R/ps_arrange.R
ps_arrange.RdUses information in the sample_data or tax_table of phyloseq object to set the order of the samples (sample_data or tax_table specified by .target arg)
Give this function arguments in the same way you would use dplyr::arrange()
ps_arrange(ps, ..., .target = "sample_data")phyloseq object
dots passed directly to dplyr::arrange()
arrange samples by "sample_data" variables or "otu_table" taxa abundances
phyloseq
data("dietswap", package = "microbiome")
dietswap %>%
ps_arrange(subject, timepoint) %>%
phyloseq::sample_data() %>%
head(8)
#> subject sex nationality group sample timepoint
#> Sample-32 azh female AAM ED Sample-32 1
#> Sample-109 azh female AAM HE Sample-109 2
#> Sample-119 azh female AAM HE Sample-119 3
#> Sample-130 azh female AAM DI Sample-130 4
#> Sample-140 azh female AAM DI Sample-140 5
#> Sample-41 azh female AAM ED Sample-41 6
#> Sample-61 azl female AFR ED Sample-61 1
#> Sample-93 azl female AFR HE Sample-93 2
#> timepoint.within.group bmi_group
#> Sample-32 1 overweight
#> Sample-109 1 overweight
#> Sample-119 2 overweight
#> Sample-130 1 overweight
#> Sample-140 2 overweight
#> Sample-41 2 overweight
#> Sample-61 1 obese
#> Sample-93 1 obese
ps <- dietswap %>% ps_arrange(subject, desc(timepoint))
phyloseq::sample_data(ps) %>% head(8)
#> subject sex nationality group sample timepoint
#> Sample-41 azh female AAM ED Sample-41 6
#> Sample-140 azh female AAM DI Sample-140 5
#> Sample-130 azh female AAM DI Sample-130 4
#> Sample-119 azh female AAM HE Sample-119 3
#> Sample-109 azh female AAM HE Sample-109 2
#> Sample-32 azh female AAM ED Sample-32 1
#> Sample-67 azl female AFR ED Sample-67 6
#> Sample-86 azl female AFR DI Sample-86 5
#> timepoint.within.group bmi_group
#> Sample-41 2 overweight
#> Sample-140 2 overweight
#> Sample-130 1 overweight
#> Sample-119 2 overweight
#> Sample-109 1 overweight
#> Sample-32 1 overweight
#> Sample-67 2 obese
#> Sample-86 2 obese
phyloseq::otu_table(ps)[1:8, 1:8]
#> OTU Table: [8 taxa and 8 samples]
#> taxa are rows
#> Sample-41 Sample-140 Sample-130 Sample-119
#> Actinomycetaceae 0 0 0 0
#> Aerococcus 0 0 0 0
#> Aeromonas 0 0 0 0
#> Akkermansia 787 2627 2506 1068
#> Alcaligenes faecalis et rel. 2 2 2 2
#> Allistipes et rel. 564 224 919 209
#> Anaerobiospirillum 0 0 0 0
#> Anaerofustis 0 0 0 0
#> Sample-109 Sample-32 Sample-67 Sample-86
#> Actinomycetaceae 0 0 0 0
#> Aerococcus 0 0 0 0
#> Aeromonas 0 0 0 0
#> Akkermansia 52 200 35 93
#> Alcaligenes faecalis et rel. 2 2 2 2
#> Allistipes et rel. 756 737 39 29
#> Anaerobiospirillum 0 0 0 0
#> Anaerofustis 1 0 0 0
# you can also arrange samples by the abundances of taxa in the otu tables
pst <- dietswap %>% ps_arrange(desc(Akkermansia), .target = "otu_table")
phyloseq::otu_table(pst)[1:8, 1:8]
#> OTU Table: [8 taxa and 8 samples]
#> taxa are rows
#> Sample-140 Sample-130 Sample-167 Sample-119
#> Actinomycetaceae 0 0 0 0
#> Aerococcus 0 0 0 0
#> Aeromonas 0 0 0 0
#> Akkermansia 2627 2506 1214 1068
#> Alcaligenes faecalis et rel. 2 2 4 2
#> Allistipes et rel. 224 919 923 209
#> Anaerobiospirillum 0 0 0 0
#> Anaerofustis 0 0 0 0
#> Sample-100 Sample-121 Sample-41 Sample-35
#> Actinomycetaceae 0 0 0 0
#> Aerococcus 0 0 0 0
#> Aeromonas 0 0 0 0
#> Akkermansia 974 909 787 774
#> Alcaligenes faecalis et rel. 3 2 2 2
#> Allistipes et rel. 64 50 564 112
#> Anaerobiospirillum 0 0 0 0
#> Anaerofustis 0 0 0 0
phyloseq::sample_data(pst) %>% head(8)
#> subject sex nationality group sample timepoint
#> Sample-140 azh female AAM DI Sample-140 5
#> Sample-130 azh female AAM DI Sample-130 4
#> Sample-167 kpb male AAM HE Sample-167 3
#> Sample-119 azh female AAM HE Sample-119 3
#> Sample-100 wdf male AFR DI Sample-100 4
#> Sample-121 gty male AAM HE Sample-121 3
#> Sample-41 azh female AAM ED Sample-41 6
#> Sample-35 kpp male AAM ED Sample-35 1
#> timepoint.within.group bmi_group
#> Sample-140 2 overweight
#> Sample-130 1 overweight
#> Sample-167 2 obese
#> Sample-119 2 overweight
#> Sample-100 1 lean
#> Sample-121 2 overweight
#> Sample-41 2 overweight
#> Sample-35 1 lean