F
#> AM.AD.2 <NA> AM.AD.2 Sanger AM.AD.2 gill06 american M
#> AM.F10.T1 <NA> AM.F10.T1 Sanger AM.F10.T1 turnbaugh09 american F
#> AM.F10.T2 3 AM.F10.T2 Sanger AM.F10.T2 turnbaugh09 american F
#> DA.AD.1 2 DA.AD.1 Sanger DA.AD.1 MetaHIT danish F
#> DA.AD.1T <NA> DA.AD.1T Sanger <NA> <NA> <NA> <NA>
#> DA.AD.2 3 DA.AD.2 Sanger DA.AD.2 MetaHIT danish M
#> DA.AD.3 3 DA.AD.3 Sanger DA.AD.3 MetaHIT danish F
#> DA.AD.3T <NA> DA.AD.3T Sanger <NA> <NA> <NA> <NA>
#> DA.AD.4 2 DA.AD.4 Sanger DA.AD.4 MetaHIT danish M
#> Age ClinicalStatus
#> AM.AD.1 28 healthy
#> AM.AD.2 37 healthy
#> AM.F10.T1 NA obese
#> AM.F10.T2 NA obese
#> DA.AD.1 59 healthy
#> DA.AD.1T NA <NA>
#> DA.AD.2 54 healthy
#> DA.AD.3 49 obese
#> DA.AD.3T NA <NA>
#> DA.AD.4 59 obese
months_in_year <- 12
ps <- enterotype %>%
ps_mutate(
age_months = Age * months_in_year,
IDs_match = as.character(Sample_ID) == as.character(SampleID),
placeholder = "Word"
)
sample_data(ps)[1:10, ]
#> Enterotype Sample_ID SeqTech SampleID Project Nationality Gender
#> AM.AD.1 <NA> AM.AD.1 Sanger AM.AD.1 gill06 american F
#> AM.AD.2 <NA> AM.AD.2 Sanger AM.AD.2 gill06 american M
#> AM.F10.T1 <NA> AM.F10.T1 Sanger AM.F10.T1 turnbaugh09 american F
#> AM.F10.T2 3 AM.F10.T2 Sanger AM.F10.T2 turnbaugh09 american F
#> DA.AD.1 2 DA.AD.1 Sanger DA.AD.1 MetaHIT danish F
#> DA.AD.1T <NA> DA.AD.1T Sanger <NA> <NA> <NA> <NA>
#> DA.AD.2 3 DA.AD.2 Sanger DA.AD.2 MetaHIT danish M
#> DA.AD.3 3 DA.AD.3 Sanger DA.AD.3 MetaHIT danish F
#> DA.AD.3T <NA> DA.AD.3T Sanger <NA> <NA> <NA> <NA>
#> DA.AD.4 2 DA.AD.4 Sanger DA.AD.4 MetaHIT danish M
#> Age ClinicalStatus age_months IDs_match placeholder
#> AM.AD.1 28 healthy 336 TRUE Word
#> AM.AD.2 37 healthy 444 TRUE Word
#> AM.F10.T1 NA obese NA TRUE Word
#> AM.F10.T2 NA obese NA TRUE Word
#> DA.AD.1 59 healthy 708 TRUE Word
#> DA.AD.1T NA <NA> NA NA Word
#> DA.AD.2 54 healthy 648 TRUE Word
#> DA.AD.3 49 obese 588 TRUE Word
#> DA.AD.3T NA <NA> NA NA Word
#> DA.AD.4 59 obese 708 TRUE Word
# Using the dplyr::across functionality is also possible
ps <- ps %>%
ps_mutate(
dplyr::across(where(is.factor), toupper),
another_var = TRUE,
SeqTech = NULL # deletes SeqTech variable
)
head(sample_data(ps))
#> Enterotype Sample_ID SampleID Project Nationality Gender Age
#> AM.AD.1 <NA> AM.AD.1 AM.AD.1 GILL06 AMERICAN F 28
#> AM.AD.2 <NA> AM.AD.2 AM.AD.2 GILL06 AMERICAN M 37
#> AM.F10.T1 <NA> AM.F10.T1 AM.F10.T1 TURNBAUGH09 AMERICAN F NA
#> AM.F10.T2 3 AM.F10.T2 AM.F10.T2 TURNBAUGH09 AMERICAN F NA
#> DA.AD.1 2 DA.AD.1 DA.AD.1 METAHIT DANISH F 59
#> DA.AD.1T <NA> DA.AD.1T <NA> <NA> <NA> <NA> NA
#> ClinicalStatus age_months IDs_match placeholder another_var
#> AM.AD.1 HEALTHY 336 TRUE Word TRUE
#> AM.AD.2 HEALTHY 444 TRUE Word TRUE
#> AM.F10.T1 OBESE NA TRUE Word TRUE
#> AM.F10.T2 OBESE NA TRUE Word TRUE
#> DA.AD.1 HEALTHY 708 TRUE Word TRUE
#> DA.AD.1T <NA> NA NA Word TRUE