├── .Rbuildignore
├── .travis.yml
├── DESCRIPTION
├── LICENSE
├── NAMESPACE
├── NEWS.md
├── R
    ├── agglomeration.R
    ├── as_tibble.R
    ├── dplyr-joins.R
    ├── dplyr-verbs.R
    ├── import-phyloseq.R
    ├── merge_samples.R
    ├── merge_taxa_vec.R
    ├── phyloseq-constructors.R
    ├── plot-methods.R
    ├── printing.R
    ├── psmelt.R
    ├── transform-filter.R
    ├── utils-pipe.R
    ├── utils.R
    └── zzz.R
├── README.Rmd
├── README.md
├── _pkgdown.yml
├── docs
    ├── 404.html
    ├── LICENSE-text.html
    ├── authors.html
    ├── bootstrap-toc.css
    ├── bootstrap-toc.js
    ├── docsearch.css
    ├── docsearch.js
    ├── index.html
    ├── link.svg
    ├── news
    │   └── index.html
    ├── pkgdown.css
    ├── pkgdown.js
    ├── pkgdown.yml
    ├── reference
    │   ├── Rplot001.png
    │   ├── Rplot002.png
    │   ├── Rplot003.png
    │   ├── Rplot004.png
    │   ├── Rplot005.png
    │   ├── Rplot006.png
    │   ├── Rplot007.png
    │   ├── Rplot008.png
    │   ├── as_tibble-phyloseq.html
    │   ├── bad_flush_right.html
    │   ├── bad_or_unique.html
    │   ├── filter-phyloseq.html
    │   ├── filter_sample_data.html
    │   ├── filter_tax_table.html
    │   ├── filter_taxa.html
    │   ├── import_phyloseq_functions.html
    │   ├── index.html
    │   ├── join-phyloseq.html
    │   ├── merge_groups.html
    │   ├── merge_samples2.html
    │   ├── merge_taxa_vec-methods.html
    │   ├── merge_taxa_vec.html
    │   ├── merge_taxa_vec_pseudo.html
    │   ├── mutate-phyloseq.html
    │   ├── mutate_sample_data.html
    │   ├── mutate_tax_table.html
    │   ├── oneline.html
    │   ├── orient_taxa.html
    │   ├── pipe.html
    │   ├── plot_bar-1.png
    │   ├── plot_bar-2.png
    │   ├── plot_bar-3.png
    │   ├── plot_bar-4.png
    │   ├── plot_bar.html
    │   ├── plot_heatmap-1.png
    │   ├── plot_heatmap-2.png
    │   ├── plot_heatmap-3.png
    │   ├── plot_heatmap-4.png
    │   ├── plot_heatmap-5.png
    │   ├── plot_heatmap-6.png
    │   ├── plot_heatmap-7.png
    │   ├── plot_heatmap-8.png
    │   ├── plot_heatmap.html
    │   ├── plot_tree-1.png
    │   ├── plot_tree-2.png
    │   ├── plot_tree.html
    │   ├── ps_tibble.html
    │   ├── psmelt-1.png
    │   ├── psmelt.html
    │   ├── reexports.html
    │   ├── relocate-phyloseq.html
    │   ├── rename-phyloseq.html
    │   ├── sample_data_stable.html
    │   ├── select-phyloseq.html
    │   ├── select_taxa-methods.html
    │   ├── show-methods.html
    │   ├── tax_glom.html
    │   ├── tibble-constructors.html
    │   ├── tibble_print.html
    │   ├── tip_glom-1.png
    │   ├── tip_glom-2.png
    │   ├── tip_glom.html
    │   ├── transform_sample_counts.html
    │   ├── tree_glom-1.png
    │   ├── tree_glom.html
    │   └── unique_or_na.html
    └── sitemap.xml
├── man
    ├── as_tibble-phyloseq.Rd
    ├── bad_flush_right.Rd
    ├── bad_or_unique.Rd
    ├── filter-phyloseq.Rd
    ├── filter_taxa.Rd
    ├── import_phyloseq_functions.Rd
    ├── join-phyloseq.Rd
    ├── merge_groups.Rd
    ├── merge_samples2.Rd
    ├── merge_taxa_vec.Rd
    ├── merge_taxa_vec_pseudo.Rd
    ├── mutate-phyloseq.Rd
    ├── oneline.Rd
    ├── orient_taxa.Rd
    ├── pipe.Rd
    ├── plot_bar.Rd
    ├── plot_heatmap.Rd
    ├── plot_tree.Rd
    ├── psmelt.Rd
    ├── reexports.Rd
    ├── relocate-phyloseq.Rd
    ├── rename-phyloseq.Rd
    ├── sample_data_stable.Rd
    ├── select-phyloseq.Rd
    ├── select_taxa-methods.Rd
    ├── show-methods.Rd
    ├── tax_glom.Rd
    ├── tibble-constructors.Rd
    ├── tibble_print.Rd
    ├── tip_glom.Rd
    ├── transform_sample_counts.Rd
    ├── tree_glom.Rd
    └── unique_or_na.Rd
└── tests
    ├── testthat.R
    └── testthat
        ├── test-as_tibble.R
        ├── test-constructors.R
        ├── test-dplyr-verbs.R
        ├── test-glom-functions.R
        ├── test-merge_samples.R
        ├── test-merge_taxa_vec.R
        ├── test-print.R
        ├── test-psmelt.R
        ├── test-transform-filter.R
        └── test-utils.R


/.Rbuildignore:
--------------------------------------------------------------------------------
1 | ^README\.Rmd$
2 | ^dev$
3 | ^\.travis\.yml$
4 | ^_pkgdown\.yml$
5 | ^docs$
6 | ^pkgdown$
7 | 


--------------------------------------------------------------------------------
/.travis.yml:
--------------------------------------------------------------------------------
 1 | # R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
 2 | 
 3 | language: r
 4 | cache: packages
 5 | warnings_are_errors: true
 6 | r: 
 7 |   - bioc-release
 8 | bioc_packages:
 9 |   - phyloseq
10 | r_packages:
11 |   - covr
12 | after_success:
13 |   - Rscript -e 'covr::codecov()'
14 | 


--------------------------------------------------------------------------------
/DESCRIPTION:
--------------------------------------------------------------------------------
 1 | Package: speedyseq
 2 | Title: Faster implementations of phyloseq functions
 3 | Version: 0.5.3.9021
 4 | Authors@R: 
 5 |     person(given = "Michael",
 6 |            family = "McLaren",
 7 |            role = c("aut", "cre"),
 8 |            email = "m.mclaren42@gmail.com")
 9 | Description: Faster implementations of phyloseq functions.
10 | URL: https://mikemc.github.io/speedyseq, https://github.com/mikemc/speedyseq
11 | BugReports: https://github.com/mikemc/speedyseq/issues
12 | License: AGPL-3 + file LICENSE
13 | Encoding: UTF-8
14 | LazyData: true
15 | Depends:
16 |     phyloseq
17 | Imports: 
18 |     ape,
19 |     Biostrings,
20 |     castor,
21 |     data.table,
22 |     dplyr,
23 |     ggplot2,
24 |     magrittr,
25 |     methods,
26 |     purrr,
27 |     rlang,
28 |     tibble,
29 |     tidyr,
30 |     scales,
31 |     stringr,
32 |     vctrs (>= 0.3.0),
33 |     vegan
34 | RoxygenNote: 7.3.1
35 | Suggests: 
36 |     cowplot,
37 |     DECIPHER,
38 |     forcats,
39 |     ggtree,
40 |     janitor,
41 |     phangorn,
42 |     testthat,
43 |     withr
44 | Roxygen: list(markdown = TRUE)
45 | 


--------------------------------------------------------------------------------
/NAMESPACE:
--------------------------------------------------------------------------------
 1 | # Generated by roxygen2: do not edit by hand
 2 | 
 3 | S3method(as_tibble,XStringSet)
 4 | S3method(as_tibble,otu_table)
 5 | S3method(as_tibble,phyloseq)
 6 | S3method(as_tibble,sample_data)
 7 | S3method(as_tibble,taxonomyTable)
 8 | S3method(glimpse,sample_data)
 9 | S3method(glimpse,taxonomyTable)
10 | S3method(print,otu_table)
11 | S3method(print,sample_data)
12 | S3method(print,taxonomyTable)
13 | S3method(unique_or_na,default)
14 | S3method(unique_or_na,factor)
15 | export("%>%")
16 | export(filter_sample_data)
17 | export(filter_tax_table)
18 | export(filter_taxa)
19 | export(filter_taxa2)
20 | export(inner_join_sample_data)
21 | export(inner_join_tax_table)
22 | export(left_join_sample_data)
23 | export(left_join_tax_table)
24 | export(merge_samples2)
25 | export(merge_taxa_vec)
26 | export(mutate_sample_data)
27 | export(mutate_tax_table)
28 | export(orient_taxa)
29 | export(plot_bar)
30 | export(plot_heatmap)
31 | export(plot_tree)
32 | export(psmelt)
33 | export(relocate_sample_data)
34 | export(relocate_tax_table)
35 | export(rename_sample_data)
36 | export(rename_tax_table)
37 | export(rename_with_sample_data)
38 | export(rename_with_tax_table)
39 | export(select_sample_data)
40 | export(select_tax_table)
41 | export(tax_glom)
42 | export(tip_glom)
43 | export(transform_sample_counts)
44 | export(transmute_sample_data)
45 | export(transmute_tax_table)
46 | export(tree_glom)
47 | export(unique_or_na)
48 | exportMethods(otu_table)
49 | exportMethods(sample_data)
50 | exportMethods(show)
51 | exportMethods(tax_table)
52 | import(phyloseq, except = c(psmelt, plot_bar, plot_tree, plot_heatmap, tax_glom, tip_glom, filter_taxa, transform_sample_counts))
53 | importFrom(data.table,data.table)
54 | importFrom(data.table,setkey)
55 | importFrom(data.table,setkeyv)
56 | importFrom(ggplot2,aes)
57 | importFrom(ggplot2,aes_string)
58 | importFrom(ggplot2,element_blank)
59 | importFrom(ggplot2,element_text)
60 | importFrom(ggplot2,facet_grid)
61 | importFrom(ggplot2,geom_bar)
62 | importFrom(ggplot2,geom_point)
63 | importFrom(ggplot2,geom_raster)
64 | importFrom(ggplot2,geom_segment)
65 | importFrom(ggplot2,geom_text)
66 | importFrom(ggplot2,ggplot)
67 | importFrom(ggplot2,ggtitle)
68 | importFrom(ggplot2,scale_fill_gradient)
69 | importFrom(ggplot2,scale_size_continuous)
70 | importFrom(ggplot2,scale_x_continuous)
71 | importFrom(ggplot2,scale_x_discrete)
72 | importFrom(ggplot2,scale_y_discrete)
73 | importFrom(ggplot2,theme)
74 | importFrom(magrittr,"%>%")
75 | importFrom(scales,log_trans)
76 | importFrom(tibble,as_tibble)
77 | importFrom(tibble,glimpse)
78 | importFrom(vegan,scores)
79 | 


--------------------------------------------------------------------------------
/R/as_tibble.R:
--------------------------------------------------------------------------------
  1 | #' @importFrom tibble as_tibble
  2 | tibble::as_tibble
  3 | 
  4 | #' Coerce phyloseq objects to tibble data frames
  5 | #'
  6 | #' These functions extend the `as_tibble()` function defined by the tibble
  7 | #' package to work on phyloseq objects or phyloseq-component objects.
  8 | #'
  9 | #' Tibbles (tbl_df objects) do not support rownames; the taxa and sample names
 10 | #' in the returned tibbles will always be stored in the first or second
 11 | #' columns. The names ".otu", ".sample", ".abundance", and ".sequence" are
 12 | #' special column names reserved for the otu/taxa names, sample names,
 13 | #' abundances, and reference sequences.
 14 | #'
 15 | #' @param x A phyloseq object or component.
 16 | #' @param pivot Whether to pivot the otu table to long format.
 17 | #' @param tax Whether to include taxonomy data.
 18 | #' @param ref Whether to include reference sequences.
 19 | #' @param .name_repair Function to repair names in the case of conflicts.
 20 | #'
 21 | #' @return A tibble (tbl_df)
 22 | #'
 23 | #' @name as_tibble-phyloseq
 24 | #'
 25 | #' @examples
 26 | #' library(tibble) # for as_tibble() and glimpse()
 27 | #'
 28 | #' data(GlobalPatterns)
 29 | #'
 30 | #' # Subset to 1/100 of the original taxa to speed operations
 31 | #' ps <- GlobalPatterns %>%
 32 | #'   filter_tax_table(dplyr::row_number() %% 100 == 1)
 33 | #'
 34 | #' # On phyloseq objects, as_tibble is similar to psmelt()
 35 | #' psmelt(ps) %>% glimpse
 36 | #' as_tibble(ps) %>% glimpse
 37 | #'
 38 | #' # By default, the otu_table method provides a tibble in long-format like
 39 | #' # psmelt and the phyloseq method
 40 | #' otu_table(ps) %>% as_tibble %>% glimpse
 41 | #'
 42 | #' # Sample data and taxonomy tables produced by as_tibble can be converted
 43 | #' # back into their respective phyloseq objects using speedyseq's tbl_df
 44 | #' # constructors
 45 | #' sample_data(ps) <- sample_data(ps) %>%
 46 | #'   as_tibble %>%
 47 | #'   dplyr::mutate(sample_sum = sample_sums(ps)) %>%
 48 | #'   sample_data
 49 | #' sample_data(ps) %>% glimpse
 50 | NULL
 51 | 
 52 | #' @export
 53 | #' @rdname as_tibble-phyloseq
 54 | as_tibble.otu_table <- function(x, 
 55 |                                 pivot = TRUE, 
 56 |                                 .name_repair = base::make.unique) {
 57 |   mat <- x %>% as("matrix")
 58 |   if (pivot) {
 59 |     if (!taxa_are_rows(x))
 60 |       mat <- t(mat)
 61 |     tb <- mat %>%
 62 |       tibble::as_tibble(rownames = ".otu") %>%
 63 |       tidyr::pivot_longer(
 64 |         cols = sample_names(x),
 65 |         names_to = ".sample",
 66 |         values_to = ".abundance"
 67 |       )
 68 |   } else {
 69 |     rn <- ifelse(taxa_are_rows(x), ".otu", ".sample")
 70 |     tb <- mat %>%
 71 |       tibble::as_tibble(rownames = rn) %>%
 72 |       rlang::set_names(., 
 73 |         vctrs::vec_as_names(names(.), repair = .name_repair)
 74 |       )
 75 |   }
 76 |   tb
 77 | }
 78 | 
 79 | #' @export
 80 | #' @rdname as_tibble-phyloseq
 81 | as_tibble.sample_data <- function(x, .name_repair = base::make.unique) {
 82 |   x %>%
 83 |     as("data.frame") %>%
 84 |     tibble::as_tibble(rownames = ".sample") %>%
 85 |     rlang::set_names(., 
 86 |       vctrs::vec_as_names(names(.), repair = .name_repair)
 87 |     )
 88 | }
 89 | 
 90 | #' @export
 91 | #' @rdname as_tibble-phyloseq
 92 | as_tibble.taxonomyTable <- function(x, .name_repair = base::make.unique) {
 93 |   x %>%
 94 |     as("matrix") %>%
 95 |     tibble::as_tibble(rownames = ".otu") %>%
 96 |     rlang::set_names(., 
 97 |       vctrs::vec_as_names(names(.), repair = .name_repair)
 98 |     )
 99 | }
100 | 
101 | #' @export
102 | #' @rdname as_tibble-phyloseq
103 | as_tibble.XStringSet <- function(x) {
104 |   x %>%
105 |     as.character %>%
106 |     tibble::enframe(".otu", ".sequence")
107 | }
108 | 
109 | #' @export
110 | #' @rdname as_tibble-phyloseq
111 | as_tibble.phyloseq <- function(x, 
112 |                                tax = TRUE, 
113 |                                ref = FALSE, 
114 |                                .name_repair = base::make.unique) {
115 |   tb <- otu_table(x) %>% as_tibble(pivot = TRUE)
116 |   # Add sample data if it exists
117 |   sam <- access(x, "sam_data")
118 |   if (!is.null(sam)) {
119 |     tb <- tb %>% 
120 |       dplyr::left_join(
121 |         sam %>% as_tibble(.name_repair = .name_repair),
122 |         by = ".sample",
123 |         suffix = c("", ".sam")
124 |       )
125 |   }
126 |   # Add tax_table if it exists and tax = TRUE
127 |   tt <- access(x, "tax_table")
128 |   if (tax & !is.null(tt)) {
129 |     tb <- tb %>% 
130 |       dplyr::left_join(
131 |         tt %>% as_tibble(.name_repair = .name_repair),
132 |         by = ".otu", 
133 |         suffix = c("", ".tax")
134 |       )
135 |   }
136 |   # Add refseq if it exists and ref = TRUE
137 |   rs <- access(x, "refseq")
138 |   if (ref & !is.null(rs)) {
139 |     tb <- tb %>% 
140 |       dplyr::left_join(
141 |         rs %>% as_tibble,
142 |         by = ".otu", 
143 |         suffix = c("", ".ref")
144 |       )
145 |   }
146 |   tb
147 | }
148 | 


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/R/dplyr-joins.R:
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  1 | # vim: foldmethod=marker
  2 | 
  3 | # Some of the documentation for these functions is modified from the
  4 | # corresponding dplyr functions (https://github.com/tidyverse/dplyr), MIT
  5 | # license RStudio and others.
  6 | 
  7 | #' Mutating joins of the taxonomy table or sample data
  8 | #'
  9 | #' @description
 10 | #' `r lifecycle::badge("experimental")`
 11 | #'
 12 | #' These functions are wrappers around dplyr joining operations (see
 13 | #' `dplyr::mutate-joins`) whose first (`x`) argument is either the taxonomy
 14 | #' table or sample data.
 15 | #'
 16 | #' @details
 17 | #' When joining by taxonomy table, the .otu column name can be used to match
 18 | #' taxa names.
 19 | #' When joining by sample data, the .sample column name can be used to match
 20 | #' sample names.
 21 | #' 
 22 | #' @param x A `phyloseq`, `taxonomyTable`, or `sample_data` object
 23 | #' @param ... Arguments passed to respective dplyr joining operation
 24 | #'
 25 | #' @return An object of the same type as `x`, with added columns
 26 | #'
 27 | #' @seealso 
 28 | #' [dplyr::left_join()]
 29 | #' [dplyr::inner_join()]
 30 | #'
 31 | #' @name join-phyloseq
 32 | #'
 33 | #' @examples
 34 | #' library(tibble)
 35 | #'
 36 | #' data(GlobalPatterns)
 37 | #' 
 38 | #' GlobalPatterns %>% sample_variables
 39 | #' ps1 <- GlobalPatterns %>%
 40 | #'   select_sample_data(!contains("Barcode"))
 41 | #' y <- GlobalPatterns %>%
 42 | #'   sample_data %>%
 43 | #'   select_sample_data(contains("Barcode")) %>%
 44 | #'   as_tibble
 45 | #' ps2 <- ps1 %>% left_join_sample_data(y, by = ".sample")
 46 | #' ps2 %>% sample_variables
 47 | NULL
 48 | 
 49 | # left_join ----------------------------------------------------------------{{{
 50 | 
 51 | #' @rdname join-phyloseq
 52 | #' @export
 53 | setGeneric("left_join_tax_table", 
 54 |   function(x, ...) standardGeneric("left_join_tax_table")
 55 | )
 56 | 
 57 | #' @rdname join-phyloseq
 58 | setMethod("left_join_tax_table", "phyloseq",
 59 |   function(x, ...) {
 60 |     tax_table(x) <- tax_table(x) %>% left_join_tax_table(...)
 61 |     x
 62 |   })
 63 | 
 64 | #' @rdname join-phyloseq
 65 | setMethod("left_join_tax_table", "taxonomyTable",
 66 |   function(x, ...) {
 67 |     x %>%
 68 |       as_tibble %>%
 69 |       dplyr::left_join(...) %>%
 70 |       {suppressMessages(tax_table(.))}
 71 |   })
 72 | 
 73 | #' @rdname join-phyloseq
 74 | #' @export
 75 | setGeneric("left_join_sample_data", 
 76 |   function(x, ...) standardGeneric("left_join_sample_data")
 77 | )
 78 | 
 79 | #' @rdname join-phyloseq
 80 | setMethod("left_join_sample_data", "phyloseq",
 81 |   function(x, ...) {
 82 |     sample_data(x) <- sample_data(x) %>% left_join_sample_data(...)
 83 |     x
 84 |   })
 85 | 
 86 | #' @rdname join-phyloseq
 87 | setMethod("left_join_sample_data", "sample_data",
 88 |   function(x, ...) {
 89 |     x %>%
 90 |       as_tibble %>%
 91 |       dplyr::left_join(...) %>%
 92 |       {suppressMessages(sample_data(.))}
 93 |   })
 94 | 
 95 | # }}}
 96 | 
 97 | # inner_join ---------------------------------------------------------------{{{
 98 | 
 99 | #' @rdname join-phyloseq
100 | #' @export
101 | setGeneric("inner_join_tax_table", 
102 |   function(x, ...) standardGeneric("inner_join_tax_table")
103 | )
104 | 
105 | #' @rdname join-phyloseq
106 | setMethod("inner_join_tax_table", "phyloseq",
107 |   function(x, ...) {
108 |     tax_table(x) <- tax_table(x) %>% inner_join_tax_table(...)
109 |     x
110 |   })
111 | 
112 | #' @rdname join-phyloseq
113 | setMethod("inner_join_tax_table", "taxonomyTable",
114 |   function(x, ...) {
115 |     x %>%
116 |       as_tibble %>%
117 |       dplyr::inner_join(...) %>%
118 |       {suppressMessages(tax_table(.))}
119 |   })
120 | 
121 | #' @rdname join-phyloseq
122 | #' @export
123 | setGeneric("inner_join_sample_data", 
124 |   function(x, ...) standardGeneric("inner_join_sample_data")
125 | )
126 | 
127 | #' @rdname join-phyloseq
128 | setMethod("inner_join_sample_data", "phyloseq",
129 |   function(x, ...) {
130 |     sample_data(x) <- sample_data(x) %>% inner_join_sample_data(...)
131 |     x
132 |   })
133 | 
134 | #' @rdname join-phyloseq
135 | setMethod("inner_join_sample_data", "sample_data",
136 |   function(x, ...) {
137 |     x %>%
138 |       as_tibble %>%
139 |       dplyr::inner_join(...) %>%
140 |       {suppressMessages(sample_data(.))}
141 |   })
142 | 
143 | # }}}
144 | 


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/R/import-phyloseq.R:
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1 | #' Import phyloseq functions
2 | #' 
3 | #' Import all phyloseq functions for internal use, except those we are
4 | #' reimplementing.
5 | #' 
6 | #' @name import_phyloseq_functions
7 | #' @rawNamespace import(phyloseq, except = c(psmelt, plot_bar, plot_tree, plot_heatmap, tax_glom, tip_glom, filter_taxa, transform_sample_counts))
8 | NULL
9 | 


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/R/phyloseq-constructors.R:
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 1 | #' Construct phyloseq objects from tibbles
 2 | #'
 3 | #' These methods extend phyloseq's constructor functions to construct phyloseq
 4 | #' components from tibbles (objects with class "tbl_df"). 
 5 | #'
 6 | #' Since tibbles cannot have row names, the sample or taxon identifiers must be
 7 | #' contained in a regular column. Speedyseq currently always uses the first
 8 | #' column for the identifiers that would normally be taken from the row names
 9 | #' by phyloseq's built-in constructors. Thus the first column is assumed to
10 | #' contain the sample names for `sample_data()` and the OTU/taxa names for
11 | #' `tax_table()`; for `otu_table()`, the first column is assumed to contain the
12 | #' sample names if `taxa_are_rows = TRUE` and the taxa names if `taxa_are_rows
13 | #' = FALSE`, with the other identifier being taken from the remaining column
14 | #' names.
15 | #' 
16 | #' @param object A tibble whose first column contains the sample or taxa ids
17 | #' @param taxa_are_rows Logical; `TRUE` if rows correspond to taxa and `FALSE`
18 | #' if rows correspond to samples
19 | #'
20 | #' @name tibble-constructors
21 | #' 
22 | #' @seealso 
23 | #' [`tibble::tbl_df`]
24 | #' [phyloseq::otu_table()]
25 | #' [phyloseq::sample_data()]
26 | #' [phyloseq::tax_table()]
27 | #'
28 | #' @examples
29 | #' \dontrun{
30 | #' # Read a .csv file with readr, which creates an object of class `tbl_df`
31 | #' tbl <- readr::read_csv("path/to/otu_table.csv")
32 | #' # Inspect and check if taxa are rows and that the first column contains the
33 | #' # sample names or the taxa/OTU names
34 | #' head(tbl) 
35 | #' # Create a phyloseq `otu_table` object
36 | #' otu <- otu_table(tbl, taxa_are_rows = FALSE)
37 | #'
38 | #' # Read a .csv file with readr, which creates an object of class `tbl_df`
39 | #' tbl <- readr::read_csv("path/to/sample_data.csv")
40 | #' # Inspect and check that the first column contains the sample names
41 | #' head(tbl) 
42 | #' # Create a phyloseq `sample_data` object
43 | #' sam <- sample_data(tbl)
44 | #' }
45 | NULL
46 | 
47 | #' @rdname tibble-constructors
48 | #' @export
49 | setMethod("otu_table", "tbl_df", function (object, taxa_are_rows) {
50 |   if (taxa_are_rows) {
51 |     message(paste0(
52 |       "Assuming first column, `", names(object)[1], 
53 |       "`, contains the taxa names"))
54 |   } else {
55 |     message(paste0(
56 |       "Assuming first column, `", names(object)[1], 
57 |       "`, contains the sample names"))
58 |   }
59 |   object %>%
60 |     tibble::column_to_rownames(var = names(object)[1]) %>%
61 |     as("matrix") %>%
62 |     otu_table(taxa_are_rows)
63 | })
64 | 
65 | #' @rdname tibble-constructors
66 | #' @export
67 | setMethod("sample_data", "tbl_df", function(object) {
68 |   message(paste0(
69 |     "Assuming first column, `", names(object)[1], 
70 |     "`, contains the sample names"))
71 |   object %>%
72 |     tibble::column_to_rownames(var = names(object)[1]) %>%
73 |     sample_data
74 | })
75 | 
76 | #' @rdname tibble-constructors
77 | #' @export
78 | setMethod("tax_table", "tbl_df", function (object) {
79 |   message(paste0(
80 |     "Assuming first column, `", names(object)[1], 
81 |     "`, contains the taxa names"))
82 |   object %>%
83 |     tibble::column_to_rownames(var = names(object)[1]) %>%
84 |     as("matrix") %>%
85 |     tax_table
86 | })
87 | 
88 | 


--------------------------------------------------------------------------------
/R/psmelt.R:
--------------------------------------------------------------------------------
  1 | # Attribution: The documentation for `psmelt()` and the first 54 lines of the
  2 | # function's code are from phyloseq,
  3 | # https://github.com/joey711/phyloseq/blob/master/R/plot-methods.R
  4 | 
  5 | #' Melt phyloseq data object into large data.frame
  6 | #'
  7 | #' The psmelt function is a specialized melt function for melting phyloseq
  8 | #' objects (instances of the phyloseq class), usually for producing graphics
  9 | #' with [ggplot2::ggplot2]. The naming conventions used in downstream phyloseq
 10 | #' graphics functions have reserved the following variable names that should
 11 | #' not be used as the names of [sample_variables()] or taxonomic
 12 | #' [rank_names()]. These reserved names are `c("Sample", "Abundance", "OTU")`.
 13 | #' Also, you should not have identical names for sample variables and taxonomic
 14 | #' ranks. That is, the intersection of the output of the following two
 15 | #' functions [sample_variables()], [rank_names()] should be an empty vector
 16 | #' (e.g.  `intersect(sample_variables(physeq), rank_names(physeq))`). All of
 17 | #' these potential name collisions are checked-for and renamed automatically
 18 | #' with a warning. However, if you (re)name your variables accordingly ahead of
 19 | #' time, it will reduce confusion and eliminate the warnings.
 20 | #'
 21 | #' The `as` argument allows choosing between three classes for tabular data:
 22 | #'
 23 | #' - [data.frame-class] is chosen by "data.frame" or "df"
 24 | #' - [data.table-class] is chosen by "data.table" or "dt"
 25 | #' - [tbl_df-class] is chosen by "tbl_df", "tbl", or "tibble"
 26 | #'
 27 | #' The default is "data.frame" and can be overridden by setting the
 28 | #' "speedyseq.psmelt_class" option.
 29 | #'
 30 | #' Note that "melted" phyloseq data is stored much less efficiently, and so RAM
 31 | #' storage issues could arise with a smaller dataset (smaller number of
 32 | #' samples/OTUs/variables) than one might otherwise expect.  For common sizes
 33 | #' of graphics-ready datasets, however, this should not be a problem.  Because
 34 | #' the number of OTU entries has a large effect on the RAM requirement, methods
 35 | #' to reduce the number of separate OTU entries -- for instance by
 36 | #' agglomerating OTUs based on phylogenetic distance using [tip_glom()] -- can
 37 | #' help alleviate RAM usage problems. This function is made user-accessible for
 38 | #' flexibility, but is also used extensively by plot functions in phyloseq.
 39 | #'
 40 | #' @param physeq An [otu_table-class] or [phyloseq-class]; most useful for
 41 | #'   phyloseq-class.
 42 | #' @param as Class of the output table; see Details.
 43 | #'
 44 | #' @return A table of the specified class
 45 | #'
 46 | #' @seealso
 47 | #' [plot_bar()]
 48 | #' 
 49 | #' @export
 50 | #'
 51 | #' @examples
 52 | #' data("GlobalPatterns")
 53 | #' gp.ch = subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
 54 | #' mdf = psmelt(gp.ch)
 55 | #' nrow(mdf)
 56 | #' ncol(mdf)
 57 | #' colnames(mdf)
 58 | #' head(rownames(mdf))
 59 | #' # Create a ggplot similar to
 60 | #' library("ggplot2")
 61 | #' p = ggplot(mdf, aes(x=SampleType, y=Abundance, fill=Genus))
 62 | #' p = p + geom_bar(color="black", stat="identity", position="stack")
 63 | #' print(p)
 64 | psmelt <- function(physeq, as = getOption("speedyseq.psmelt_class")) {
 65 |   stopifnot(as %in% c("data.frame", "df", "data.table", "dt", "tbl_df", "tbl",
 66 |       "tibble"))
 67 |   # Access covariate names from object, if present
 68 |   if(!inherits(physeq, "phyloseq")){
 69 |     rankNames = NULL
 70 |     sampleVars = NULL
 71 |   } else {
 72 |     # Still might be NULL, but attempt access
 73 |     rankNames = rank_names(physeq, FALSE)
 74 |     sampleVars = sample_variables(physeq, FALSE) 
 75 |   }
 76 |   # Define reserved names
 77 |   reservedVarnames = c("Sample", "Abundance", "OTU")  
 78 |   # type-1a conflict: between sample_data 
 79 |   # and reserved psmelt variable names
 80 |   type1aconflict = intersect(reservedVarnames, sampleVars)
 81 |   if(length(type1aconflict) > 0){
 82 |     wh1a = which(sampleVars %in% type1aconflict)
 83 |     new1a = paste0("sample_", sampleVars[wh1a])
 84 |     # First warn about the change
 85 |     warning("The sample variables: \n",
 86 |             paste(sampleVars[wh1a], collapse=", "), 
 87 |             "\n have been renamed to: \n",
 88 |             paste0(new1a, collapse=", "), "\n",
 89 |             "to avoid conflicts with special phyloseq plot attribute names.")
 90 |     # Rename the sample variables.
 91 |     colnames(sample_data(physeq))[wh1a] <- new1a
 92 |   }
 93 |   # type-1b conflict: between tax_table
 94 |   # and reserved psmelt variable names
 95 |   type1bconflict = intersect(reservedVarnames, rankNames)
 96 |   if(length(type1bconflict) > 0){
 97 |     wh1b = which(rankNames %in% type1bconflict)
 98 |     new1b = paste0("taxa_", rankNames[wh1b])
 99 |     # First warn about the change
100 |     warning("The rank names: \n",
101 |             paste(rankNames[wh1b], collapse=", "), 
102 |             "\n have been renamed to: \n",
103 |             paste0(new1b, collapse=", "), "\n",
104 |             "to avoid conflicts with special phyloseq plot attribute names.")
105 |     # Rename the conflicting taxonomic ranks
106 |     colnames(tax_table(physeq))[wh1b] <- new1b
107 |   }
108 |   # type-2 conflict: internal between tax_table and sample_data
109 |   type2conflict = intersect(sampleVars, rankNames)
110 |   if(length(type2conflict) > 0){
111 |     wh2 = which(sampleVars %in% type2conflict)
112 |     new2 = paste0("sample_", sampleVars[wh2])
113 |     # First warn about the change
114 |     warning("The sample variables: \n",
115 |             paste0(sampleVars[wh2], collapse=", "), 
116 |             "\n have been renamed to: \n",
117 |             paste0(new2, collapse=", "), "\n",
118 |             "to avoid conflicts with taxonomic rank names.")
119 |     # Rename the sample variables
120 |     colnames(sample_data(physeq))[wh2] <- new2
121 |   }
122 |   # Enforce OTU table orientation. Redundant-looking step
123 |   # supports "naked" otu_table as `physeq` input.
124 |   otutab = otu_table(physeq)
125 |   if(!taxa_are_rows(otutab)){otutab <- t(otutab)}
126 |   ## Speedyseq changes start here
127 |   # Convert the otu table to a tibble in tall form (one sample-taxon obsevation
128 |   # per row)
129 |   dtb <- otutab %>% 
130 |     as("matrix") %>%
131 |     data.table::as.data.table(keep.rownames = "OTU") %>%
132 |     data.table::melt(id.vars = c("OTU"), variable.name = "Sample", 
133 |       value.name = "Abundance", variable.factor = FALSE)
134 |   # Add the sample data if it exists
135 |   if (!is.null(sampleVars)) {
136 |     sam <- sample_data(physeq) %>%
137 |       as("data.frame") %>% 
138 |       data.table::as.data.table(keep.rownames = "Sample")
139 |     dtb <- dtb[sam, on = .(Sample = Sample)]
140 |   }
141 |   # Add the tax table if it exists
142 |   if (!is.null(rankNames)) {
143 |     tax <- tax_table(physeq) %>%
144 |       as("matrix") %>%
145 |       data.table::as.data.table(keep.rownames = "OTU")
146 |     dtb <- dtb[tax, on = .(OTU = OTU)]
147 |   }
148 |   # Arrange by Abundance, then OTU names (to approx. phyloseq behavior)
149 |   dtb <- dtb %>%
150 |     data.table::setorder(-Abundance, OTU)
151 |   # Return as requested class
152 |   if (as %in% c("data.table", "dt")) {
153 |     dtb
154 |   } else if (as %in% c("data.frame", "df")) {
155 |     dtb %>% as("data.frame")
156 |   } else if (as %in% c("tbl_df", "tbl", "tibble")) {
157 |     dtb <- dtb %>% tibble::as_tibble()
158 |     attr(dtb, ".internal.selfref") <- NULL
159 |     dtb
160 |   } else {
161 |     stop("Invalid output class specified by `as`.")
162 |   }
163 | }
164 | 


--------------------------------------------------------------------------------
/R/transform-filter.R:
--------------------------------------------------------------------------------
 1 | # Function titles copied from phyloseq's documentation
 2 | 
 3 | # Transform sample counts -----------------------------------------------------
 4 | 
 5 | #' Transform abundance data in an `otu_table`, sample-by-sample
 6 | #' 
 7 | #' Version of [phyloseq::transform_sample_counts()] that allows a
 8 | #' purrr-style anonymous function.
 9 | #'
10 | #' This function simply calls [purrr::as_mapper()] on `fun` and passes the
11 | #' resulting function on to [phyloseq::transform_sample_counts()].
12 | #'
13 | #' @param physeq [phyloseq-class()] or [ape::phylo()].
14 | #' @param fun A function or formula that can be converted to a function by
15 | #'   [purrr::as_mapper()]
16 | #' @param ... Additional arguments passed on to `fun`
17 | #' 
18 | #' @seealso 
19 | #' [phyloseq::transform_sample_counts()]
20 | #' [purrr::as_mapper()]
21 | #'
22 | #' @export
23 | #' @examples
24 | #' data(GlobalPatterns)
25 | #' # Filter low prevalence taxa, then convert to proportions
26 | #' gp.prop <- GlobalPatterns %>%
27 | #'   filter_taxa2(~ sum(. > 0) > 5) %>%
28 | #'   transform_sample_counts(~ . / sum(.))
29 | transform_sample_counts <- function(physeq, fun, ...) {
30 |   phyloseq::transform_sample_counts(physeq, purrr::as_mapper(fun), ...)
31 | }
32 | 
33 | # Filter taxa -----------------------------------------------------------------
34 | 
35 | #' Filter taxa based on across-sample OTU abundance criteria
36 | #' 
37 | #' Variations of [phyloseq::filter_taxa()] that allows a purrr-style anonymous
38 | #' function.
39 | #'
40 | #' `filter_taxa()` simply calls [purrr::as_mapper()] on `fun` and passes the
41 | #' resulting function on to [phyloseq::filter_taxa()]. `filter_taxa2()` also
42 | #' sets `prune = TRUE`, which is convenient when passing a phyloseq object in a
43 | #' pipe chain (see example).
44 | #'
45 | #' @param physeq [phyloseq-class()] or [ape::phylo()].
46 | #' @param fun A function or formula that can be converted to a function by
47 | #'   [purrr::as_mapper()]
48 | #' @param prune A logical. If `FALSE`, then this function returns a logical
49 | #'   vector specifying the taxa that passed the filter; if `TRUE`, then this
50 | #'   function returns the pruned phyloseq object.
51 | #'
52 | #' @export
53 | #' @seealso 
54 | #' [phyloseq::filter_taxa()]
55 | #' [purrr::as_mapper()]
56 | #' 
57 | #' @examples
58 | #' data(GlobalPatterns)
59 | #' # Filter low prevalence taxa and then convert to proportions
60 | #' gp.prop <- GlobalPatterns %>%
61 | #'   filter_taxa2(~ sum(. > 0) > 5) %>%
62 | #'   transform_sample_counts(~ . / sum(.))
63 | filter_taxa <- function(physeq, fun, prune = FALSE) {
64 |   phyloseq::filter_taxa(physeq, purrr::as_mapper(fun), prune = prune)
65 | }
66 | 
67 | #' @export
68 | #' @describeIn filter_taxa Sets `prune = TRUE`
69 | filter_taxa2 <- function(physeq, fun) {
70 |   phyloseq::filter_taxa(physeq, purrr::as_mapper(fun), prune = TRUE)
71 | }
72 | 
73 | 


--------------------------------------------------------------------------------
/R/utils-pipe.R:
--------------------------------------------------------------------------------
 1 | #' Pipe operator
 2 | #'
 3 | #' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
 4 | #'
 5 | #' @name %>%
 6 | #' @rdname pipe
 7 | #' @keywords internal
 8 | #' @export
 9 | #' @importFrom magrittr %>%
10 | #' @usage lhs \%>\% rhs
11 | NULL
12 | 


--------------------------------------------------------------------------------
/R/utils.R:
--------------------------------------------------------------------------------
  1 | # select_taxa -----------------------------------------------------------------
  2 | 
  3 | #' Select a subset of taxa in a specified order where possible
  4 | #'
  5 | #' Select (a subset of) taxa; if `x` allows taxa to be reordered, then taxa are
  6 | #' given in the specified order.
  7 | #'
  8 | #' This is a simple selector function that is like `prune_taxa(taxa, x)` when
  9 | #' `taxa` is a character vector but always gives the taxa in the order `taxa`
 10 | #' if possible (that is, except for phy_tree's and phyloseq objects that
 11 | #' contain phy_tree's).
 12 | #'
 13 | #' @param x A phyloseq object or phyloseq component object
 14 | #' @param taxa Character vector of taxa to select, in requested order
 15 | #' @param reorder Logical specifying whether to use the order in `taxa` (TRUE)
 16 | #'   or keep the order in `taxa_names(x)` (FALSE)
 17 | #' @keywords internal
 18 | #' @rdname select_taxa-methods
 19 | setGeneric("select_taxa", 
 20 |   function(x, taxa, reorder = TRUE) standardGeneric("select_taxa")
 21 | )
 22 | 
 23 | #' @rdname select_taxa-methods
 24 | setMethod("select_taxa", signature("sample_data", "character"), 
 25 |   function(x, taxa) {
 26 |     stopifnot(!anyDuplicated(taxa))
 27 |     x
 28 |   }
 29 | )
 30 | 
 31 | #' @rdname select_taxa-methods
 32 | setMethod("select_taxa", signature("otu_table", "character"), 
 33 |   function(x, taxa, reorder = TRUE){
 34 |     stopifnot(!anyDuplicated(taxa))
 35 |     stopifnot(all(taxa %in% taxa_names(x)))
 36 |     if (!reorder)
 37 |       taxa <- intersect(taxa_names(x), taxa)
 38 |     if (taxa_are_rows(x)) {
 39 |       x[taxa, , drop=FALSE]
 40 |     } else {
 41 |       x[, taxa, drop=FALSE]
 42 |     }
 43 |   }
 44 | )
 45 | 
 46 | #' @rdname select_taxa-methods
 47 | setMethod("select_taxa", signature("taxonomyTable", "character"), 
 48 |   function(x, taxa, reorder = TRUE) {
 49 |     stopifnot(!anyDuplicated(taxa))
 50 |     stopifnot(all(taxa %in% taxa_names(x)))
 51 |     if (!reorder)
 52 |       taxa <- intersect(taxa_names(x), taxa)
 53 | 		x[taxa, , drop=FALSE]
 54 | 	}
 55 | )
 56 | 
 57 | #' @rdname select_taxa-methods
 58 | setMethod("select_taxa", signature("XStringSet", "character"), 
 59 |   function(x, taxa, reorder = TRUE) {
 60 |     stopifnot(!anyDuplicated(taxa))
 61 |     stopifnot(all(taxa %in% taxa_names(x)))
 62 |     if (!reorder)
 63 |       taxa <- intersect(taxa_names(x), taxa)
 64 | 		x[taxa]
 65 | 	}
 66 | )
 67 | 
 68 | #' @rdname select_taxa-methods
 69 | setMethod("select_taxa", signature("phylo", "character"), 
 70 |   function(x, taxa) {
 71 |     # NOTE: `reorder` argument silently ignored if supplied
 72 |     stopifnot(!anyDuplicated(taxa))
 73 |     stopifnot(all(taxa %in% taxa_names(x)))
 74 |     ape::keep.tip(x, taxa)
 75 |   }
 76 | )
 77 | 
 78 | #' @rdname select_taxa-methods
 79 | setMethod("select_taxa", signature("phyloseq", "character"), 
 80 |   function(x, taxa, reorder = TRUE) {
 81 |     stopifnot(!anyDuplicated(taxa))
 82 |     stopifnot(all(taxa %in% taxa_names(x)))
 83 |     if (!reorder)
 84 |       taxa <- intersect(taxa_names(x), taxa)
 85 |     otu_table(x) <- select_taxa(otu_table(x), taxa)
 86 |     phyloseq:::index_reorder(x, index_type = "taxa")
 87 |   }
 88 | )
 89 | 
 90 | # orient_taxa -----------------------------------------------------------------
 91 | 
 92 | #' Orient a phyloseq object or otu table to have taxa as rows or as columns
 93 | #'
 94 | #' Puts the phyloseq or otu-table object `x` in the orientation (taxa as rows
 95 | #' or as columns) specified by `as`. This is useful when passing the otu table
 96 | #' on to functions that require the abundance matrix to have a specific
 97 | #' orientation and are unaware of the `taxa_are_rows(x)` property.
 98 | #'
 99 | #' @param x A phyloseq or otu-table object
100 | #' @param as The matrix dimension that is desired for taxa. Must be
101 | #'   "rows" for rows and "columns" or "cols" for columns.
102 | #'
103 | #' @export
104 | #'
105 | #' @examples
106 | #' data(soilrep)
107 | #' taxa_are_rows(soilrep)
108 | #' x <- soilrep %>% orient_taxa(as = "columns")
109 | #' taxa_are_rows(x)
110 | setGeneric("orient_taxa", 
111 |   function(x, as) standardGeneric("orient_taxa")
112 | )
113 | 
114 | orient_taxa_default <- function(x, as) {
115 |   stopifnot(as %in% c("rows", "columns", "cols"))
116 |   if (identical(as, "rows")) {
117 |     if (!taxa_are_rows(x))
118 |       x <- t(x)
119 |   } else {
120 |     if (taxa_are_rows(x))
121 |       x <- t(x)
122 |   }
123 |   x
124 | }
125 | 
126 | #' @rdname orient_taxa
127 | setMethod("orient_taxa", "otu_table", orient_taxa_default)
128 | 
129 | #' @rdname orient_taxa
130 | setMethod("orient_taxa", "phyloseq", orient_taxa_default)
131 | 


--------------------------------------------------------------------------------
/R/zzz.R:
--------------------------------------------------------------------------------
 1 | speedyseq_default_options <- list(
 2 |   speedyseq.psmelt_class = "data.frame"
 3 | )
 4 | 
 5 | .onLoad <- function(libname, pkgname) {
 6 |   op <- options()
 7 |   toset <- !(names(speedyseq_default_options) %in% names(op))
 8 |   if (any(toset)) 
 9 |     options(speedyseq_default_options[toset])
10 |   invisible()
11 | }
12 | 


--------------------------------------------------------------------------------
/README.Rmd:
--------------------------------------------------------------------------------
 1 | ---
 2 | output: github_document
 3 | ---
 4 | 
 5 | <!-- README.md is generated from README.Rmd. Please edit that file -->
 6 | 
 7 | ```{r setup, include = FALSE}
 8 | knitr::opts_chunk$set(
 9 |   collapse = TRUE,
10 |   comment = "#>",
11 |   fig.path = "man/figures/README-",
12 |   out.width = "100%"
13 | )
14 | ```
15 | # speedyseq
16 | 
17 | <!-- badges: start -->
18 | [![DOI](https://zenodo.org/badge/179732395.svg)](https://zenodo.org/badge/latestdoi/179732395)
19 | [![Travis build
20 | status](https://travis-ci.com/mikemc/speedyseq.svg?branch=main)](https://travis-ci.com/mikemc/speedyseq)
21 | [![Codecov test
22 | coverage](https://codecov.io/gh/mikemc/speedyseq/branch/main/graph/badge.svg)](https://codecov.io/gh/mikemc/speedyseq?branch=main)
23 | <!-- badges: end -->
24 | 
25 | Speedyseq is an R package for microbiome data analysis that extends the popular [phyloseq](https://joey711.github.io/phyloseq/) package. Speedyseq began with the limited goal of providing faster versions of phyloseq's plotting and taxonomic merging functions, but now contains a growing number of enhancements to phyloseq which I have found useful.
26 | 
27 | ## Installation
28 | 
29 | Install the current development version with the remotes package,
30 | ```{r, eval = FALSE}
31 | # install.packages("remotes")
32 | remotes::install_github("mikemc/speedyseq")
33 | ```
34 | 
35 | ## Usage
36 | 
37 | Method 1: Call speedyseq functions explicitly when you want to use speedyseq's version instead of phyloseq. This method ensures that you do not unintentionally call speedyseq's version of a phyloseq function.
38 | ```{r}
39 | library(phyloseq)
40 | data(GlobalPatterns)
41 | system.time(
42 |   # Calls phyloseq's psmelt
43 |   df1 <- psmelt(GlobalPatterns) # slow
44 | )
45 | system.time(
46 |   df2 <- speedyseq::psmelt(GlobalPatterns) # fast
47 | )
48 | dplyr::all_equal(df1, df2, ignore_row_order = TRUE)
49 | detach(package:phyloseq)
50 | ```
51 | 
52 | Method 2: Load speedyseq, which will load phyloseq and all speedyseq functions and cause calls to the overlapping function names to go to speedyseq by default.
53 | ```{r}
54 | library(speedyseq)
55 | data(GlobalPatterns)
56 | system.time(
57 |   ps1 <- phyloseq::tax_glom(GlobalPatterns, "Genus") # slow
58 | )
59 | system.time(
60 |   # Calls speedyseq's tax_glom
61 |   ps2 <- tax_glom(GlobalPatterns, "Genus") # fast
62 | )
63 | ```
64 | Loading speedyseq will also load the [magrittr](https://magrittr.tidyverse.org/) pipe (`%>%`) to allow pipe chains with phyloseq objects,
65 | ```{r}
66 | gp.filt.prop <- GlobalPatterns %>%
67 |   filter_taxa2(~ sum(. > 0) > 5) %>%
68 |   transform_sample_counts(~ . / sum(.))
69 | ```
70 | 
71 | ## Features
72 | 
73 | ### Faster implementations of phyloseq functions
74 | 
75 | * `psmelt()` and the plotting functions that use it: `plot_bar()`, `plot_heatmap()`, and `plot_tree()`.
76 | * The taxonomic merging functions `tax_glom()` and `tip_glom()`. Speedyseq's `tip_glom()` also has significantly lower memory usage.
77 | 
78 | These functions should generally function as drop-in replacements for phyloseq's versions, with additional arguments allowing for modified behavior. Differences in row order (for `psmelt()`) and taxon order (for `tax_glom()`) can occur; see [Changelog](https://mikemc.github.io/speedyseq/news/index.html) for details.
79 | 
80 | ### New taxonomic merging functions
81 | 
82 | * A general-purpose merging function `merge_taxa_vec()` that provides a vectorized version of phyloseq's `merge_taxa()` function.
83 | * A function `tree_glom()` that performs direct phylogenetic merging of taxa. This function provides an alternative to the indirect phylogenetic merging done by `tip_glom()` that is much faster and arguably more intuitive.
84 | 
85 | See the [Changelog](https://mikemc.github.io/speedyseq/news/index.html) for details and examples.
86 | 
87 | ### Enhancements and additions to other phyloseq functions
88 | 
89 | See the [online documentation](https://mikemc.github.io/speedyseq/reference/index.html) for an up-to-date list and usage information and the [Changelog](https://mikemc.github.io/speedyseq/news/index.html) for further information. 
90 | 


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/README.md:
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  1 | 
  2 | <!-- README.md is generated from README.Rmd. Please edit that file -->
  3 | 
  4 | # speedyseq
  5 | 
  6 | <!-- badges: start -->
  7 | 
  8 | [![DOI](https://zenodo.org/badge/179732395.svg)](https://zenodo.org/badge/latestdoi/179732395)
  9 | [![Travis build
 10 | status](https://travis-ci.com/mikemc/speedyseq.svg?branch=main)](https://travis-ci.com/mikemc/speedyseq)
 11 | [![Codecov test
 12 | coverage](https://codecov.io/gh/mikemc/speedyseq/branch/main/graph/badge.svg)](https://codecov.io/gh/mikemc/speedyseq?branch=main)
 13 | <!-- badges: end -->
 14 | 
 15 | Speedyseq is an R package for microbiome data analysis that extends the
 16 | popular [phyloseq](https://joey711.github.io/phyloseq/) package.
 17 | Speedyseq began with the limited goal of providing faster versions of
 18 | phyloseq’s plotting and taxonomic merging functions, but now contains a
 19 | growing number of enhancements to phyloseq which I have found useful.
 20 | 
 21 | ## Installation
 22 | 
 23 | Install the current development version with the remotes package,
 24 | 
 25 | ``` r
 26 | # install.packages("remotes")
 27 | remotes::install_github("mikemc/speedyseq")
 28 | ```
 29 | 
 30 | ## Usage
 31 | 
 32 | Method 1: Call speedyseq functions explicitly when you want to use
 33 | speedyseq’s version instead of phyloseq. This method ensures that you do
 34 | not unintentionally call speedyseq’s version of a phyloseq function.
 35 | 
 36 | ``` r
 37 | library(phyloseq)
 38 | data(GlobalPatterns)
 39 | system.time(
 40 |   # Calls phyloseq's psmelt
 41 |   df1 <- psmelt(GlobalPatterns) # slow
 42 | )
 43 | #>    user  system elapsed 
 44 | #>   6.320   0.063   6.390
 45 | system.time(
 46 |   df2 <- speedyseq::psmelt(GlobalPatterns) # fast
 47 | )
 48 | #>    user  system elapsed 
 49 | #>   0.344   0.004   0.245
 50 | dplyr::all_equal(df1, df2, ignore_row_order = TRUE)
 51 | #> [1] TRUE
 52 | detach(package:phyloseq)
 53 | ```
 54 | 
 55 | Method 2: Load speedyseq, which will load phyloseq and all speedyseq
 56 | functions and cause calls to the overlapping function names to go to
 57 | speedyseq by default.
 58 | 
 59 | ``` r
 60 | library(speedyseq)
 61 | #> Loading required package: phyloseq
 62 | #> 
 63 | #> Attaching package: 'speedyseq'
 64 | #> The following objects are masked from 'package:phyloseq':
 65 | #> 
 66 | #>     filter_taxa, plot_bar, plot_heatmap, plot_tree, psmelt, tax_glom, tip_glom,
 67 | #>     transform_sample_counts
 68 | data(GlobalPatterns)
 69 | system.time(
 70 |   ps1 <- phyloseq::tax_glom(GlobalPatterns, "Genus") # slow
 71 | )
 72 | #>    user  system elapsed 
 73 | #>  31.856   0.106  32.031
 74 | system.time(
 75 |   # Calls speedyseq's tax_glom
 76 |   ps2 <- tax_glom(GlobalPatterns, "Genus") # fast
 77 | )
 78 | #>    user  system elapsed 
 79 | #>   0.241   0.000   0.230
 80 | ```
 81 | 
 82 | Loading speedyseq will also load the
 83 | [magrittr](https://magrittr.tidyverse.org/) pipe (`%>%`) to allow pipe
 84 | chains with phyloseq objects,
 85 | 
 86 | ``` r
 87 | gp.filt.prop <- GlobalPatterns %>%
 88 |   filter_taxa2(~ sum(. > 0) > 5) %>%
 89 |   transform_sample_counts(~ . / sum(.))
 90 | ```
 91 | 
 92 | ## Features
 93 | 
 94 | ### Faster implementations of phyloseq functions
 95 | 
 96 | -   `psmelt()` and the plotting functions that use it: `plot_bar()`,
 97 |     `plot_heatmap()`, and `plot_tree()`.
 98 | -   The taxonomic merging functions `tax_glom()` and `tip_glom()`.
 99 |     Speedyseq’s `tip_glom()` also has significantly lower memory usage.
100 | 
101 | These functions should generally function as drop-in replacements for
102 | phyloseq’s versions, with additional arguments allowing for modified
103 | behavior. Differences in row order (for `psmelt()`) and taxon order (for
104 | `tax_glom()`) can occur; see
105 | [Changelog](https://mikemc.github.io/speedyseq/news/index.html) for
106 | details.
107 | 
108 | ### New taxonomic merging functions
109 | 
110 | -   A general-purpose merging function `merge_taxa_vec()` that provides
111 |     a vectorized version of phyloseq’s `merge_taxa()` function.
112 | -   A function `tree_glom()` that performs direct phylogenetic merging
113 |     of taxa. This function provides an alternative to the indirect
114 |     phylogenetic merging done by `tip_glom()` that is much faster and
115 |     arguably more intuitive.
116 | 
117 | See the [Changelog](https://mikemc.github.io/speedyseq/news/index.html)
118 | for details and examples.
119 | 
120 | ### Enhancements and additions to other phyloseq functions
121 | 
122 | See the [online
123 | documentation](https://mikemc.github.io/speedyseq/reference/index.html)
124 | for an up-to-date list and usage information and the
125 | [Changelog](https://mikemc.github.io/speedyseq/news/index.html) for
126 | further information.
127 | 


--------------------------------------------------------------------------------
/_pkgdown.yml:
--------------------------------------------------------------------------------
 1 | url: https://mikemc.github.io/speedyseq
 2 | reference:
 3 | - title: "Merge samples or taxa"
 4 | - contents: 
 5 |   - merge_samples2
 6 |   - merge_taxa_vec
 7 |   - ends_with("glom")
 8 | - title: "Transform or subset data"
 9 | - contents: 
10 |   - select-phyloseq
11 |   - relocate-phyloseq
12 |   - rename-phyloseq
13 |   - filter-phyloseq
14 |   - mutate-phyloseq
15 |   - join-phyloseq
16 |   - starts_with("filter_taxa")
17 |   - starts_with("transform")
18 |   - orient_taxa
19 | - title: "Coercion"
20 | - contents: 
21 |   - psmelt
22 |   - as_tibble-phyloseq
23 |   - tibble-constructors
24 | - title: "Plotting"
25 | - contents:
26 |   - starts_with("plot")
27 | - title: "Other"
28 | - contents:
29 |   - unique_or_na
30 |   - show,phyloseq-method
31 | 


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63 |   var content = hit._highlightResult.content;
64 |   if (content) {
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66 |   }
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 1 | pandoc: 2.14.0.2
 2 | pkgdown: 1.6.1
 3 | pkgdown_sha: ~
 4 | articles: {}
 5 | last_built: 2021-06-25T11:20Z
 6 | urls:
 7 |   reference: https://mikemc.github.io/speedyseq/reference
 8 |   article: https://mikemc.github.io/speedyseq/articles
 9 | 
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  9 | <title>Replace all values with NA upon seeing a bad value — bad_flush_right • speedyseq</title>
 10 | 
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 37 | <script src="../pkgdown.js"></script>
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 41 | 
 42 | <meta property="og:title" content="Replace all values with NA upon seeing a bad value — bad_flush_right" />
 43 | <meta property="og:description" content="Helper for merge_taxa_vec()" />
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 49 | <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
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106 | 
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109 |       </header>
110 | 
111 | <div class="row">
112 |   <div class="col-md-9 contents">
113 |     <div class="page-header">
114 |     <h1>Replace all values with NA upon seeing a bad value</h1>
115 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/merge_taxa_vec.R'><code>R/merge_taxa_vec.R</code></a></small>
116 |     <div class="hidden name"><code>bad_flush_right.Rd</code></div>
117 |     </div>
118 | 
119 |     <div class="ref-description">
120 |     <p>Helper for <code><a href='merge_taxa_vec.html'>merge_taxa_vec()</a></code></p>
121 |     </div>
122 | 
123 |     <pre class="usage"><span class='fu'>bad_flush_right</span><span class='op'>(</span><span class='va'>x</span>, bad <span class='op'>=</span> <span class='st'>"BAD"</span>, na_bad <span class='op'>=</span> <span class='cn'>FALSE</span>, k <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span><span class='op'>)</span></pre>
124 | 
125 |     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
126 |     <table class="ref-arguments">
127 |     <colgroup><col class="name" /><col class="desc" /></colgroup>
128 |     <tr>
129 |       <th>x</th>
130 |       <td><p>a vector</p></td>
131 |     </tr>
132 |     <tr>
133 |       <th>bad</th>
134 |       <td><p>the string representing a bad value</p></td>
135 |     </tr>
136 |     <tr>
137 |       <th>na_bad</th>
138 |       <td><p>whether NAs should also be treated as bad</p></td>
139 |     </tr>
140 |     <tr>
141 |       <th>k</th>
142 |       <td><p>the index to which values are flushed</p></td>
143 |     </tr>
144 |     </table>
145 | 
146 | 
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112 |   <div class="col-md-9 contents">
113 |     <div class="page-header">
114 |     <h1>Reduce a vector x to its unique value or the value of <code>bad</code></h1>
115 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/merge_taxa_vec.R'><code>R/merge_taxa_vec.R</code></a></small>
116 |     <div class="hidden name"><code>bad_or_unique.Rd</code></div>
117 |     </div>
118 | 
119 |     <div class="ref-description">
120 |     <p>Helper for <code><a href='merge_taxa_vec.html'>merge_taxa_vec()</a></code></p>
121 |     </div>
122 | 
123 |     <pre class="usage"><span class='fu'>bad_or_unique</span><span class='op'>(</span><span class='va'>x</span>, bad <span class='op'>=</span> <span class='st'>"BAD"</span><span class='op'>)</span></pre>
124 | 
125 |     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
126 |     <table class="ref-arguments">
127 |     <colgroup><col class="name" /><col class="desc" /></colgroup>
128 |     <tr>
129 |       <th>x</th>
130 |       <td><p>a vector</p></td>
131 |     </tr>
132 |     <tr>
133 |       <th>bad</th>
134 |       <td><p>the string representing a bad value</p></td>
135 |     </tr>
136 |     </table>
137 | 
138 | 
139 |   </div>
140 |   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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 10 | 
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 42 | <meta property="og:title" content="Subset samples using values in the sample data — filter_sample_data" />
 43 | <meta property="og:description" content="This function is a wrapper around dplyr::filter() that provides a
 44 | convenient way to subset samples using the sample_data(x)." />
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103 |       
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106 | </div><!--/.navbar -->
107 | 
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110 |       </header>
111 | 
112 | <div class="row">
113 |   <div class="col-md-9 contents">
114 |     <div class="page-header">
115 |     <h1>Subset samples using values in the sample data</h1>
116 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/dplyr-verbs.R'><code>R/dplyr-verbs.R</code></a></small>
117 |     <div class="hidden name"><code>filter_sample_data.Rd</code></div>
118 |     </div>
119 | 
120 |     <div class="ref-description">
121 |     <p>This function is a wrapper around <code><a href='https://dplyr.tidyverse.org/reference/filter.html'>dplyr::filter()</a></code> that provides a
122 | convenient way to subset samples using the <code><a href='https://rdrr.io/pkg/phyloseq/man/sample_data-methods.html'>sample_data(x)</a></code>.</p>
123 |     </div>
124 | 
125 |     <pre class="usage"><span class='fu'>filter_sample_data</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span>
126 | 
127 | <span class='co'># S4 method for phyloseq</span>
128 | <span class='fu'>filter_sample_data</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span>
129 | 
130 | <span class='co'># S4 method for sample_data</span>
131 | <span class='fu'>filter_sample_data</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span></pre>
132 | 
133 |     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
134 |     <table class="ref-arguments">
135 |     <colgroup><col class="name" /><col class="desc" /></colgroup>
136 |     <tr>
137 |       <th>x</th>
138 |       <td><p>A <code>phyloseq</code> or <code>sample_data</code> object</p></td>
139 |     </tr>
140 |     <tr>
141 |       <th>...</th>
142 |       <td><p>Expressions passed to <code><a href='https://dplyr.tidyverse.org/reference/filter.html'>dplyr::filter()</a></code></p></td>
143 |     </tr>
144 |     </table>
145 | 
146 | 
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159 | </div>
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116 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/import-phyloseq.R'><code>R/import-phyloseq.R</code></a></small>
117 |     <div class="hidden name"><code>import_phyloseq_functions.Rd</code></div>
118 |     </div>
119 | 
120 |     <div class="ref-description">
121 |     <p>Import all phyloseq functions for internal use, except those we are
122 | reimplementing.</p>
123 |     </div>
124 | 
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 43 | <meta property="og:description" content="Internal function used in merge_samples2() to merge variables. Note, owing
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115 |     <div class="page-header">
116 |     <h1>Merge groups of elements within a vector by a function</h1>
117 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/merge_samples.R'><code>R/merge_samples.R</code></a></small>
118 |     <div class="hidden name"><code>merge_groups.Rd</code></div>
119 |     </div>
120 | 
121 |     <div class="ref-description">
122 |     <p>Internal function used in <code><a href='merge_samples2.html'>merge_samples2()</a></code> to merge variables. Note, owing
123 | to the use of <code><a href='https://rdrr.io/r/base/split.html'>split()</a></code>, the merged elements in the new vector will be
124 | reordered according to <code>group</code>.</p>
125 |     </div>
126 | 
127 |     <pre class="usage"><span class='fu'>merge_groups</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>group</span>, f <span class='op'>=</span> <span class='va'>unique_or_na</span><span class='op'>)</span></pre>
128 | 
129 |     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
130 |     <table class="ref-arguments">
131 |     <colgroup><col class="name" /><col class="desc" /></colgroup>
132 |     <tr>
133 |       <th>x</th>
134 |       <td><p>A vector whose elements will be merged.</p></td>
135 |     </tr>
136 |     <tr>
137 |       <th>group</th>
138 |       <td><p>A vector such that <code><a href='https://rdrr.io/r/base/factor.html'>as.factor(group)</a></code> defines the grouping.</p></td>
139 |     </tr>
140 |     <tr>
141 |       <th>f</th>
142 |       <td><p>A function that, when applied to a subvector of x, returns a single
143 | value. Can also be a formula as interpretted by <code><a href='https://purrr.tidyverse.org/reference/as_mapper.html'>purrr::as_mapper()</a></code>.</p></td>
144 |     </tr>
145 |     </table>
146 | 
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113 |     <div class="page-header">
114 |     <h1>Pseudo-merge taxa in groups</h1>
115 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/merge_taxa_vec.R'><code>R/merge_taxa_vec.R</code></a></small>
116 |     <div class="hidden name"><code>merge_taxa_vec_pseudo.Rd</code></div>
117 |     </div>
118 | 
119 |     <div class="ref-description">
120 |     <p>Returns <code>x</code> pruned to the first taxon of each group defined in <code>group</code>.</p>
121 |     </div>
122 | 
123 |     <pre class="usage"><span class='fu'>merge_taxa_vec_pseudo</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>group</span>, reorder <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span></pre>
124 | 
125 |     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
126 |     <table class="ref-arguments">
127 |     <colgroup><col class="name" /><col class="desc" /></colgroup>
128 |     <tr>
129 |       <th>x</th>
130 |       <td><p>a phyloseq component-class object</p></td>
131 |     </tr>
132 |     <tr>
133 |       <th>group</th>
134 |       <td><p>a vector with one element for each taxon in <code>x</code> that defines
135 | the new groups</p></td>
136 |     </tr>
137 |     </table>
138 | 
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 71 |         <span class="icon-bar"></span>
 72 |       </button>
 73 |       <span class="navbar-brand">
 74 |         <a class="navbar-link" href="../index.html">speedyseq</a>
 75 |         <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.5.3.9018</span>
 76 |       </span>
 77 |     </div>
 78 | 
 79 |     <div id="navbar" class="navbar-collapse collapse">
 80 |       <ul class="nav navbar-nav">
 81 |         <li>
 82 |   <a href="../index.html">
 83 |     <span class="fas fa-home fa-lg"></span>
 84 |      
 85 |   </a>
 86 | </li>
 87 | <li>
 88 |   <a href="../reference/index.html">Reference</a>
 89 | </li>
 90 | <li>
 91 |   <a href="../news/index.html">Changelog</a>
 92 | </li>
 93 |       </ul>
 94 |       <ul class="nav navbar-nav navbar-right">
 95 |         <li>
 96 |   <a href="https://github.com/mikemc/speedyseq/">
 97 |     <span class="fab fa-github fa-lg"></span>
 98 |      
 99 |   </a>
100 | </li>
101 |       </ul>
102 |       
103 |     </div><!--/.nav-collapse -->
104 |   </div><!--/.container -->
105 | </div><!--/.navbar -->
106 | 
107 |       
108 | 
109 |       </header>
110 | 
111 | <div class="row">
112 |   <div class="col-md-9 contents">
113 |     <div class="page-header">
114 |     <h1>One-line summaries of phyloseq components</h1>
115 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/printing.R'><code>R/printing.R</code></a></small>
116 |     <div class="hidden name"><code>oneline.Rd</code></div>
117 |     </div>
118 | 
119 |     <div class="ref-description">
120 |     <p>One-line summaries of phyloseq components</p>
121 |     </div>
122 | 
123 |     <pre class="usage"><span class='fu'>oneline</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre>
124 | 
125 |     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
126 |     <table class="ref-arguments">
127 |     <colgroup><col class="name" /><col class="desc" /></colgroup>
128 |     <tr>
129 |       <th>x</th>
130 |       <td><p>A phyloseq component object</p></td>
131 |     </tr>
132 |     </table>
133 | 
134 | 
135 |   </div>
136 |   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
137 |     <nav id="toc" data-toggle="toc" class="sticky-top">
138 |       <h2 data-toc-skip>Contents</h2>
139 |     </nav>
140 |   </div>
141 | </div>
142 | 
143 | 
144 |       <footer>
145 |       <div class="copyright">
146 |   <p>Developed by Michael McLaren.</p>
147 | </div>
148 | 
149 | <div class="pkgdown">
150 |   <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
151 | </div>
152 | 
153 |       </footer>
154 |    </div>
155 | 
156 |   
157 | 
158 | 
159 |   </body>
160 | </html>
161 | 
162 | 
163 | 


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 42 | <meta property="og:title" content="Pipe operator — %&gt;%" />
 43 | <meta property="og:description" content="See magrittr::%&amp;gt;% for details." />
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 72 |       </button>
 73 |       <span class="navbar-brand">
 74 |         <a class="navbar-link" href="../index.html">speedyseq</a>
 75 |         <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.5.3.9018</span>
 76 |       </span>
 77 |     </div>
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 88 |   <a href="../reference/index.html">Reference</a>
 89 | </li>
 90 | <li>
 91 |   <a href="../news/index.html">Changelog</a>
 92 | </li>
 93 |       </ul>
 94 |       <ul class="nav navbar-nav navbar-right">
 95 |         <li>
 96 |   <a href="https://github.com/mikemc/speedyseq/">
 97 |     <span class="fab fa-github fa-lg"></span>
 98 |      
 99 |   </a>
100 | </li>
101 |       </ul>
102 |       
103 |     </div><!--/.nav-collapse -->
104 |   </div><!--/.container -->
105 | </div><!--/.navbar -->
106 | 
107 |       
108 | 
109 |       </header>
110 | 
111 | <div class="row">
112 |   <div class="col-md-9 contents">
113 |     <div class="page-header">
114 |     <h1>Pipe operator</h1>
115 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/utils-pipe.R'><code>R/utils-pipe.R</code></a></small>
116 |     <div class="hidden name"><code>pipe.Rd</code></div>
117 |     </div>
118 | 
119 |     <div class="ref-description">
120 |     <p>See <code>magrittr::%&gt;%</code> for details.</p>
121 |     </div>
122 | 
123 |     <pre class="usage"><span class='va'>lhs</span> <span class='op'>%&gt;%</span> <span class='va'>rhs</span></pre>
124 | 
125 | 
126 | 
127 |   </div>
128 |   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
129 |     <nav id="toc" data-toggle="toc" class="sticky-top">
130 |       <h2 data-toc-skip>Contents</h2>
131 |     </nav>
132 |   </div>
133 | </div>
134 | 
135 | 
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137 |       <div class="copyright">
138 |   <p>Developed by Michael McLaren.</p>
139 | </div>
140 | 
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142 |   <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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151 |   </body>
152 | </html>
153 | 
154 | 
155 | 


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118 |     <div class="page-header">
119 |     <h1>Objects exported from other packages</h1>
120 |     <small class="dont-index">Source: <a href='https://github.com/mikemc/speedyseq/blob/master/R/as_tibble.R'><code>R/as_tibble.R</code></a></small>
121 |     <div class="hidden name"><code>reexports.Rd</code></div>
122 |     </div>
123 | 
124 |     <div class="ref-description">
125 |     <p>These objects are imported from other packages. Follow the links
126 | below to see their documentation.</p>
127 | <dl>
128 |   <dt>tibble</dt><dd><p><code><a href='https://tibble.tidyverse.org/reference/as_tibble.html'>as_tibble</a></code></p></dd>
129 | 
130 | </dl>
131 |     </div>
132 | 
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 11 |   </url>
 12 |   <url>
 13 |     <loc>https://mikemc.github.io/speedyseq/reference/bad_or_unique.html</loc>
 14 |   </url>
 15 |   <url>
 16 |     <loc>https://mikemc.github.io/speedyseq/reference/filter-phyloseq.html</loc>
 17 |   </url>
 18 |   <url>
 19 |     <loc>https://mikemc.github.io/speedyseq/reference/filter_taxa.html</loc>
 20 |   </url>
 21 |   <url>
 22 |     <loc>https://mikemc.github.io/speedyseq/reference/import_phyloseq_functions.html</loc>
 23 |   </url>
 24 |   <url>
 25 |     <loc>https://mikemc.github.io/speedyseq/reference/join-phyloseq.html</loc>
 26 |   </url>
 27 |   <url>
 28 |     <loc>https://mikemc.github.io/speedyseq/reference/merge_groups.html</loc>
 29 |   </url>
 30 |   <url>
 31 |     <loc>https://mikemc.github.io/speedyseq/reference/merge_samples2.html</loc>
 32 |   </url>
 33 |   <url>
 34 |     <loc>https://mikemc.github.io/speedyseq/reference/merge_taxa_vec.html</loc>
 35 |   </url>
 36 |   <url>
 37 |     <loc>https://mikemc.github.io/speedyseq/reference/merge_taxa_vec_pseudo.html</loc>
 38 |   </url>
 39 |   <url>
 40 |     <loc>https://mikemc.github.io/speedyseq/reference/mutate-phyloseq.html</loc>
 41 |   </url>
 42 |   <url>
 43 |     <loc>https://mikemc.github.io/speedyseq/reference/oneline.html</loc>
 44 |   </url>
 45 |   <url>
 46 |     <loc>https://mikemc.github.io/speedyseq/reference/orient_taxa.html</loc>
 47 |   </url>
 48 |   <url>
 49 |     <loc>https://mikemc.github.io/speedyseq/reference/pipe.html</loc>
 50 |   </url>
 51 |   <url>
 52 |     <loc>https://mikemc.github.io/speedyseq/reference/plot_bar.html</loc>
 53 |   </url>
 54 |   <url>
 55 |     <loc>https://mikemc.github.io/speedyseq/reference/plot_heatmap.html</loc>
 56 |   </url>
 57 |   <url>
 58 |     <loc>https://mikemc.github.io/speedyseq/reference/plot_tree.html</loc>
 59 |   </url>
 60 |   <url>
 61 |     <loc>https://mikemc.github.io/speedyseq/reference/psmelt.html</loc>
 62 |   </url>
 63 |   <url>
 64 |     <loc>https://mikemc.github.io/speedyseq/reference/reexports.html</loc>
 65 |   </url>
 66 |   <url>
 67 |     <loc>https://mikemc.github.io/speedyseq/reference/relocate-phyloseq.html</loc>
 68 |   </url>
 69 |   <url>
 70 |     <loc>https://mikemc.github.io/speedyseq/reference/rename-phyloseq.html</loc>
 71 |   </url>
 72 |   <url>
 73 |     <loc>https://mikemc.github.io/speedyseq/reference/sample_data_stable.html</loc>
 74 |   </url>
 75 |   <url>
 76 |     <loc>https://mikemc.github.io/speedyseq/reference/select-phyloseq.html</loc>
 77 |   </url>
 78 |   <url>
 79 |     <loc>https://mikemc.github.io/speedyseq/reference/select_taxa-methods.html</loc>
 80 |   </url>
 81 |   <url>
 82 |     <loc>https://mikemc.github.io/speedyseq/reference/show-methods.html</loc>
 83 |   </url>
 84 |   <url>
 85 |     <loc>https://mikemc.github.io/speedyseq/reference/tax_glom.html</loc>
 86 |   </url>
 87 |   <url>
 88 |     <loc>https://mikemc.github.io/speedyseq/reference/tibble-constructors.html</loc>
 89 |   </url>
 90 |   <url>
 91 |     <loc>https://mikemc.github.io/speedyseq/reference/tibble_print.html</loc>
 92 |   </url>
 93 |   <url>
 94 |     <loc>https://mikemc.github.io/speedyseq/reference/tip_glom.html</loc>
 95 |   </url>
 96 |   <url>
 97 |     <loc>https://mikemc.github.io/speedyseq/reference/transform_sample_counts.html</loc>
 98 |   </url>
 99 |   <url>
100 |     <loc>https://mikemc.github.io/speedyseq/reference/tree_glom.html</loc>
101 |   </url>
102 |   <url>
103 |     <loc>https://mikemc.github.io/speedyseq/reference/unique_or_na.html</loc>
104 |   </url>
105 | </urlset>
106 | 


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/man/as_tibble-phyloseq.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/as_tibble.R
 3 | \name{as_tibble-phyloseq}
 4 | \alias{as_tibble-phyloseq}
 5 | \alias{as_tibble.otu_table}
 6 | \alias{as_tibble.sample_data}
 7 | \alias{as_tibble.taxonomyTable}
 8 | \alias{as_tibble.XStringSet}
 9 | \alias{as_tibble.phyloseq}
10 | \title{Coerce phyloseq objects to tibble data frames}
11 | \usage{
12 | \method{as_tibble}{otu_table}(x, pivot = TRUE, .name_repair = base::make.unique)
13 | 
14 | \method{as_tibble}{sample_data}(x, .name_repair = base::make.unique)
15 | 
16 | \method{as_tibble}{taxonomyTable}(x, .name_repair = base::make.unique)
17 | 
18 | \method{as_tibble}{XStringSet}(x)
19 | 
20 | \method{as_tibble}{phyloseq}(x, tax = TRUE, ref = FALSE, .name_repair = base::make.unique)
21 | }
22 | \arguments{
23 | \item{x}{A phyloseq object or component.}
24 | 
25 | \item{pivot}{Whether to pivot the otu table to long format.}
26 | 
27 | \item{.name_repair}{Function to repair names in the case of conflicts.}
28 | 
29 | \item{tax}{Whether to include taxonomy data.}
30 | 
31 | \item{ref}{Whether to include reference sequences.}
32 | }
33 | \value{
34 | A tibble (tbl_df)
35 | }
36 | \description{
37 | These functions extend the \code{as_tibble()} function defined by the tibble
38 | package to work on phyloseq objects or phyloseq-component objects.
39 | }
40 | \details{
41 | Tibbles (tbl_df objects) do not support rownames; the taxa and sample names
42 | in the returned tibbles will always be stored in the first or second
43 | columns. The names ".otu", ".sample", ".abundance", and ".sequence" are
44 | special column names reserved for the otu/taxa names, sample names,
45 | abundances, and reference sequences.
46 | }
47 | \examples{
48 | library(tibble) # for as_tibble() and glimpse()
49 | 
50 | data(GlobalPatterns)
51 | 
52 | # Subset to 1/100 of the original taxa to speed operations
53 | ps <- GlobalPatterns \%>\%
54 |   filter_tax_table(dplyr::row_number() \%\% 100 == 1)
55 | 
56 | # On phyloseq objects, as_tibble is similar to psmelt()
57 | psmelt(ps) \%>\% glimpse
58 | as_tibble(ps) \%>\% glimpse
59 | 
60 | # By default, the otu_table method provides a tibble in long-format like
61 | # psmelt and the phyloseq method
62 | otu_table(ps) \%>\% as_tibble \%>\% glimpse
63 | 
64 | # Sample data and taxonomy tables produced by as_tibble can be converted
65 | # back into their respective phyloseq objects using speedyseq's tbl_df
66 | # constructors
67 | sample_data(ps) <- sample_data(ps) \%>\%
68 |   as_tibble \%>\%
69 |   dplyr::mutate(sample_sum = sample_sums(ps)) \%>\%
70 |   sample_data
71 | sample_data(ps) \%>\% glimpse
72 | }
73 | 


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/man/bad_flush_right.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_taxa_vec.R
 3 | \name{bad_flush_right}
 4 | \alias{bad_flush_right}
 5 | \title{Replace all values with NA upon seeing a bad value}
 6 | \usage{
 7 | bad_flush_right(x, bad = "BAD", na_bad = FALSE, k = length(x))
 8 | }
 9 | \arguments{
10 | \item{x}{a vector}
11 | 
12 | \item{bad}{the string representing a bad value}
13 | 
14 | \item{na_bad}{whether NAs should also be treated as bad}
15 | 
16 | \item{k}{the index to which values are flushed}
17 | }
18 | \description{
19 | Helper for \code{merge_taxa_vec()}
20 | }
21 | \keyword{internal}
22 | 


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/man/bad_or_unique.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_taxa_vec.R
 3 | \name{bad_or_unique}
 4 | \alias{bad_or_unique}
 5 | \title{Reduce a vector x to its unique value or the value of \code{bad}}
 6 | \usage{
 7 | bad_or_unique(x, bad = "BAD")
 8 | }
 9 | \arguments{
10 | \item{x}{a vector}
11 | 
12 | \item{bad}{the string representing a bad value}
13 | }
14 | \description{
15 | Helper for \code{merge_taxa_vec()}
16 | }
17 | \keyword{internal}
18 | 


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/man/filter-phyloseq.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/dplyr-verbs.R
 3 | \name{filter-phyloseq}
 4 | \alias{filter-phyloseq}
 5 | \alias{filter_tax_table}
 6 | \alias{filter_tax_table,phyloseq-method}
 7 | \alias{filter_tax_table,taxonomyTable-method}
 8 | \alias{filter_sample_data}
 9 | \alias{filter_sample_data,phyloseq-method}
10 | \alias{filter_sample_data,sample_data-method}
11 | \title{Subset rows in the taxonomy table or sample data using column values}
12 | \usage{
13 | filter_tax_table(x, ...)
14 | 
15 | \S4method{filter_tax_table}{phyloseq}(x, ...)
16 | 
17 | \S4method{filter_tax_table}{taxonomyTable}(x, ...)
18 | 
19 | filter_sample_data(x, ...)
20 | 
21 | \S4method{filter_sample_data}{phyloseq}(x, ...)
22 | 
23 | \S4method{filter_sample_data}{sample_data}(x, ...)
24 | }
25 | \arguments{
26 | \item{x}{A \code{phyloseq}, \code{taxonomyTable}, or \code{sample_data} object}
27 | 
28 | \item{...}{Expressions passed to \code{dplyr::filter()}}
29 | }
30 | \description{
31 | These functions are wrappers around \code{dplyr::filter()} that make it possible
32 | to subset rows (corresponding to taxa or samples) using column values in the
33 | taxonomy table or sample data.
34 | }
35 | \examples{
36 | data(GlobalPatterns)
37 | 
38 | ps <- GlobalPatterns \%>\%
39 |   filter_tax_table(Kingdom == "Bacteria") \%>\%
40 |   filter_sample_data(SampleType \%in\% c("Feces", "Soil"))
41 | }
42 | 


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/man/filter_taxa.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/transform-filter.R
 3 | \name{filter_taxa}
 4 | \alias{filter_taxa}
 5 | \alias{filter_taxa2}
 6 | \title{Filter taxa based on across-sample OTU abundance criteria}
 7 | \usage{
 8 | filter_taxa(physeq, fun, prune = FALSE)
 9 | 
10 | filter_taxa2(physeq, fun)
11 | }
12 | \arguments{
13 | \item{physeq}{\code{\link[=phyloseq-class]{phyloseq-class()}} or \code{\link[ape:read.tree]{ape::phylo()}}.}
14 | 
15 | \item{fun}{A function or formula that can be converted to a function by
16 | \code{\link[purrr:as_mapper]{purrr::as_mapper()}}}
17 | 
18 | \item{prune}{A logical. If \code{FALSE}, then this function returns a logical
19 | vector specifying the taxa that passed the filter; if \code{TRUE}, then this
20 | function returns the pruned phyloseq object.}
21 | }
22 | \description{
23 | Variations of \code{\link[phyloseq:filter_taxa]{phyloseq::filter_taxa()}} that allows a purrr-style anonymous
24 | function.
25 | }
26 | \details{
27 | \code{filter_taxa()} simply calls \code{\link[purrr:as_mapper]{purrr::as_mapper()}} on \code{fun} and passes the
28 | resulting function on to \code{\link[phyloseq:filter_taxa]{phyloseq::filter_taxa()}}. \code{filter_taxa2()} also
29 | sets \code{prune = TRUE}, which is convenient when passing a phyloseq object in a
30 | pipe chain (see example).
31 | }
32 | \section{Functions}{
33 | \itemize{
34 | \item \code{filter_taxa2()}: Sets \code{prune = TRUE}
35 | 
36 | }}
37 | \examples{
38 | data(GlobalPatterns)
39 | # Filter low prevalence taxa and then convert to proportions
40 | gp.prop <- GlobalPatterns \%>\%
41 |   filter_taxa2(~ sum(. > 0) > 5) \%>\%
42 |   transform_sample_counts(~ . / sum(.))
43 | }
44 | \seealso{
45 | \code{\link[phyloseq:filter_taxa]{phyloseq::filter_taxa()}}
46 | \code{\link[purrr:as_mapper]{purrr::as_mapper()}}
47 | }
48 | 


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/man/import_phyloseq_functions.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/import-phyloseq.R
 3 | \name{import_phyloseq_functions}
 4 | \alias{import_phyloseq_functions}
 5 | \title{Import phyloseq functions}
 6 | \description{
 7 | Import all phyloseq functions for internal use, except those we are
 8 | reimplementing.
 9 | }
10 | 


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/man/join-phyloseq.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/dplyr-joins.R
 3 | \name{join-phyloseq}
 4 | \alias{join-phyloseq}
 5 | \alias{left_join_tax_table}
 6 | \alias{left_join_tax_table,phyloseq-method}
 7 | \alias{left_join_tax_table,taxonomyTable-method}
 8 | \alias{left_join_sample_data}
 9 | \alias{left_join_sample_data,phyloseq-method}
10 | \alias{left_join_sample_data,sample_data-method}
11 | \alias{inner_join_tax_table}
12 | \alias{inner_join_tax_table,phyloseq-method}
13 | \alias{inner_join_tax_table,taxonomyTable-method}
14 | \alias{inner_join_sample_data}
15 | \alias{inner_join_sample_data,phyloseq-method}
16 | \alias{inner_join_sample_data,sample_data-method}
17 | \title{Mutating joins of the taxonomy table or sample data}
18 | \usage{
19 | left_join_tax_table(x, ...)
20 | 
21 | \S4method{left_join_tax_table}{phyloseq}(x, ...)
22 | 
23 | \S4method{left_join_tax_table}{taxonomyTable}(x, ...)
24 | 
25 | left_join_sample_data(x, ...)
26 | 
27 | \S4method{left_join_sample_data}{phyloseq}(x, ...)
28 | 
29 | \S4method{left_join_sample_data}{sample_data}(x, ...)
30 | 
31 | inner_join_tax_table(x, ...)
32 | 
33 | \S4method{inner_join_tax_table}{phyloseq}(x, ...)
34 | 
35 | \S4method{inner_join_tax_table}{taxonomyTable}(x, ...)
36 | 
37 | inner_join_sample_data(x, ...)
38 | 
39 | \S4method{inner_join_sample_data}{phyloseq}(x, ...)
40 | 
41 | \S4method{inner_join_sample_data}{sample_data}(x, ...)
42 | }
43 | \arguments{
44 | \item{x}{A \code{phyloseq}, \code{taxonomyTable}, or \code{sample_data} object}
45 | 
46 | \item{...}{Arguments passed to respective dplyr joining operation}
47 | }
48 | \value{
49 | An object of the same type as \code{x}, with added columns
50 | }
51 | \description{
52 | \ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
53 | 
54 | These functions are wrappers around dplyr joining operations (see
55 | \code{dplyr::mutate-joins}) whose first (\code{x}) argument is either the taxonomy
56 | table or sample data.
57 | }
58 | \details{
59 | When joining by taxonomy table, the .otu column name can be used to match
60 | taxa names.
61 | When joining by sample data, the .sample column name can be used to match
62 | sample names.
63 | }
64 | \examples{
65 | library(tibble)
66 | 
67 | data(GlobalPatterns)
68 | 
69 | GlobalPatterns \%>\% sample_variables
70 | ps1 <- GlobalPatterns \%>\%
71 |   select_sample_data(!contains("Barcode"))
72 | y <- GlobalPatterns \%>\%
73 |   sample_data \%>\%
74 |   select_sample_data(contains("Barcode")) \%>\%
75 |   as_tibble
76 | ps2 <- ps1 \%>\% left_join_sample_data(y, by = ".sample")
77 | ps2 \%>\% sample_variables
78 | }
79 | \seealso{
80 | \code{\link[dplyr:mutate-joins]{dplyr::left_join()}}
81 | \code{\link[dplyr:mutate-joins]{dplyr::inner_join()}}
82 | }
83 | 


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/man/merge_groups.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_samples.R
 3 | \name{merge_groups}
 4 | \alias{merge_groups}
 5 | \title{Merge groups of elements within a vector by a function}
 6 | \usage{
 7 | merge_groups(x, group, f = unique_or_na)
 8 | }
 9 | \arguments{
10 | \item{x}{A vector whose elements will be merged.}
11 | 
12 | \item{group}{A vector such that \code{as.factor(group)} defines the grouping.}
13 | 
14 | \item{f}{A function that, when applied to a subvector of x, returns a single
15 | value. Can also be a formula as interpretted by \code{purrr::as_mapper()}.}
16 | }
17 | \description{
18 | Internal function used in \code{merge_samples2()} to merge variables. Note, owing
19 | to the use of \code{split()}, the merged elements in the new vector will be
20 | reordered according to \code{group}.
21 | }
22 | \keyword{internal}
23 | 


--------------------------------------------------------------------------------
/man/merge_samples2.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_samples.R
 3 | \name{merge_samples2}
 4 | \alias{merge_samples2}
 5 | \alias{merge_samples2,phyloseq-method}
 6 | \alias{merge_samples2,otu_table-method}
 7 | \alias{merge_samples2,sample_data-method}
 8 | \title{Merge samples by a sample variable or factor}
 9 | \usage{
10 | merge_samples2(x, group, fun_otu = sum, funs = list(), reorder = FALSE)
11 | 
12 | \S4method{merge_samples2}{phyloseq}(x, group, fun_otu = sum, funs = list(), reorder = FALSE)
13 | 
14 | \S4method{merge_samples2}{otu_table}(x, group, fun_otu = sum, reorder = FALSE)
15 | 
16 | \S4method{merge_samples2}{sample_data}(x, group, funs = list(), reorder = FALSE)
17 | }
18 | \arguments{
19 | \item{x}{A \code{phyloseq}, \code{otu_table}, or \code{sample_data} object}
20 | 
21 | \item{group}{A sample variable or a vector of length \code{nsamples(x)} defining
22 | the sample grouping. A vector must be supplied if x is an otu_table}
23 | 
24 | \item{fun_otu}{Function for combining abundances in the otu table; default
25 | is \code{sum}. Can be a formula to be converted to a function by
26 | \code{\link[purrr:as_mapper]{purrr::as_mapper()}}}
27 | 
28 | \item{funs}{Named list of merge functions for sample variables; default is
29 | \code{unique_or_na}}
30 | 
31 | \item{reorder}{Logical specifying whether to reorder the new (merged)
32 | samples by name}
33 | }
34 | \description{
35 | This function provides an alternative to \code{phyloseq::merge_samples()} that
36 | better handles sample variables of different types, especially categorical
37 | sample variables. It combines the samples in \code{x} defined by the sample
38 | variable or factor \code{group} by summing the abundances in \code{otu_table(x)} and
39 | combines sample variables by the summary functions in \code{funs}. The default
40 | summary function, \code{unique_or_na()}, collapses the values within a group to a
41 | single unique value if it exists and otherwise returns NA. The new (merged)
42 | samples are named by the values in \code{group}.
43 | }
44 | \examples{
45 | data(enterotype)
46 | 
47 | # Merge samples with the same project and clinical status
48 | ps <- enterotype 
49 | sample_data(ps) <- sample_data(ps) \%>\%
50 |   transform(Project.ClinicalStatus = Project:ClinicalStatus)
51 | sample_data(ps) \%>\% head
52 | ps0 <- merge_samples2(ps, "Project.ClinicalStatus",
53 |   fun_otu = mean,
54 |   funs = list(Age = mean)
55 | )
56 | sample_data(ps0) \%>\% head
57 | }
58 | 


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/man/merge_taxa_vec.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_taxa_vec.R
 3 | \name{merge_taxa_vec}
 4 | \alias{merge_taxa_vec}
 5 | \alias{merge_taxa_vec,phyloseq-method}
 6 | \alias{merge_taxa_vec,otu_table-method}
 7 | \alias{merge_taxa_vec,taxonomyTable-method}
 8 | \alias{merge_taxa_vec,phylo-method}
 9 | \alias{merge_taxa_vec,XStringSet-method}
10 | \title{Merge taxa in groups (vectorized version)}
11 | \usage{
12 | merge_taxa_vec(x, group, reorder = FALSE, tax_adjust = 1L)
13 | 
14 | \S4method{merge_taxa_vec}{phyloseq}(x, group, reorder = FALSE, tax_adjust = 1L)
15 | 
16 | \S4method{merge_taxa_vec}{otu_table}(x, group, reorder = FALSE)
17 | 
18 | \S4method{merge_taxa_vec}{taxonomyTable}(x, group, reorder = FALSE, tax_adjust = 1L)
19 | 
20 | \S4method{merge_taxa_vec}{phylo}(x, group)
21 | 
22 | \S4method{merge_taxa_vec}{XStringSet}(x, group, reorder = FALSE)
23 | }
24 | \arguments{
25 | \item{x}{A phyloseq object or component object}
26 | 
27 | \item{group}{A vector with one element for each taxon in \code{physeq} that
28 | defines the new groups. see \code{base::rowsum()}.}
29 | 
30 | \item{reorder}{Logical specifying whether to reorder the taxa by their
31 | \code{group} values. Ignored if \code{x} has (or is) a phylogenetic tree.}
32 | 
33 | \item{tax_adjust}{0: no adjustment; 1: phyloseq-compatible adjustment; 2:
34 | conservative adjustment}
35 | }
36 | \description{
37 | Merge taxa in \code{x} into a smaller set of taxa defined by the vector \code{group}.
38 | Taxa whose value in \code{group} is NA will be dropped. New taxa will be named
39 | according to the most abundant taxon in each group (\code{phyloseq} and
40 | \code{otu_table} objects) or the first taxon in each group (all other phyloseq
41 | component objects).
42 | }
43 | \details{
44 | If \code{x} is a phyloseq object with a phylogenetic tree, then the new taxa will
45 | be ordered as they are in the tree. Otherwise, the taxa order can be
46 | controlled by the \code{reorder} argument, which behaves like the \code{reorder}
47 | argument in \code{\link[base:rowsum]{base::rowsum()}}. \code{reorder = FALSE} will keep taxa in
48 | the original order determined by when the member of each group first appears
49 | in \code{taxa_names(x)}; \code{reorder = TRUE} will order new taxa according to their
50 | corresponding value in \code{group}.
51 | 
52 | The \code{tax_adjust} argument controls the handling of taxonomic disagreements
53 | within groups. Setting \code{tax_adjust == 0} causes no adjustment; the taxonomy
54 | of the new group is set to the archetype taxon (see below). Otherwise,
55 | disagreements within a group at a given rank cause the values at lower ranks
56 | to be set to \code{NA}. If \code{tax_adjust == 1} (the default), then a rank where all
57 | taxa in the group are already NA is not counted as a disagreement, and lower
58 | ranks may be kept if the taxa agree. This corresponds to the original
59 | phyloseq behavior. If \code{tax_adjust == 2}, then these NAs are treated as a
60 | disagreement; all ranks are set to NA after the first disagreement or NA.
61 | }
62 | \examples{
63 | \dontrun{
64 | # Create 97\% OTUs from a phyloseq object with reference sequences
65 | # Assume ps is a phyloseq object with refseq slot
66 | dna <- refseq(ps)
67 | nproc <- 1 # Increase to use multiple processors
68 | aln <- DECIPHER::AlignSeqs(dna, processors = nproc)
69 | d <- DECIPHER::DistanceMatrix(aln, processors = nproc)
70 | clusters <- DECIPHER::IdClusters(
71 |   d, 
72 |   method = "complete",
73 |   cutoff = 0.03,
74 |   processors = nproc
75 | )
76 | ps0 <- merge_taxa_vec(
77 |   ps, 
78 |   group = clusters$cluster,
79 | )
80 | }
81 | }
82 | \seealso{
83 | Merging functions that use this function: \code{\link[=tax_glom]{tax_glom()}}, \code{\link[=tip_glom]{tip_glom()}},
84 | \code{\link[=tree_glom]{tree_glom()}}
85 | 
86 | \code{\link[base:rowsum]{base::rowsum()}}
87 | 
88 | \code{\link[phyloseq:merge_taxa-methods]{phyloseq::merge_taxa()}}
89 | }
90 | 


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/man/merge_taxa_vec_pseudo.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_taxa_vec.R
 3 | \name{merge_taxa_vec_pseudo}
 4 | \alias{merge_taxa_vec_pseudo}
 5 | \title{Pseudo-merge taxa in groups}
 6 | \usage{
 7 | merge_taxa_vec_pseudo(x, group, reorder = FALSE)
 8 | }
 9 | \arguments{
10 | \item{x}{a phyloseq component-class object}
11 | 
12 | \item{group}{a vector with one element for each taxon in \code{x} that defines
13 | the new groups}
14 | }
15 | \description{
16 | Returns \code{x} pruned to the first taxon of each group defined in \code{group}.
17 | }
18 | \keyword{internal}
19 | 


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/man/mutate-phyloseq.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/dplyr-verbs.R
 3 | \name{mutate-phyloseq}
 4 | \alias{mutate-phyloseq}
 5 | \alias{mutate_tax_table}
 6 | \alias{mutate_tax_table,taxonomyTable-method}
 7 | \alias{mutate_tax_table,phyloseq-method}
 8 | \alias{transmute_tax_table}
 9 | \alias{transmute_tax_table,taxonomyTable-method}
10 | \alias{transmute_tax_table,phyloseq-method}
11 | \alias{mutate_sample_data}
12 | \alias{mutate_sample_data,sample_data-method}
13 | \alias{mutate_sample_data,phyloseq-method}
14 | \alias{transmute_sample_data}
15 | \alias{transmute_sample_data,sample_data-method}
16 | \alias{transmute_sample_data,phyloseq-method}
17 | \title{Create, modify, and delete columns in the taxonomy table or sample data}
18 | \usage{
19 | mutate_tax_table(x, ...)
20 | 
21 | \S4method{mutate_tax_table}{taxonomyTable}(x, ...)
22 | 
23 | \S4method{mutate_tax_table}{phyloseq}(x, ...)
24 | 
25 | transmute_tax_table(x, ...)
26 | 
27 | \S4method{transmute_tax_table}{taxonomyTable}(x, ...)
28 | 
29 | \S4method{transmute_tax_table}{phyloseq}(x, ...)
30 | 
31 | mutate_sample_data(x, ...)
32 | 
33 | \S4method{mutate_sample_data}{sample_data}(x, ...)
34 | 
35 | \S4method{mutate_sample_data}{phyloseq}(x, ...)
36 | 
37 | transmute_sample_data(x, ...)
38 | 
39 | \S4method{transmute_sample_data}{sample_data}(x, ...)
40 | 
41 | \S4method{transmute_sample_data}{phyloseq}(x, ...)
42 | }
43 | \arguments{
44 | \item{x}{A \code{phyloseq}, \code{taxonomyTable}, or \code{sample_data} object}
45 | 
46 | \item{...}{Expressions passed to \code{dplyr::mutate()} or \code{dplyr::transmute()}}
47 | }
48 | \description{
49 | These functions are wrappers around \code{dplyr::mutate()} and
50 | \code{dplyr::transmute()} that provide convenient ways to modify \code{tax_table(x)}
51 | and \code{sample_data(x)} as well as the sample and taxa names.
52 | }
53 | \details{
54 | When modifying the taxonomy table, the \code{.otu} column name can be used to set
55 | new taxa names.
56 | When modifying the sample data, the \code{.sample} column name can be used to set
57 | new taxa names.
58 | 
59 | The experimental arguments to \code{dplyr::mutate()} of \code{.keep}, \code{.before}, and
60 | \code{.after} are not currently supported and may result in errors or unexpected
61 | behavior.
62 | }
63 | \examples{
64 | data(GlobalPatterns)
65 | 
66 | ps <- GlobalPatterns \%>\%
67 |   transmute_sample_data(SampleType, sample_sum = sample_sums(.)) \%>\%
68 |   filter_sample_data(SampleType \%in\% c("Feces", "Skin", "Tongue")) \%>\%
69 |   filter_tax_table(Kingdom == "Bacteria") \%>\%
70 |   tax_glom("Phylum") \%>\%
71 |   transmute_tax_table(Kingdom, Phylum, .otu = Phylum)
72 | sample_data(ps)
73 | tax_table(ps)
74 | }
75 | 


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/man/oneline.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/printing.R
 3 | \name{oneline}
 4 | \alias{oneline}
 5 | \title{One-line summaries of phyloseq components}
 6 | \usage{
 7 | oneline(x)
 8 | }
 9 | \arguments{
10 | \item{x}{A phyloseq component object}
11 | }
12 | \description{
13 | One-line summaries of phyloseq components
14 | }
15 | \keyword{internal}
16 | 


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/man/orient_taxa.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/utils.R
 3 | \name{orient_taxa}
 4 | \alias{orient_taxa}
 5 | \alias{orient_taxa,otu_table-method}
 6 | \alias{orient_taxa,phyloseq-method}
 7 | \title{Orient a phyloseq object or otu table to have taxa as rows or as columns}
 8 | \usage{
 9 | orient_taxa(x, as)
10 | 
11 | \S4method{orient_taxa}{otu_table}(x, as)
12 | 
13 | \S4method{orient_taxa}{phyloseq}(x, as)
14 | }
15 | \arguments{
16 | \item{x}{A phyloseq or otu-table object}
17 | 
18 | \item{as}{The matrix dimension that is desired for taxa. Must be
19 | "rows" for rows and "columns" or "cols" for columns.}
20 | }
21 | \description{
22 | Puts the phyloseq or otu-table object \code{x} in the orientation (taxa as rows
23 | or as columns) specified by \code{as}. This is useful when passing the otu table
24 | on to functions that require the abundance matrix to have a specific
25 | orientation and are unaware of the \code{taxa_are_rows(x)} property.
26 | }
27 | \examples{
28 | data(soilrep)
29 | taxa_are_rows(soilrep)
30 | x <- soilrep \%>\% orient_taxa(as = "columns")
31 | taxa_are_rows(x)
32 | }
33 | 


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/man/pipe.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/utils-pipe.R
 3 | \name{\%>\%}
 4 | \alias{\%>\%}
 5 | \title{Pipe operator}
 6 | \usage{
 7 | lhs \%>\% rhs
 8 | }
 9 | \description{
10 | See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
11 | }
12 | \keyword{internal}
13 | 


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/man/plot_bar.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/plot-methods.R
 3 | \name{plot_bar}
 4 | \alias{plot_bar}
 5 | \title{A flexible, informative barplot phyloseq data}
 6 | \usage{
 7 | plot_bar(physeq, x="Sample", y="Abundance", fill=NULL,
 8 |  title=NULL, facet_grid=NULL)
 9 | }
10 | \arguments{
11 | \item{physeq}{(Required). An \code{\link{otu_table-class}} or
12 | \code{\link{phyloseq-class}}.}
13 | 
14 | \item{x}{(Optional). Optional, but recommended, especially if your data
15 | is comprised of many samples. A character string.
16 | The variable in the melted-data that should be mapped to the x-axis.
17 | See \code{\link{psmelt}}, \code{\link{melt}},
18 | and \code{\link{ggplot}} for more details.}
19 | 
20 | \item{y}{(Optional). A character string.
21 | The variable in the melted-data that should be mapped to the y-axis.
22 | Typically this will be \code{"Abundance"}, in order to
23 | quantitatively display the abundance values for each OTU/group.
24 | However, alternative variables could be used instead,
25 | producing a very different, though possibly still informative, plot.
26 | See \code{\link{psmelt}}, \code{\link{melt}},
27 | and \code{\link{ggplot}} for more details.}
28 | 
29 | \item{fill}{(Optional). A character string. Indicates which sample variable
30 | should be used to map to the fill color of the bars.
31 | The default is \code{NULL}, resulting in a gray fill for all bar segments.}
32 | 
33 | \item{title}{(Optional). Default \code{NULL}. Character string.
34 | The main title for the graphic.}
35 | 
36 | \item{facet_grid}{(Optional). A formula object.
37 | It should describe the faceting you want in exactly the same way as for
38 | \code{\link[ggplot2]{facet_grid}},
39 | and is ulitmately provided to \code{\link{ggplot}}2 graphics.
40 | The default is: \code{NULL}, resulting in no faceting.}
41 | }
42 | \value{
43 | A \code{\link[ggplot2]{ggplot}}2 graphic object -- rendered in the
44 | graphical device as the default
45 | \code{\link[base]{print}}/\code{\link[methods]{show}} method.
46 | }
47 | \description{
48 | There are many useful examples of phyloseq barplot graphics in the
49 | \href{http://joey711.github.io/phyloseq/plot_bar-examples}{phyloseq online
50 | tutorials}.  This function wraps \code{ggplot2} plotting, and returns a
51 | \code{ggplot2} graphic object that can be saved or further modified with
52 | additional layers, options, etc.  The main purpose of this function is to
53 | quickly and easily create informative summary graphics of the differences in
54 | taxa abundance between samples in an experiment.
55 | }
56 | \examples{
57 | data("GlobalPatterns")
58 | gp.ch = subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
59 | plot_bar(gp.ch)
60 | plot_bar(gp.ch, fill="Genus")
61 | plot_bar(gp.ch, x="SampleType", fill="Genus")
62 | plot_bar(gp.ch, "SampleType", fill="Genus", facet_grid=~Family)
63 | # See additional examples in the plot_bar online tutorial. Link above.
64 | }
65 | \seealso{
66 | \href{http://joey711.github.io/phyloseq/plot_bar-examples}{phyloseq online
67 | tutorials}.
68 | 
69 | \code{\link{psmelt}}
70 | 
71 | \code{\link{ggplot}}
72 | 
73 | \code{\link{qplot}}
74 | }
75 | 


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/man/psmelt.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/psmelt.R
 3 | \name{psmelt}
 4 | \alias{psmelt}
 5 | \title{Melt phyloseq data object into large data.frame}
 6 | \usage{
 7 | psmelt(physeq, as = getOption("speedyseq.psmelt_class"))
 8 | }
 9 | \arguments{
10 | \item{physeq}{An \linkS4class{otu_table} or \linkS4class{phyloseq}; most useful for
11 | phyloseq-class.}
12 | 
13 | \item{as}{Class of the output table; see Details.}
14 | }
15 | \value{
16 | A table of the specified class
17 | }
18 | \description{
19 | The psmelt function is a specialized melt function for melting phyloseq
20 | objects (instances of the phyloseq class), usually for producing graphics
21 | with \link[ggplot2:ggplot2-package]{ggplot2::ggplot2}. The naming conventions used in downstream phyloseq
22 | graphics functions have reserved the following variable names that should
23 | not be used as the names of \code{\link[=sample_variables]{sample_variables()}} or taxonomic
24 | \code{\link[=rank_names]{rank_names()}}. These reserved names are \code{c("Sample", "Abundance", "OTU")}.
25 | Also, you should not have identical names for sample variables and taxonomic
26 | ranks. That is, the intersection of the output of the following two
27 | functions \code{\link[=sample_variables]{sample_variables()}}, \code{\link[=rank_names]{rank_names()}} should be an empty vector
28 | (e.g.  \code{intersect(sample_variables(physeq), rank_names(physeq))}). All of
29 | these potential name collisions are checked-for and renamed automatically
30 | with a warning. However, if you (re)name your variables accordingly ahead of
31 | time, it will reduce confusion and eliminate the warnings.
32 | }
33 | \details{
34 | The \code{as} argument allows choosing between three classes for tabular data:
35 | \itemize{
36 | \item \linkS4class{data.frame} is chosen by "data.frame" or "df"
37 | \item \linkS4class{data.table} is chosen by "data.table" or "dt"
38 | \item \linkS4class{tbl_df} is chosen by "tbl_df", "tbl", or "tibble"
39 | }
40 | 
41 | The default is "data.frame" and can be overridden by setting the
42 | "speedyseq.psmelt_class" option.
43 | 
44 | Note that "melted" phyloseq data is stored much less efficiently, and so RAM
45 | storage issues could arise with a smaller dataset (smaller number of
46 | samples/OTUs/variables) than one might otherwise expect.  For common sizes
47 | of graphics-ready datasets, however, this should not be a problem.  Because
48 | the number of OTU entries has a large effect on the RAM requirement, methods
49 | to reduce the number of separate OTU entries -- for instance by
50 | agglomerating OTUs based on phylogenetic distance using \code{\link[=tip_glom]{tip_glom()}} -- can
51 | help alleviate RAM usage problems. This function is made user-accessible for
52 | flexibility, but is also used extensively by plot functions in phyloseq.
53 | }
54 | \examples{
55 | data("GlobalPatterns")
56 | gp.ch = subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
57 | mdf = psmelt(gp.ch)
58 | nrow(mdf)
59 | ncol(mdf)
60 | colnames(mdf)
61 | head(rownames(mdf))
62 | # Create a ggplot similar to
63 | library("ggplot2")
64 | p = ggplot(mdf, aes(x=SampleType, y=Abundance, fill=Genus))
65 | p = p + geom_bar(color="black", stat="identity", position="stack")
66 | print(p)
67 | }
68 | \seealso{
69 | \code{\link[=plot_bar]{plot_bar()}}
70 | }
71 | 


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/man/reexports.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/as_tibble.R
 3 | \docType{import}
 4 | \name{reexports}
 5 | \alias{reexports}
 6 | \alias{as_tibble}
 7 | \title{Objects exported from other packages}
 8 | \keyword{internal}
 9 | \description{
10 | These objects are imported from other packages. Follow the links
11 | below to see their documentation.
12 | 
13 | \describe{
14 |   \item{tibble}{\code{\link[tibble]{as_tibble}}}
15 | }}
16 | 
17 | 


--------------------------------------------------------------------------------
/man/relocate-phyloseq.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/dplyr-verbs.R
 3 | \name{relocate-phyloseq}
 4 | \alias{relocate-phyloseq}
 5 | \alias{relocate_tax_table}
 6 | \alias{relocate_tax_table,taxonomyTable-method}
 7 | \alias{relocate_tax_table,phyloseq-method}
 8 | \alias{relocate_sample_data}
 9 | \alias{relocate_sample_data,sample_data-method}
10 | \alias{relocate_sample_data,phyloseq-method}
11 | \title{Change column order in the taxonomy table or sample data}
12 | \usage{
13 | relocate_tax_table(x, ...)
14 | 
15 | \S4method{relocate_tax_table}{taxonomyTable}(x, ...)
16 | 
17 | \S4method{relocate_tax_table}{phyloseq}(x, ...)
18 | 
19 | relocate_sample_data(x, ...)
20 | 
21 | \S4method{relocate_sample_data}{sample_data}(x, ...)
22 | 
23 | \S4method{relocate_sample_data}{phyloseq}(x, ...)
24 | }
25 | \arguments{
26 | \item{x}{A \code{phyloseq}, \code{taxonomyTable}, or \code{sample_data} object}
27 | 
28 | \item{...}{Expressions and arguments passed to \code{dplyr::relocate()}}
29 | }
30 | \description{
31 | These functions are wrappers around \code{dplyr::relocate()} that provide
32 | convenient ways to modify \code{tax_table(x)} and \code{sample_data(x)}.
33 | }
34 | \details{
35 | The special column names '.otu' and '.sample' should not be used; see
36 | \code{mutate-phyloseq} for the ability to change taxa and sample names using
37 | these names.
38 | }
39 | \examples{
40 | data(GlobalPatterns)
41 | 
42 | GlobalPatterns \%>\% sample_variables
43 | ps <- GlobalPatterns \%>\%
44 |   relocate_sample_data(SampleType, .after = X.SampleID)
45 | ps \%>\% sample_variables
46 | }
47 | 


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/man/rename-phyloseq.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/dplyr-verbs.R
 3 | \name{rename-phyloseq}
 4 | \alias{rename-phyloseq}
 5 | \alias{rename_tax_table}
 6 | \alias{rename_tax_table,taxonomyTable-method}
 7 | \alias{rename_tax_table,phyloseq-method}
 8 | \alias{rename_with_tax_table}
 9 | \alias{rename_with_tax_table,taxonomyTable-method}
10 | \alias{rename_with_tax_table,phyloseq-method}
11 | \alias{rename_sample_data}
12 | \alias{rename_sample_data,sample_data-method}
13 | \alias{rename_sample_data,phyloseq-method}
14 | \alias{rename_with_sample_data}
15 | \alias{rename_with_sample_data,sample_data-method}
16 | \alias{rename_with_sample_data,phyloseq-method}
17 | \title{Rename columns in the taxonomy table or sample data}
18 | \usage{
19 | rename_tax_table(x, ...)
20 | 
21 | \S4method{rename_tax_table}{taxonomyTable}(x, ...)
22 | 
23 | \S4method{rename_tax_table}{phyloseq}(x, ...)
24 | 
25 | rename_with_tax_table(x, ...)
26 | 
27 | \S4method{rename_with_tax_table}{taxonomyTable}(x, ...)
28 | 
29 | \S4method{rename_with_tax_table}{phyloseq}(x, ...)
30 | 
31 | rename_sample_data(x, ...)
32 | 
33 | \S4method{rename_sample_data}{sample_data}(x, ...)
34 | 
35 | \S4method{rename_sample_data}{phyloseq}(x, ...)
36 | 
37 | rename_with_sample_data(x, ...)
38 | 
39 | \S4method{rename_with_sample_data}{sample_data}(x, ...)
40 | 
41 | \S4method{rename_with_sample_data}{phyloseq}(x, ...)
42 | }
43 | \arguments{
44 | \item{x}{A \code{phyloseq}, \code{taxonomyTable}, or \code{sample_data} object}
45 | 
46 | \item{...}{Renaming expressions passed to \code{dplyr::rename()} or arguments
47 | passed to \code{dplyr::rename_with()}}
48 | }
49 | \description{
50 | \code{rename_tax_table()} and \code{rename_with_tax_table()} provide the functionality
51 | of \code{dplyr::rename()} and \code{dplyr::rename_with()} to phyloseq taxonomy tables;
52 | \code{rename_sample_data()} and \code{rename_with_sample_data()} provide this
53 | functionality to phyloseq sample data.
54 | }
55 | \examples{
56 | data(GlobalPatterns)
57 | 
58 | GlobalPatterns \%>\% rank_names
59 | 
60 | ps1 <- GlobalPatterns \%>\%
61 |   rename_tax_table(Domain = Kingdom) \%>\%
62 |   rename_with_tax_table(stringr::str_to_lower)
63 | ps1 \%>\% rank_names
64 | 
65 | GlobalPatterns \%>\% sample_variables
66 | ps2 <- GlobalPatterns \%>\%
67 |   rename_with_sample_data(janitor::make_clean_names) \%>\%
68 |   rename_sample_data(sample_id = x_sample_id)
69 | ps2 \%>\% sample_variables
70 | }
71 | 


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/man/sample_data_stable.Rd:
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 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_samples.R
 3 | \name{sample_data_stable}
 4 | \alias{sample_data_stable}
 5 | \title{Create sample data without adjusting row/sample names}
 6 | \usage{
 7 | sample_data_stable(object)
 8 | }
 9 | \arguments{
10 | \item{object}{A "data.frame"-class object}
11 | }
12 | \description{
13 | \code{phyloseq::sample_data()} will change the sample names from the row names if
14 | they are \code{as.character(1:nrow(object))}. This function instead keeps the
15 | names as is.
16 | }
17 | \examples{
18 | x <- data.frame(var1 = letters[1:3], var2 = 7:9)
19 | rownames(x)
20 | sample_data(x)
21 | speedyseq:::sample_data_stable(x)
22 | }
23 | \keyword{internal}
24 | 


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/man/select-phyloseq.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/dplyr-verbs.R
 3 | \name{select-phyloseq}
 4 | \alias{select-phyloseq}
 5 | \alias{select_tax_table}
 6 | \alias{select_tax_table,taxonomyTable-method}
 7 | \alias{select_tax_table,phyloseq-method}
 8 | \alias{select_sample_data}
 9 | \alias{select_sample_data,sample_data-method}
10 | \alias{select_sample_data,phyloseq-method}
11 | \title{Subset columns in the taxonomy table or sample data using their names and types}
12 | \usage{
13 | select_tax_table(x, ...)
14 | 
15 | \S4method{select_tax_table}{taxonomyTable}(x, ...)
16 | 
17 | \S4method{select_tax_table}{phyloseq}(x, ...)
18 | 
19 | select_sample_data(x, ...)
20 | 
21 | \S4method{select_sample_data}{sample_data}(x, ...)
22 | 
23 | \S4method{select_sample_data}{phyloseq}(x, ...)
24 | }
25 | \arguments{
26 | \item{x}{A \code{phyloseq}, \code{taxonomyTable}, or \code{sample_data} object}
27 | 
28 | \item{...}{Expressions passed to \code{dplyr::select()}}
29 | }
30 | \description{
31 | These functions are wrappers around \code{dplyr::select()} that provide
32 | convenient ways to modify \code{tax_table(x)} and \code{sample_data(x)}.
33 | See \code{dplyr::select()} for supported syntax and helpers.
34 | }
35 | \details{
36 | The special column names '.otu' and '.sample' should not be used; see
37 | \code{mutate-phyloseq} for the ability to change taxa and sample names using
38 | these names.
39 | }
40 | \examples{
41 | data(GlobalPatterns)
42 | 
43 | GlobalPatterns \%>\% rank_names
44 | GlobalPatterns \%>\% sample_variables
45 | ps <- GlobalPatterns \%>\%
46 |   select_tax_table(Phylum, Genus:Species) \%>\%
47 |   select_sample_data(!dplyr::contains("Barcode"))
48 | ps \%>\% rank_names
49 | ps \%>\% sample_variables
50 | }
51 | 


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/man/select_taxa-methods.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/utils.R
 3 | \name{select_taxa}
 4 | \alias{select_taxa}
 5 | \alias{select_taxa,sample_data,character-method}
 6 | \alias{select_taxa,otu_table,character-method}
 7 | \alias{select_taxa,taxonomyTable,character-method}
 8 | \alias{select_taxa,XStringSet,character-method}
 9 | \alias{select_taxa,phylo,character-method}
10 | \alias{select_taxa,phyloseq,character-method}
11 | \title{Select a subset of taxa in a specified order where possible}
12 | \usage{
13 | select_taxa(x, taxa, reorder = TRUE)
14 | 
15 | \S4method{select_taxa}{sample_data,character}(x, taxa)
16 | 
17 | \S4method{select_taxa}{otu_table,character}(x, taxa, reorder = TRUE)
18 | 
19 | \S4method{select_taxa}{taxonomyTable,character}(x, taxa, reorder = TRUE)
20 | 
21 | \S4method{select_taxa}{XStringSet,character}(x, taxa, reorder = TRUE)
22 | 
23 | \S4method{select_taxa}{phylo,character}(x, taxa)
24 | 
25 | \S4method{select_taxa}{phyloseq,character}(x, taxa, reorder = TRUE)
26 | }
27 | \arguments{
28 | \item{x}{A phyloseq object or phyloseq component object}
29 | 
30 | \item{taxa}{Character vector of taxa to select, in requested order}
31 | 
32 | \item{reorder}{Logical specifying whether to use the order in \code{taxa} (TRUE)
33 | or keep the order in \code{taxa_names(x)} (FALSE)}
34 | }
35 | \description{
36 | Select (a subset of) taxa; if \code{x} allows taxa to be reordered, then taxa are
37 | given in the specified order.
38 | }
39 | \details{
40 | This is a simple selector function that is like \code{prune_taxa(taxa, x)} when
41 | \code{taxa} is a character vector but always gives the taxa in the order \code{taxa}
42 | if possible (that is, except for phy_tree's and phyloseq objects that
43 | contain phy_tree's).
44 | }
45 | \keyword{internal}
46 | 


--------------------------------------------------------------------------------
/man/show-methods.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/printing.R
 3 | \name{show,phyloseq-method}
 4 | \alias{show,phyloseq-method}
 5 | \alias{show,taxonomyTable-method}
 6 | \alias{show,sample_data-method}
 7 | \alias{show,otu_table-method}
 8 | \title{Methods for \code{show()} for phyloseq objects.}
 9 | \usage{
10 | \S4method{show}{phyloseq}(object)
11 | 
12 | \S4method{show}{taxonomyTable}(object)
13 | 
14 | \S4method{show}{sample_data}(object)
15 | 
16 | \S4method{show}{otu_table}(object)
17 | }
18 | \arguments{
19 | \item{object}{Any R object}
20 | }
21 | \description{
22 | See \code{\link[methods]{show}}. Speedyseq provides \code{show()} methods that
23 | overwrite phyloseq's methods for the classes defined in phyloseq.
24 | }
25 | \examples{
26 | data(GlobalPatterns)
27 | show(GlobalPatterns)
28 | GlobalPatterns
29 | }
30 | \seealso{
31 | \code{\link[methods]{show}}
32 | }
33 | 


--------------------------------------------------------------------------------
/man/tax_glom.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/agglomeration.R
 3 | \name{tax_glom}
 4 | \alias{tax_glom}
 5 | \title{Agglomerate taxa using taxonomy.}
 6 | \usage{
 7 | tax_glom(
 8 |   physeq,
 9 |   taxrank = rank_names(physeq)[1],
10 |   NArm = TRUE,
11 |   bad_empty = c(NA, "", " ", "\\t"),
12 |   reorder = FALSE
13 | )
14 | }
15 | \arguments{
16 | \item{physeq}{(Required). \code{\link[=phyloseq-class]{phyloseq-class()}} or \code{\link[=tax_table]{tax_table()}}.}
17 | 
18 | \item{taxrank}{A character string specifying the taxonomic level that you
19 | want to agglomerate over. Should be among the results of
20 | \code{rank_names(physeq)}. The default value is \code{rank_names(physeq)[1]}, which
21 | may agglomerate too broadly for a given experiment. You are strongly
22 | encouraged to try different values for this argument.}
23 | 
24 | \item{NArm}{(Optional). Logical, length equal to one. Default is \code{TRUE}.
25 | CAUTION. The decision to prune (or not) taxa for which you lack categorical
26 | data could have a large effect on downstream analysis.  You may want to
27 | re-compute your analysis under both conditions, or at least think carefully
28 | about what the effect might be and the reasons explaining the absence of
29 | information for certain taxa. In the case of taxonomy, it is often a result
30 | of imprecision in taxonomic designation based on short phylogenetic
31 | sequences and a patchy system of nomenclature.  If this seems to be an issue
32 | for your analysis, think about also trying the nomenclature-agnostic
33 | \code{\link[=tip_glom]{tip_glom()}} method if you have a phylogenetic tree available.}
34 | 
35 | \item{bad_empty}{(Optional). Character vector. Default: \code{c(NA, "", " ", "\\t")}. Defines the bad/empty values that should be ignored and/or
36 | considered unknown. They will be removed from the internal agglomeration
37 | vector derived from the argument to \code{tax}, and therefore agglomeration will
38 | not combine taxa according to the presence of these values in \code{tax}.
39 | Furthermore, the corresponding taxa can be optionally pruned from the output
40 | if \code{NArm} is set to \code{TRUE}.}
41 | 
42 | \item{reorder}{Logical specifying whether to reorder the taxa by taxonomy
43 | strings or keep initial order. Ignored if \code{physeq} has a phylogenetic tree.}
44 | }
45 | \value{
46 | A taxonomically-agglomerated, optionally-pruned, object with class
47 | matching the class of \code{physeq}.
48 | }
49 | \description{
50 | This method merges species that have the same taxonomy at a certain
51 | taxonomic rank. Its approach is analogous to \code{\link[=tip_glom]{tip_glom()}}, but
52 | uses categorical data instead of a tree. In principal, other categorical
53 | data known for all taxa could also be used in place of taxonomy, but for the
54 | moment, this must be stored in the \code{taxonomyTable} of the data. Also,
55 | columns/ranks to the right of the rank chosen to use for agglomeration will
56 | be replaced with \code{NA}, because they should be meaningless following
57 | agglomeration.
58 | }
59 | \details{
60 | This is the speedyseq reimplementation of \code{phyloseq::tax_glom()}. It should
61 | produce results that are identical to phyloseq up to taxon order.
62 | 
63 | If \code{x} is a phyloseq object with a phylogenetic tree, then the new taxa will
64 | be ordered as they are in the tree. Otherwise, the taxa order can be
65 | controlled by the \code{reorder} argument, which behaves like the \code{reorder}
66 | argument in \code{\link[base:rowsum]{base::rowsum()}}. \code{reorder = FALSE} will keep taxa in
67 | the original order determined by when the member of each group first appears
68 | in \code{taxa_names(x)}; \code{reorder = TRUE} will order new taxa alphabetically
69 | according to taxonomy (string of concatenated rank values).
70 | 
71 | Acknowledgements: Documentation and general strategy derived from
72 | \code{phyloseq::tax_glom()}.
73 | }
74 | \examples{
75 | data(GlobalPatterns)
76 | # print the available taxonomic ranks
77 | colnames(tax_table(GlobalPatterns))
78 | # agglomerate at the Family taxonomic rank
79 | (x1 <- tax_glom(GlobalPatterns, taxrank="Family"))
80 | # How many taxa before/after agglomeration?
81 | ntaxa(GlobalPatterns); ntaxa(x1)
82 | }
83 | \seealso{
84 | \code{\link[phyloseq:tax_glom]{phyloseq::tax_glom()}}
85 | 
86 | \code{\link[=tip_glom]{tip_glom()}}
87 | 
88 | \code{\link[=prune_taxa]{prune_taxa()}}
89 | 
90 | \code{\link[=merge_taxa_vec]{merge_taxa_vec()}}
91 | }
92 | 


--------------------------------------------------------------------------------
/man/tibble-constructors.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/phyloseq-constructors.R
 3 | \name{tibble-constructors}
 4 | \alias{tibble-constructors}
 5 | \alias{otu_table,tbl_df-method}
 6 | \alias{sample_data,tbl_df-method}
 7 | \alias{tax_table,tbl_df-method}
 8 | \title{Construct phyloseq objects from tibbles}
 9 | \usage{
10 | \S4method{otu_table}{tbl_df}(object, taxa_are_rows)
11 | 
12 | \S4method{sample_data}{tbl_df}(object)
13 | 
14 | \S4method{tax_table}{tbl_df}(object)
15 | }
16 | \arguments{
17 | \item{object}{A tibble whose first column contains the sample or taxa ids}
18 | 
19 | \item{taxa_are_rows}{Logical; \code{TRUE} if rows correspond to taxa and \code{FALSE}
20 | if rows correspond to samples}
21 | }
22 | \description{
23 | These methods extend phyloseq's constructor functions to construct phyloseq
24 | components from tibbles (objects with class "tbl_df").
25 | }
26 | \details{
27 | Since tibbles cannot have row names, the sample or taxon identifiers must be
28 | contained in a regular column. Speedyseq currently always uses the first
29 | column for the identifiers that would normally be taken from the row names
30 | by phyloseq's built-in constructors. Thus the first column is assumed to
31 | contain the sample names for \code{sample_data()} and the OTU/taxa names for
32 | \code{tax_table()}; for \code{otu_table()}, the first column is assumed to contain the
33 | sample names if \code{taxa_are_rows = TRUE} and the taxa names if \code{taxa_are_rows = FALSE}, with the other identifier being taken from the remaining column
34 | names.
35 | }
36 | \examples{
37 | \dontrun{
38 | # Read a .csv file with readr, which creates an object of class `tbl_df`
39 | tbl <- readr::read_csv("path/to/otu_table.csv")
40 | # Inspect and check if taxa are rows and that the first column contains the
41 | # sample names or the taxa/OTU names
42 | head(tbl) 
43 | # Create a phyloseq `otu_table` object
44 | otu <- otu_table(tbl, taxa_are_rows = FALSE)
45 | 
46 | # Read a .csv file with readr, which creates an object of class `tbl_df`
47 | tbl <- readr::read_csv("path/to/sample_data.csv")
48 | # Inspect and check that the first column contains the sample names
49 | head(tbl) 
50 | # Create a phyloseq `sample_data` object
51 | sam <- sample_data(tbl)
52 | }
53 | }
54 | \seealso{
55 | \code{\link[tibble:tbl_df-class]{tibble::tbl_df}}
56 | \code{\link[phyloseq:otu_table-methods]{phyloseq::otu_table()}}
57 | \code{\link[phyloseq:sample_data-methods]{phyloseq::sample_data()}}
58 | \code{\link[phyloseq:tax_table-methods]{phyloseq::tax_table()}}
59 | }
60 | 


--------------------------------------------------------------------------------
/man/tibble_print.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/printing.R
 3 | \name{tibble_print}
 4 | \alias{tibble_print}
 5 | \title{Print a 2-d data.frame or matrix like a tibble}
 6 | \usage{
 7 | tibble_print(
 8 |   x,
 9 |   n = NULL,
10 |   width = NULL,
11 |   n_extra = NULL,
12 |   types = TRUE,
13 |   rows = "rows",
14 |   cols = "variables"
15 | )
16 | }
17 | \arguments{
18 | \item{x}{A data frame or matrix}
19 | 
20 | \item{n}{Number of rows to print}
21 | 
22 | \item{width}{Display width}
23 | 
24 | \item{n_extra}{Number of extra columns after width to summarize after main
25 | display}
26 | 
27 | \item{types}{Whether to print the second row with the variable types}
28 | 
29 | \item{rows}{What rows are called in the end summary}
30 | 
31 | \item{cols}{What columns are called in the end summary}
32 | }
33 | \description{
34 | Print a 2-d data.frame or matrix like a tibble
35 | }
36 | \keyword{internal}
37 | 


--------------------------------------------------------------------------------
/man/tip_glom.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/agglomeration.R
 3 | \name{tip_glom}
 4 | \alias{tip_glom}
 5 | \title{Agglomerate taxa using phylogeny-derived distances.}
 6 | \usage{
 7 | tip_glom(physeq, h = 0.2, hcfun = cluster::agnes, tax_adjust = 1L, ...)
 8 | }
 9 | \arguments{
10 | \item{physeq}{(Required). A \code{\link[=phyloseq-class]{phyloseq-class()}}, containing a phylogenetic
11 | tree. Alternatively, a phylogenetic tree \code{\link[ape:read.tree]{ape::phylo()}} will also work.}
12 | 
13 | \item{h}{(Optional). Numeric scalar of the height where the tree should be
14 | cut. This refers to the tree resulting from hierarchical clustering of the
15 | distance matrix, not the original phylogenetic tree. Default value is \code{0.2}.}
16 | 
17 | \item{hcfun}{(Optional). A function. The (agglomerative, hierarchical)
18 | clustering function to use. The default is \code{\link[cluster:agnes]{cluster::agnes()}} for phyloseq
19 | compatiblity.}
20 | 
21 | \item{tax_adjust}{0: no adjustment; 1: phyloseq-compatible adjustment; 2:
22 | conservative adjustment (see \code{\link[=merge_taxa_vec]{merge_taxa_vec()}} for details)}
23 | 
24 | \item{...}{(Optional). Additional named arguments to pass to \code{hcfun}.}
25 | }
26 | \value{
27 | An instance of the \code{\link[=phyloseq-class]{phyloseq-class()}}.  Or alternatively, a
28 | \code{\link[=phylo]{phylo()}} object if the \code{physeq} argument was just a tree.  In the
29 | expected-use case, the number of OTUs will be fewer (see \code{\link[=ntaxa]{ntaxa()}}), after
30 | merging OTUs that are related enough to be called the same OTU.
31 | }
32 | \description{
33 | All tips of the tree separated by a phylogenetic cophenetic distance smaller
34 | than \code{h} will be agglomerated into one taxon.
35 | }
36 | \details{
37 | Can be used to create a non-trivial OTU Table, if a phylogenetic tree is
38 | available.
39 | 
40 | By default, simple "greedy" single-linkage clustering is used. It is
41 | possible to specify different clustering approaches by setting \code{hcfun} and
42 | its parameters in \code{...}. In particular, complete-linkage clustering appears
43 | to be used more commonly for OTU clustering applications.
44 | 
45 | The merged taxon is named according to the "archetype" defined as the the
46 | most abundant taxon (having the largest value of \code{taxa_sums(physeq)}. The
47 | tree and refseq objects are pruned to the archetype taxa.
48 | 
49 | Speedyseq note: \code{\link[stats:hclust]{stats::hclust()}} is faster than the default \code{hcfun}; set
50 | \code{method = "average"} to get equivalent clustering.
51 | 
52 | Acknowledgements: Documentation and general strategy derived from
53 | \code{phyloseq::tip_glom()}.
54 | }
55 | \examples{
56 | data("esophagus")
57 | esophagus <- prune_taxa(taxa_names(esophagus)[1:25], esophagus)
58 | plot_tree(esophagus, label.tips="taxa_names", size="abundance", 
59 |   title="Before tip_glom()")
60 | plot_tree(tip_glom(esophagus, h=0.2), label.tips="taxa_names", 
61 |   size="abundance", title="After tip_glom()")
62 | 
63 | # *speedyseq only:* Demonstration of different `tax_adjust` behaviors
64 | data(GlobalPatterns)
65 | 
66 | set.seed(20190421)
67 | ps <- prune_taxa(sample(taxa_names(GlobalPatterns), 2e2), GlobalPatterns)
68 | 
69 | ps1 <- tip_glom(ps, 0.1, tax_adjust = 1)
70 | ps2 <- tip_glom(ps, 0.1, tax_adjust = 2)
71 | tax_table(ps1)[c(108, 136, 45),]
72 | tax_table(ps2)[c(108, 136, 45),]
73 | }
74 | \seealso{
75 | \code{\link[phyloseq:tip_glom]{phyloseq::tip_glom()}}
76 | 
77 | \code{\link[=tree_glom]{tree_glom()}} for direct phylogenetic merging
78 | 
79 | \code{\link[=merge_taxa_vec]{merge_taxa_vec()}}
80 | 
81 | \code{\link[cluster:agnes]{cluster::agnes()}}
82 | 
83 | \code{\link[stats:hclust]{stats::hclust()}}
84 | 
85 | \code{\link[castor:get_all_pairwise_distances]{castor::get_all_pairwise_distances()}}
86 | 
87 | \code{\link[ape:read.tree]{ape::phylo()}}
88 | }
89 | 


--------------------------------------------------------------------------------
/man/transform_sample_counts.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/transform-filter.R
 3 | \name{transform_sample_counts}
 4 | \alias{transform_sample_counts}
 5 | \title{Transform abundance data in an \code{otu_table}, sample-by-sample}
 6 | \usage{
 7 | transform_sample_counts(physeq, fun, ...)
 8 | }
 9 | \arguments{
10 | \item{physeq}{\code{\link[=phyloseq-class]{phyloseq-class()}} or \code{\link[ape:read.tree]{ape::phylo()}}.}
11 | 
12 | \item{fun}{A function or formula that can be converted to a function by
13 | \code{\link[purrr:as_mapper]{purrr::as_mapper()}}}
14 | 
15 | \item{...}{Additional arguments passed on to \code{fun}}
16 | }
17 | \description{
18 | Version of \code{\link[phyloseq:transformcounts]{phyloseq::transform_sample_counts()}} that allows a
19 | purrr-style anonymous function.
20 | }
21 | \details{
22 | This function simply calls \code{\link[purrr:as_mapper]{purrr::as_mapper()}} on \code{fun} and passes the
23 | resulting function on to \code{\link[phyloseq:transformcounts]{phyloseq::transform_sample_counts()}}.
24 | }
25 | \examples{
26 | data(GlobalPatterns)
27 | # Filter low prevalence taxa, then convert to proportions
28 | gp.prop <- GlobalPatterns \%>\%
29 |   filter_taxa2(~ sum(. > 0) > 5) \%>\%
30 |   transform_sample_counts(~ . / sum(.))
31 | }
32 | \seealso{
33 | \code{\link[phyloseq:transformcounts]{phyloseq::transform_sample_counts()}}
34 | \code{\link[purrr:as_mapper]{purrr::as_mapper()}}
35 | }
36 | 


--------------------------------------------------------------------------------
/man/tree_glom.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/agglomeration.R
 3 | \name{tree_glom}
 4 | \alias{tree_glom}
 5 | \title{Agglomerate taxa at a given phylogenetic resolution.}
 6 | \usage{
 7 | tree_glom(physeq, resolution, criterion = "max_tip_depth", tax_adjust = 1L)
 8 | }
 9 | \arguments{
10 | \item{physeq}{\code{\link[=phyloseq-class]{phyloseq-class()}} or \code{\link[ape:read.tree]{ape::phylo()}}.}
11 | 
12 | \item{resolution}{Phylogenetic resolution at which to merge taxa.}
13 | 
14 | \item{criterion}{Criterion for determining whether to collapse taxa. See
15 | \code{\link[castor:collapse_tree_at_resolution]{castor::collapse_tree_at_resolution()}} for details.}
16 | 
17 | \item{tax_adjust}{0: no adjustment; 1: phyloseq-compatible adjustment; 2:
18 | conservative adjustment (see \code{\link[=merge_taxa_vec]{merge_taxa_vec()}} for details)}
19 | }
20 | \value{
21 | A \code{physeq} object with new taxa reflecting the phylogenetically
22 | merged groups.
23 | }
24 | \description{
25 | This function merges taxa that are sufficiently closely related in the tree
26 | in \code{phy_tree(physeq)}. It is similar to \code{tip_glom()}, except that it uses
27 | the tree directly, rather than as the basis for hierarchical clustering.
28 | }
29 | \details{
30 | This function uses \code{\link[castor:collapse_tree_at_resolution]{castor::collapse_tree_at_resolution()}} from
31 | the castor package to determine the groups of taxa to be collapsed; see this
32 | function for details about the \code{resolution} and \code{criterion} parameters.
33 | 
34 | New taxa are named according to the most abundant taxon of each group. The
35 | tree, reference sequences, and (by default) the taxonomy table reflect these
36 | "archetype" taxa.
37 | }
38 | \examples{
39 | data(GlobalPatterns)
40 | ps1 <- subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
41 | ntaxa(ps1)
42 | ps2 <- tree_glom(ps1, 0.05)
43 | ntaxa(ps2)
44 | 
45 | suppressPackageStartupMessages(library(dplyr))
46 | suppressPackageStartupMessages(library(ggtree))
47 | suppressPackageStartupMessages(library(cowplot))
48 | 
49 | plot1 <- phy_tree(ps1) \%>\%
50 |   ggtree +
51 |   geom_tiplab() +
52 |   geom_label(aes(x = branch, label = round(branch.length, 4)))
53 | plot2 <- phy_tree(ps2) \%>\%
54 |   ggtree +
55 |   geom_tiplab() +
56 |   geom_label(aes(x = branch, label = round(branch.length, 4)))
57 | plot_grid(plot1, plot2)
58 | }
59 | \seealso{
60 | \code{\link[castor:collapse_tree_at_resolution]{castor::collapse_tree_at_resolution()}} for more information about the
61 | \code{resolution} and \code{criterion} parameters.
62 | 
63 | \code{\link[=merge_taxa_vec]{merge_taxa_vec()}} for more about \code{tax_adjust} and general merging behavior
64 | 
65 | \code{\link[=tip_glom]{tip_glom()}} for indirect phylogenetic merging
66 | 
67 | \code{\link[=tax_glom]{tax_glom()}} for merging using taxonomy
68 | }
69 | 


--------------------------------------------------------------------------------
/man/unique_or_na.Rd:
--------------------------------------------------------------------------------
 1 | % Generated by roxygen2: do not edit by hand
 2 | % Please edit documentation in R/merge_samples.R
 3 | \name{unique_or_na}
 4 | \alias{unique_or_na}
 5 | \title{Get the unique value in x or NA if none}
 6 | \usage{
 7 | unique_or_na(x)
 8 | }
 9 | \arguments{
10 | \item{x}{A vector}
11 | }
12 | \description{
13 | If \code{unique(x)} is a single value, return it; otherwise, return an NA of the
14 | same type as \code{x}. If \code{x} is a factor, then the levels and ordered status
15 | will be kept in either case. If \code{x} is a non-atomic vector (i.e. a list),
16 | then the logical \code{NA} will be used.
17 | }
18 | \examples{
19 | f <- factor(c("a", "a", "b", "c"), ordered = TRUE)
20 | unique_or_na(f)
21 | unique_or_na(f[1:2])
22 | 
23 | x <- c("a", "b", "a")
24 | unique_or_na(x[c(1, 3)])
25 | unique_or_na(x)
26 | unique_or_na(x) \%>\% typeof
27 | }
28 | 


--------------------------------------------------------------------------------
/tests/testthat.R:
--------------------------------------------------------------------------------
1 | library(testthat)
2 | library(speedyseq)
3 | 
4 | test_check("speedyseq")
5 | 


--------------------------------------------------------------------------------
/tests/testthat/test-as_tibble.R:
--------------------------------------------------------------------------------
 1 | # devtools::test_active_file(here::here("tests", "testthat", "test-as_tibble.R"))
 2 | 
 3 | data(GlobalPatterns)
 4 | ps <- prune_taxa(
 5 |   ape::extract.clade(phy_tree(GlobalPatterns), "0.589.38") %>% .$tip.label,
 6 |   GlobalPatterns
 7 | )
 8 | # add reference sequences
 9 | rs <- split(
10 |   sample(c("A", "C", "G", "T"), 2 * ntaxa(ps), replace = TRUE),
11 |   rep(seq(ntaxa(ps)), each = 2)
12 | ) %>%
13 |   purrr::map_chr(paste, collapse = "") %>%
14 |   rlang::set_names(taxa_names(ps)) %>%
15 |   Biostrings::DNAStringSet()
16 | ps <- merge_phyloseq(ps, rs)
17 | rm(rs)
18 | 
19 | test_that("as_tibble and psmelt agree up to row order", {
20 |   x <- ps %>% as_tibble %>% 
21 |     dplyr::rename(OTU = .otu, Sample = .sample, Abundance = .abundance)
22 |   y <- ps %>% psmelt
23 |   expect_true(dplyr::all_equal(x, y, ignore_row_order = TRUE))
24 | })
25 | 
26 | test_that("as_tibble returns expected columns", {
27 |   x <- ps %>% sample_data %>% as_tibble
28 |   expect_identical(names(x), c(".sample", sample_variables(ps)))
29 |   x <- ps %>% otu_table %>% as_tibble
30 |   expect_identical(names(x), c(".otu", ".sample", ".abundance"))
31 |   x <- ps %>% otu_table %>% as_tibble(pivot = FALSE)
32 |   expect_identical(names(x), c(".otu", sample_names(ps)))
33 |   x <- ps %>% as_tibble(ref = TRUE)
34 |   expect_identical(names(x), 
35 |     c(".otu", ".sample", ".abundance", sample_variables(ps), rank_names(ps),
36 |       ".sequence")
37 |   )
38 | })
39 | 
40 | test_that("as_tibble works after tibble is loaded", {
41 |   library(tibble)
42 |   x <- ps %>% sample_data %>% as_tibble
43 |   expect_identical(names(x), c(".sample", sample_variables(ps)))
44 | })
45 | 


--------------------------------------------------------------------------------
/tests/testthat/test-constructors.R:
--------------------------------------------------------------------------------
 1 | # run just this file:
 2 | # devtools::test_file(here::here("tests", "testthat", "test-constructors.R"))
 3 | 
 4 | data(GlobalPatterns)
 5 | 
 6 | test_that("results from tibble constructors match starting objects", {
 7 |   tax <- tax_table(GlobalPatterns) %>% 
 8 |     as("matrix") %>% 
 9 |     tibble::as_tibble(rownames = "taxon") %>%
10 |     tax_table
11 |   expect_identical(tax, tax_table(GlobalPatterns))
12 | 
13 |   sam <- sample_data(GlobalPatterns) %>% 
14 |     as("data.frame") %>% 
15 |     tibble::as_tibble(rownames = "sample") %>%
16 |     sample_data
17 |   expect_identical(sam, sample_data(GlobalPatterns))
18 | 
19 |   otu1 <- otu_table(GlobalPatterns) %>%
20 |     as("matrix") %>% 
21 |     tibble::as_tibble(rownames = "taxon") %>%
22 |     otu_table(taxa_are_rows = TRUE)
23 |   expect_identical(otu1, otu_table(GlobalPatterns))
24 | 
25 |   otu2 <- otu_table(GlobalPatterns) %>%
26 |     t %>%
27 |     as("matrix") %>% 
28 |     tibble::as_tibble(rownames = "taxon") %>%
29 |     otu_table(taxa_are_rows = FALSE)
30 |   expect_identical(otu2, otu_table(GlobalPatterns) %>% t)
31 | })
32 | 


--------------------------------------------------------------------------------
/tests/testthat/test-dplyr-verbs.R:
--------------------------------------------------------------------------------
 1 | # run just this file:
 2 | # devtools::test_active_file(here::here("tests", "testthat", "test-dplyr-verbs.R"))
 3 | 
 4 | data(GlobalPatterns)
 5 | ps <- GlobalPatterns %>% prune_taxa(head(taxa_names(.), 10), .)
 6 | 
 7 | test_that("mutate and filter tax table", {
 8 |   ps1 <- ps %>%
 9 |     mutate_tax_table(
10 |       .otu = stringr::str_c(Kingdom, .otu),
11 |       across(Class, ~stringr::str_c("c__", .))
12 |     ) %>%
13 |     filter_tax_table(stringr::str_detect(Class, "^c__Th"))
14 |   tax1 <- ps %>%
15 |     tax_table %>%
16 |     mutate_tax_table(
17 |       .otu = stringr::str_c(Kingdom, .otu),
18 |       across(Class, ~stringr::str_c("c__", .))
19 |     ) %>%
20 |     filter_tax_table(stringr::str_detect(Class, "^c__Th"))
21 |   expect_identical(ntaxa(ps1), 3L)
22 |   expect_identical(tax_table(ps1), tax1)
23 | })
24 | 
25 | test_that("mutate and filter sample data", {
26 |   ps1 <- ps %>%
27 |     mutate_sample_data(
28 |       .sample = stringr::str_c(SampleType, .sample),
29 |       new_var = dplyr::row_number()
30 |     ) %>%
31 |     filter_sample_data(SampleType %in% c("Feces", "Soil"))
32 |   sam1 <- ps %>%
33 |     sample_data %>%
34 |     mutate_sample_data(
35 |       .sample = stringr::str_c(SampleType, .sample),
36 |       new_var = dplyr::row_number()
37 |     ) %>%
38 |     filter_sample_data(SampleType %in% c("Feces", "Soil"))
39 |   expect_identical(nsamples(ps1), 7L)
40 |   expect_identical(sample_data(ps1), sam1)
41 | })
42 | 
43 | test_that("can rename columns in taxonomy table and sample data", {
44 |   ps1 <- GlobalPatterns %>%
45 |     rename_tax_table(Domain = Kingdom) %>%
46 |     rename_with_tax_table(stringr::str_to_lower) %>%
47 |     rename_with_sample_data(janitor::make_clean_names) %>%
48 |     rename_sample_data(sample_id = x_sample_id)
49 | 
50 |   nms <- rank_names(GlobalPatterns) %>% stringr::str_to_lower()
51 |   nms[1] <- "domain"
52 |   expect_identical(ps1 %>% rank_names, nms)
53 | 
54 |   nms <- sample_variables(GlobalPatterns) %>% janitor::make_clean_names()
55 |   nms[1] <- "sample_id"
56 |   expect_identical(ps1 %>% sample_variables, nms)
57 | })
58 | 
59 | test_that("can select and relocate columns in taxonomy table and sample data", {
60 |   ps1 <- GlobalPatterns %>%
61 |     select_tax_table(Phylum, Genus:Species) %>%
62 |     select_sample_data(-dplyr::contains("Barcode"))
63 |   expect_identical(c("Phylum", "Genus", "Species"), rank_names(ps1))
64 |   nms <- GlobalPatterns %>% sample_variables %>% 
65 |     stringr::str_subset("Barcode", negate = TRUE)
66 |   expect_identical(nms, sample_variables(ps1))
67 |   ps2 <- ps1 %>%
68 |     relocate_sample_data(SampleType) %>%
69 |     relocate_tax_table(Phylum, .after = dplyr::last_col())
70 |   expect_identical(sample_variables(ps2),
71 |     c("SampleType", setdiff(sample_variables(ps1), "SampleType"))
72 |   )
73 |   expect_identical(rank_names(ps2),
74 |     c(setdiff(rank_names(ps1), "Phylum"), "Phylum")
75 |   )
76 | })
77 | 
78 | test_that("can join on sample data", {
79 |   data(GlobalPatterns)
80 | 
81 |   ps1 <- GlobalPatterns %>%
82 |     select_sample_data(!contains("Barcode"))
83 |   y <- GlobalPatterns %>%
84 |     sample_data %>%
85 |     select_sample_data(contains("Barcode")) %>%
86 |     as_tibble
87 |   ps2 <- ps1 %>% left_join_sample_data(y, by = ".sample")
88 | 
89 |   z <- GlobalPatterns %>% 
90 |     relocate_sample_data(contains("Barcode"), .after = dplyr::last_col()) %>% 
91 |     sample_data
92 | 
93 |   expect_identical(ps2 %>% sample_data, z)
94 | })
95 | 


--------------------------------------------------------------------------------
/tests/testthat/test-merge_samples.R:
--------------------------------------------------------------------------------
  1 | # run just this file:
  2 | # devtools::test_active_file(here::here("tests", "testthat", "test-merge_samples.R"))
  3 | 
  4 | # helpers ---------------------------------------------------------------------
  5 | 
  6 | test_that("unique_or_na() returns expected results", {
  7 |   ## Atomic vectors
  8 |   # Characters
  9 |   expect_identical(unique_or_na(NA_character_), NA_character_)
 10 |   expect_identical(unique_or_na("a"), "a")
 11 |   expect_identical(unique_or_na(c("a", "b")), NA_character_)
 12 |   expect_identical(unique_or_na(c("a", NA_character_)), NA_character_)
 13 |   # Integers
 14 |   expect_identical(unique_or_na(NA_integer_), NA_integer_)
 15 |   expect_identical(unique_or_na(1L), 1L)
 16 |   expect_identical(unique_or_na(c(1L, 2L)), NA_integer_)
 17 |   expect_identical(unique_or_na(c(1L, NA_integer_)), NA_integer_)
 18 |   # Doubles
 19 |   expect_identical(unique_or_na(NA_real_), NA_real_)
 20 |   expect_identical(unique_or_na(1), 1)
 21 |   expect_identical(unique_or_na(c(1, 2)), NA_real_)
 22 |   expect_identical(unique_or_na(c(1, NA_real_)), NA_real_)
 23 |   # Booleans
 24 |   expect_identical(unique_or_na(NA), NA)
 25 |   expect_identical(unique_or_na(TRUE), TRUE)
 26 |   expect_identical(unique_or_na(c(TRUE, FALSE)), NA)
 27 |   expect_identical(unique_or_na(c(TRUE, NA)), NA)
 28 |   # Factors
 29 |   expect_identical(
 30 |     unique_or_na(factor(letters[1:4], levels = letters[4:1])),
 31 |     factor(NA, levels = letters[4:1])
 32 |   )
 33 |   expect_identical(
 34 |     unique_or_na(factor(c("a", "a"), levels = letters[4:1])),
 35 |     factor("a", levels = letters[4:1])
 36 |   )
 37 |   expect_identical(
 38 |     unique_or_na(factor(c("a", "a"), levels = letters[4:1], 
 39 |       labels = paste("letter", letters[4:1]))),
 40 |     factor("a", levels = letters[4:1], labels = paste("letter", letters[4:1]))
 41 |   )
 42 |   expect_identical(
 43 |     unique_or_na(factor(letters[1:4], levels = letters[4:1], ordered = TRUE)),
 44 |     factor(NA, levels = letters[4:1], ordered = TRUE)
 45 |   )
 46 |   ## Lists
 47 |   expect_identical(unique_or_na(list(1,2)), NA)
 48 |   expect_identical(unique_or_na(list(1,1)), 1)
 49 | })
 50 | 
 51 | test_that("`sample_data_stable()` maintains names", {
 52 |   x <- data.frame(var1 = letters[1:3], var2 = 7:9)
 53 |   expect_identical(sample_data_stable(x) %>% sample_names, as.character(1:3))
 54 |   rownames(x) <- c("3", "two", "1")
 55 |   expect_identical(sample_data_stable(x) %>% sample_names, c("3", "two", "1"))
 56 | })
 57 | 
 58 | # merge_samples2 --------------------------------------------------------------
 59 | 
 60 | test_that("Test `merge_samples2()` on the `enterotype` dataset", {
 61 |   data(enterotype)
 62 |   ps <- enterotype %>% prune_taxa(head(taxa_names(.), 10), .)
 63 |   sample_data(ps) <- sample_data(ps) %>%
 64 |     transform(Project.ClinicalStatus = Project:ClinicalStatus)
 65 |   # Will get a warning since `group` has NAs
 66 |   expect_warning(
 67 |     ps0 <- merge_samples2(ps, "Project.ClinicalStatus", 
 68 |       funs = list(Age = mean))
 69 |   )
 70 |   # Types of the sample vars should be unchanged
 71 |   purrr::walk2(sample_data(ps), sample_data(ps0), 
 72 |     ~expect_equal(typeof(.x), typeof(.y))
 73 |   )
 74 |   # Check OTU table, including that order is kept
 75 |   grp <- sample_data(ps)$Project.ClinicalStatus
 76 |   expect_warning(
 77 |     x <- rowsum(t(otu_table(ps)), grp, reorder = FALSE) %>% 
 78 |       {.[!is.na(rownames(.)),]} %>% t
 79 |   )
 80 |   expect_identical(otu_table(ps0) %>% as("matrix"), x)
 81 |   # Result should be identical if computed with taxa as cols
 82 |   expect_warning(
 83 |     ps1 <- merge_samples2(ps %>% t, "Project.ClinicalStatus", 
 84 |       funs = list(Age = mean)) %>% t
 85 |   )
 86 |   expect_identical(ps0, ps1)
 87 |   # Remaining components should be the same as original
 88 |   expect_identical(tax_table(ps0), tax_table(ps))
 89 |   # Should also work on columns that are numbers, or factors whose levels are
 90 |   # numbers, even if the values are of the form 1:n. (Issue #52)
 91 |   expect_warning(ps2 <- merge_samples2(ps, "Enterotype"))
 92 |   sample_data(ps)$Enterotype <- sample_data(ps)$Enterotype %>% as.integer
 93 |   expect_warning(ps3 <- merge_samples2(ps, "Enterotype"))
 94 |   sample_data(ps3)$Enterotype <- sample_data(ps3)$Enterotype %>% as.factor
 95 |   expect_identical(ps2, ps3)
 96 |   # Test with alternate abundance-merging function
 97 |   expect_warning(
 98 |     ps0 <- merge_samples2(ps, "Project.ClinicalStatus",
 99 |       fun_otu = mean,
100 |       funs = list(Age = mean)
101 |     )
102 |   )
103 | })
104 | 
105 | test_that("Test that group names are preserved", {
106 |   # For Issue #52
107 |   sam <- tibble::tibble(sample_id = letters[1:3], group_var = 1:3) %>%
108 |     sample_data
109 |   x <- merge_samples2(sam, "group_var")
110 |   expect_identical(sample_names(x), as.character(sam$group_var))
111 | })
112 | 


--------------------------------------------------------------------------------
/tests/testthat/test-print.R:
--------------------------------------------------------------------------------
 1 | data(GlobalPatterns)
 2 | 
 3 | test_that("print(x) returns x without error", {
 4 |   # Note, ape's print.phylo method does not return anything
 5 |   slts <- setdiff(slotNames(GlobalPatterns), "phy_tree")
 6 |   for (s in slts) {
 7 |     x <- slot(GlobalPatterns, s)
 8 |     y <- slot(GlobalPatterns, s) %>% print
 9 |     expect_identical(x, y)
10 |   }
11 |   # Test when only 1 column to ensure no "drop" bugs
12 |   x <- tibble::tibble(sample = letters[1:3], var = 1:3) %>% sample_data
13 |   expect_identical(x, print(x))
14 | })
15 | 


--------------------------------------------------------------------------------
/tests/testthat/test-psmelt.R:
--------------------------------------------------------------------------------
  1 | # devtools::test_file(here::here("tests", "testthat", "test-psmelt.R"))
  2 | 
  3 | # Test on a subset of GlobalPatterns
  4 | data(GlobalPatterns)
  5 | set.seed(20190421)
  6 | ps <- GlobalPatterns %>%
  7 |   {prune_taxa(sample(taxa_names(.), 40), .)} %>%
  8 |   tax_glom("Genus")
  9 | 
 10 | test_that("psmelt() functionally matches phyloseq::psmelt()", {
 11 |   # In this example, phyloseq::psmelt() drops the "Species" column from the
 12 |   # tax_table (which is full of NAs). But speedyseq::psmelt() does not
 13 |   # discard columns even if they contain no non-missing data. Other
 14 |   # differences: phyloseq's output has rownames (which seem not meaningful);
 15 |   # speedyseq's output does not; both data frames are sorted by Abundance,
 16 |   # but the row order differs in cases where Abundance and OTU names differ.
 17 |   options(stringsAsFactors = FALSE)
 18 |   tb1 <- phyloseq::psmelt(ps)
 19 |   tb2 <- psmelt(ps)
 20 |   expect_is(tb2$Kingdom, "character")
 21 |   expect_true(dplyr::all_equal(tb1, dplyr::select(tb2, -Species),
 22 |     ignore_col_order = FALSE, ignore_row_order = TRUE))
 23 |   expect_equal(tb1$Abundance, tb2$Abundance)
 24 |   expect_identical(tb1$OTU, tb2$OTU)
 25 | })
 26 | 
 27 | test_that("psmelt() provides the requested class", {
 28 |   dt <- psmelt(ps, as = "data.table")
 29 |   df <- psmelt(ps, as = "data.frame")
 30 |   tb <- psmelt(ps, as = "tibble")
 31 |   expect_is(dt, "data.table")
 32 |   expect_is(df, "data.frame")
 33 |   expect_is(tb, "tbl_df")
 34 |   expect_identical(df, dt %>% as("data.frame"))
 35 |   expect_identical(tb, df %>% tibble::as_tibble())
 36 |   # The dt -> tibble direct conversion has the ".internal.selfref" attribute
 37 |   expect_equivalent(tb, dt %>% tibble::as_tibble())
 38 |   # By default, gets `as` from "speedyseq.psmelt_class" option
 39 |   dt <- withr::with_options(
 40 |     list(speedyseq.psmelt_class = "data.table"), 
 41 |     psmelt(ps)
 42 |   )
 43 |   expect_is(dt, "data.table")
 44 | })
 45 | 
 46 | # Test code taken from phyloseq -----------------------------------------------
 47 | # https://github.com/joey711/phyloseq/blob/master/tests/testthat/test-plot.R
 48 | 
 49 | test_that("psmelt properly protects against various name collisions", {
 50 |   data("GlobalPatterns")
 51 |   gp.ch = subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
 52 |   ps1 = NULL
 53 |   gp1 = gp.ch
 54 |   # type-1a conflict, Abundance
 55 |   sample_data(gp1)$Abundance <- paste0("Sa-", 1:nsamples(gp1))
 56 |   expect_warning(ps1 <- psmelt(gp1))
 57 |   expect_equal(colnames(ps1)[1:3], c("OTU", "Sample", "Abundance"))
 58 |   expect_equal(dim(ps1), c(546L, 18L))
 59 |   expect_true("sample_Abundance" %in% colnames(ps1))
 60 |   # A different type-1a conflict, OTU
 61 |   ps1 = NULL
 62 |   gp1 = gp.ch
 63 |   sample_data(gp1)$OTU <- paste0("Sa-", 1:nsamples(gp1))
 64 |   expect_warning(ps1 <- psmelt(gp1))
 65 |   expect_equal(colnames(ps1)[1:3], c("OTU", "Sample", "Abundance"))
 66 |   expect_equal(dim(ps1), c(546L, 18L))
 67 |   expect_true("sample_OTU" %in% colnames(ps1))
 68 |   # A different type-1a conflict, Sample
 69 |   ps1 = NULL
 70 |   gp1 = gp.ch
 71 |   sample_data(gp1)$Sample <- paste0("Sa-", 1:nsamples(gp1))
 72 |   expect_warning(ps1 <- psmelt(gp1))
 73 |   expect_equal(colnames(ps1)[1:3], c("OTU", "Sample", "Abundance"))
 74 |   expect_equal(dim(ps1), c(546L, 18L))
 75 |   expect_true("sample_Sample" %in% colnames(ps1))  
 76 |   # type-1b conflict. rank_names conflict with special variables
 77 |   ps1 = NULL
 78 |   gp1 = gp.ch
 79 |   tax_table(gp1) <- cbind(tax_table(gp1), Sample=paste0("ta", taxa_names(gp1)))
 80 |   expect_warning(ps1 <- psmelt(gp1))
 81 |   expect_equal(colnames(ps1)[1:3], c("OTU", "Sample", "Abundance"))
 82 |   expect_equal(dim(ps1), c(546L, 18L))
 83 |   expect_true("taxa_Sample" %in% colnames(ps1))  
 84 |   # type-2 conflict. Variable collision between rank_names and sample_data
 85 |   ps1 = NULL
 86 |   gp1 = gp.ch
 87 |   tax_table(gp1) <- cbind(tax_table(gp1), Primer=paste0("ta", taxa_names(gp1)))
 88 |   expect_warning(ps1 <- psmelt(gp1))
 89 |   expect_equal(colnames(ps1)[1:3], c("OTU", "Sample", "Abundance"))
 90 |   expect_equal(dim(ps1), c(546L, 18L))
 91 |   expect_true("sample_Primer" %in% colnames(ps1)) 
 92 |   # All conflict types at once.
 93 |   ps1 = NULL
 94 |   gp1 = gp.ch
 95 |   sample_data(gp1)$Abundance <- paste0("Sa-", 1:nsamples(gp1))  
 96 |   sample_data(gp1)$OTU <- paste0("Sa-", 1:nsamples(gp1))
 97 |   sample_data(gp1)$Sample <- paste0("Sa-", 1:nsamples(gp1))
 98 |   tax_table(gp1) <- cbind(tax_table(gp1), Sample=paste0("ta", taxa_names(gp1)))
 99 |   tax_table(gp1) <- cbind(tax_table(gp1), Primer=paste0("ta", taxa_names(gp1)))
100 |   expect_warning(ps1 <- psmelt(gp1))
101 |   expect_equal(colnames(ps1)[1:3], c("OTU", "Sample", "Abundance"))
102 |   expect_equal(dim(ps1), c(546L, 22L))
103 |   newvars = c("sample_OTU", "sample_Sample", "sample_Abundance",
104 |               "sample_Primer", "taxa_Sample")
105 |   expect_true(all(newvars %in% colnames(ps1)))   
106 | })
107 | 
108 | test_that("psmelt correctly handles phyloseq data with NULL components, and OTU tables", {
109 |   data("GlobalPatterns")
110 |   GP = prune_taxa(names(sort(taxa_sums(GlobalPatterns), TRUE)[1:50]),
111 |     GlobalPatterns)
112 |   # The objects with NULL components
113 |   GPS = phyloseq(otu_table(GP), sample_data(GP), phy_tree(GP))
114 |   GPT = phyloseq(otu_table(GP), tax_table(GP), phy_tree(GP))
115 |   GPTr = phyloseq(otu_table(GP), phy_tree(GP))
116 |   GPN = otu_table(GP)
117 |   # Try psmelt directly. Should be no errors or warnings.
118 |   expect_is((testT <- psmelt(GPT)), "data.frame")
119 |   expect_is((testS <- psmelt(GPS)), "data.frame")
120 |   expect_is((testTr <- psmelt(GPTr)), "data.frame")
121 |   expect_is((testN <- psmelt(GPN)), "data.frame")
122 |   # Test values of the results.
123 |   expect_is(testT$Abundance, "numeric")
124 |   expect_is(testT$OTU, "character")
125 |   expect_is(testT$Sample, "character")
126 |   expect_equivalent(
127 |     colnames(testT),
128 |     c("OTU", "Sample", "Abundance", "Kingdom", "Phylum", "Class", "Order",
129 |       "Family", "Genus", "Species")
130 |   )
131 |   expect_equivalent(
132 |     colnames(testS), 
133 |     # NOTE: phyloseq instead expects c("Sample", "OTU", ... for this case
134 |     c("OTU", "Sample",
135 |       "Abundance", "X.SampleID", "Primer", "Final_Barcode",
136 |       "Barcode_truncated_plus_T", "Barcode_full_length", "SampleType",
137 |       "Description")
138 |   )
139 |   # Try psmelt via plot function that relies on it
140 |   expect_is(pS <- plot_tree(GPS, color="SampleType"), "ggplot")
141 |   expect_is(pT <- plot_tree(GPT, shape="Kingdom"), "ggplot")
142 |   expect_is(pTr <- plot_tree(GPTr), "ggplot")
143 |   expect_is(pN <- plot_bar(GPN), "ggplot")
144 |   # Note, these calls produce graphical output
145 |   expect_is((prPS <- print(pS)), "gg")
146 |   expect_is((prPT <- print(pT)), "gg")
147 |   expect_is((prPTr <- print(pTr)), "gg")
148 |   expect_is((prPN <- print(pN)), "gg")
149 | })
150 | 
151 | test_that("psmelt doesn't break when the number of taxa is 1", {
152 |   data(GlobalPatterns)
153 |   # tree removal warning when prune to 1 OTU.
154 |   expect_warning(GP1 <- prune_taxa(taxa_names(GlobalPatterns)[1], GlobalPatterns))
155 |   expect_equal(ntaxa(GP1), 1)
156 |   df <- psmelt(GP1)
157 |   expect_is(df, 'data.frame')
158 |   reqnames = c("OTU", "Sample", "Abundance", "SampleType", "Kingdom", "Phylum")
159 |   expect_true(all(reqnames %in% names(df)))
160 |   expect_equivalent(sum(df$Abundance, na.rm = TRUE), taxa_sums(GP1))
161 | })
162 | 


--------------------------------------------------------------------------------
/tests/testthat/test-transform-filter.R:
--------------------------------------------------------------------------------
 1 | # run just this file:
 2 | # devtools::test_file(here::here("tests", "testthat", "test-transform-filter.R"))
 3 | data(GlobalPatterns)
 4 | 
 5 | test_that("transform and filter functions agree with phyloseq", {
 6 |   gp1 <- filter_taxa2(GlobalPatterns, ~ sum(. > 0) > 5)
 7 |   gp2 <- phyloseq::filter_taxa(GlobalPatterns, function(x) sum(x > 0) > 5, 
 8 |     prune = TRUE)
 9 |   expect_identical(gp1, gp2)
10 | 
11 |   gp3 <- transform_sample_counts(gp1, ~ . / sum(.))
12 |   gp4 <- phyloseq::transform_sample_counts(gp1, function(x) x / sum(x))
13 |   expect_identical(gp3, gp4)
14 | })
15 | 


--------------------------------------------------------------------------------
/tests/testthat/test-utils.R:
--------------------------------------------------------------------------------
  1 | # run just this file:
  2 | # devtools::test_file(here::here("tests", "testthat", "test-utils.R"))
  3 | 
  4 | # select_taxa -----------------------------------------------------------------
  5 | 
  6 | # Get a phyloseq object with all slots filled for testing
  7 | data(GlobalPatterns)
  8 | ps <- prune_taxa(
  9 |   ape::extract.clade(phy_tree(GlobalPatterns), "0.589.38") %>% .$tip.label,
 10 |   GlobalPatterns
 11 | )
 12 | # add reference sequences
 13 | rs <- phy_tree(ps) %>% 
 14 |   phangorn::simSeq(10) %>%
 15 |   purrr::map(factor, levels = as.character(1:4)) %>%
 16 |   purrr::map(forcats::fct_recode, A = "1", C = "2", G = "3", T = "4") %>%
 17 |   purrr::map(as.character) %>%
 18 |   purrr::map_chr(paste, collapse = "") %>%
 19 |   Biostrings::DNAStringSet()
 20 | ps <- merge_phyloseq(ps, rs)
 21 | rm(rs)
 22 | # Taxa to select, in order different from original
 23 | taxa <- taxa_names(ps)[c(5,1,9,2,6,3)]
 24 | current_order <- taxa_names(ps) %>% intersect(taxa)
 25 | 
 26 | test_that("select_taxa() gives taxa in requested order unless `x` is or contains a tree", {
 27 |   expect_equal(current_order, ps %>% select_taxa(taxa) %>% taxa_names)
 28 |   expect_equal(current_order, ps %>% phy_tree %>% select_taxa(taxa) %>% taxa_names)
 29 |   expect_equal(taxa, ps %>% refseq %>% select_taxa(taxa) %>% taxa_names)
 30 |   expect_equal(taxa, ps %>% otu_table %>% select_taxa(taxa) %>% taxa_names)
 31 |   expect_equal(taxa, ps %>% tax_table %>% select_taxa(taxa) %>% taxa_names)
 32 |   expect_equal(current_order, 
 33 |     ps %>% refseq %>% select_taxa(taxa, reorder = FALSE) %>% taxa_names)
 34 |   expect_equal(current_order, 
 35 |     ps %>% otu_table %>% select_taxa(taxa, reorder = FALSE) %>% taxa_names)
 36 |   expect_equal(current_order, 
 37 |     ps %>% tax_table %>% select_taxa(taxa, reorder = FALSE) %>% taxa_names)
 38 | 
 39 |   ps1 <- ps
 40 |   ps1@phy_tree <- NULL
 41 |   expect_equal(taxa, ps1 %>% select_taxa(taxa) %>% taxa_names)
 42 |   expect_equal(current_order, 
 43 |     ps1 %>% select_taxa(taxa, reorder = FALSE) %>% taxa_names)
 44 |   ps1@refseq <- NULL
 45 |   expect_equal(taxa, ps1 %>% select_taxa(taxa) %>% taxa_names)
 46 |   ps1@tax_table <- NULL
 47 |   expect_equal(taxa, ps1 %>% select_taxa(taxa) %>% taxa_names)
 48 | })
 49 | 
 50 | test_that("select_taxa() agrees with prune_taxa() when accounting for order", {
 51 |   expect_equal(
 52 |     ps %>% select_taxa(taxa) %>% otu_table,
 53 |     ps %>% prune_taxa(taxa, .) %>% otu_table
 54 |   )
 55 |   expect_equal(
 56 |     ps %>% sample_data %>% select_taxa(taxa),
 57 |     ps %>% sample_data %>% prune_taxa(taxa, .)
 58 |   )
 59 |   expect_equal(
 60 |     ps %>% phy_tree %>% select_taxa(taxa),
 61 |     ps %>% phy_tree %>% prune_taxa(taxa, .)
 62 |   )
 63 |   expect_equal(
 64 |     ps %>% refseq %>% select_taxa(taxa, reorder = FALSE),
 65 |     ps %>% refseq %>% prune_taxa(taxa, .)
 66 |   )
 67 |   expect_equal(
 68 |     ps %>% otu_table %>% select_taxa(taxa, reorder = TRUE),
 69 |     ps %>% otu_table %>% prune_taxa(taxa, .)
 70 |   )
 71 |   expect_equal(
 72 |     ps %>% tax_table %>% select_taxa(taxa, reorder = TRUE),
 73 |     ps %>% tax_table %>% prune_taxa(taxa, .)
 74 |   )
 75 | })
 76 | 
 77 | test_that("select_taxa() returns sample data unchanged", {
 78 |   expect_equal(ps %>% sample_data %>% select_taxa(taxa), sample_data(ps))
 79 | })
 80 | 
 81 | test_that("select_taxa() throws an error on duplicates or taxa not in x", {
 82 |   expect_error(ps %>% select_taxa(c(taxa, taxa[1])),
 83 |     "is not TRUE"
 84 |   )
 85 |   expect_error(ps %>% sample_data %>% select_taxa(c(taxa, taxa[1])),
 86 |     "is not TRUE"
 87 |   )
 88 |   expect_error(ps %>% otu_table %>% select_taxa(c(taxa, taxa[1])),
 89 |     "is not TRUE"
 90 |   )
 91 |   expect_error(ps %>% phy_tree %>% select_taxa(c(taxa, taxa[1])),
 92 |     "is not TRUE"
 93 |   )
 94 |   expect_error(ps %>% tax_table %>% select_taxa(c(taxa, taxa[1])),
 95 |     "is not TRUE"
 96 |   )
 97 |   expect_error(ps %>% refseq %>% select_taxa(c(taxa, taxa[1])),
 98 |     "is not TRUE"
 99 |   )
100 |   expect_error(ps %>% select_taxa(c(taxa, "asdf")),
101 |     "is not TRUE"
102 |   )
103 |   expect_error(ps %>% otu_table %>% select_taxa(c(taxa, "asdf")),
104 |     "is not TRUE"
105 |   )
106 |   expect_error(ps %>% phy_tree %>% select_taxa(c(taxa, "asdf")),
107 |     "is not TRUE"
108 |   )
109 |   expect_error(ps %>% tax_table %>% select_taxa(c(taxa, "asdf")),
110 |     "is not TRUE"
111 |   )
112 |   expect_error(ps %>% refseq %>% select_taxa(c(taxa, "asdf")),
113 |     "is not TRUE"
114 |   )
115 | })
116 | 
117 | # orient_taxa -----------------------------------------------------------------
118 | #
119 | test_that("orient_taxa() produces expected output on phyloseq and otu-table objects", {
120 |   otu <- otu_table(ps)
121 |   ps_t <- t(ps)
122 |   expect_identical(ps %>% orient_taxa("rows"), ps)
123 |   expect_identical(ps %>% orient_taxa("columns"), ps %>% t)
124 |   expect_identical(ps_t %>% orient_taxa("rows"), ps)
125 |   expect_identical(ps_t %>% orient_taxa("columns"), ps_t)
126 |   expect_identical(otu %>% orient_taxa("rows"), otu)
127 |   expect_identical(otu %>% orient_taxa("columns"), otu %>% t)
128 | })
129 | 


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