Taxonomic Classes

Provides taxonomic classes for groupings of taxonomic names without data, and those with data. Methods provided are "taxonomically aware", in that they know about ordering of ranks, and methods that filter based on taxonomy also filter associated data.


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taxa defines taxonomic classes and functions to manipulate them. The goal is to use these classes as low level fundamental taxonomic classes that other R packages can build on and use.

There are two distinct types of classes in taxa:

  • Classes that are concerned only with taxonomic information: taxon, taxonomy, hierarchy, etc.
  • A class called taxmap that is concerned with combining taxonomic data with user-defined data of any type (e.g. molecular sequences, abundance counts etc.)

Diagram of class concepts for taxa classes:

Install

CRAN version

install.packages("taxa")

Development version from GitHub

devtools::install_github("ropensci/taxa")

library("taxa")

The classes

There are a few optional classes used to store information in other classes. In most cases, these can be replaced with simple character values but using them provides more information and potential functionality.

database

Taxonomic data usually comes from a database. A common example is the NCBI Taxonomy Database used to provide taxonomic classifications to sequences deposited in other NCBI databases. The database class stores the name of the database and associated information:

(ncbi <- taxon_database(
  name = "ncbi",
  url = "http://www.ncbi.nlm.nih.gov/taxonomy",
  description = "NCBI Taxonomy Database",
  id_regex = "*"
))
#> <database> ncbi
#>   url: http://www.ncbi.nlm.nih.gov/taxonomy
#>   description: NCBI Taxonomy Database
#>   id regex: *
ncbi$name
#> [1] "ncbi"
ncbi$url
#> [1] "http://www.ncbi.nlm.nih.gov/taxonomy"

To save on memory, a selection of common databases is provided with the package (database_list) and any in this list can be used by name instead of making a new database object (e.g. "ncbi" instead of the ncbi above).

database_list
#> $ncbi
#> <database> ncbi
#>   url: http://www.ncbi.nlm.nih.gov/taxonomy
#>   description: NCBI Taxonomy Database
#>   id regex: .*
#> 
#> $gbif
#> <database> gbif
#>   url: http://www.gbif.org/developer/species
#>   description: GBIF Taxonomic Backbone
#>   id regex: .*
#> 
#> $bold
#> <database> bold
#>   url: http://www.boldsystems.org
#>   description: Barcode of Life
#>   id regex: .*
#> 
#> $col
#> <database> col
#>   url: http://www.catalogueoflife.org
#>   description: Catalogue of Life
#>   id regex: .*
#> 
#> $eol
#> <database> eol
#>   url: http://eol.org
#>   description: Encyclopedia of Life
#>   id regex: .*
#> 
#> $nbn
#> <database> nbn
#>   url: https://nbn.org.uk
#>   description: UK National Biodiversity Network
#>   id regex: .*
#> 
#> $tps
#> <database> tps
#>   url: http://www.tropicos.org/
#>   description: Tropicos
#>   id regex: .*
#> 
#> $itis
#> <database> itis
#>   url: http://www.itis.gov
#>   description: Integrated Taxonomic Information System
#>   id regex: .*

rank

Taxa might have defined ranks (e.g. species, family, etc.), ambiguous ranks (e.g. "unranked", "unknown"), or no rank information at all. The particular selection and format of valid ranks varies with database, so the database can be optionally defined. If no database is defined, any ranks in any order are allowed.

taxon_rank(name = "species", database = "ncbi")
#> <TaxonRank> species
#>   database: ncbi

taxon_name

The taxon name can be defined in the same way as rank.

taxon_name("Poa", database = "ncbi")
#> <TaxonName> Poa
#>   database: ncbi

taxon_id

Each database has its set of unique taxon IDs. These IDs are better than using the taxon name directly because they are guaranteed to be unique, whereas there are often duplicates of taxon names (e.g. Orestias elegans is the name of both an orchid and a fish).

taxon_id(12345, database = "ncbi")
#> <TaxonId> 12345
#>   database: ncbi

The "taxon" class

The taxon class combines the classes containing the name, rank, and ID for the taxon. There is also a place to define an authority of the taxon.

(x <- taxon(
  name = taxon_name("Poa annua"),
  rank = taxon_rank("species"),
  id = taxon_id(93036),
  authority = "Linnaeus"
))
#> <Taxon>
#>   name: Poa annua
#>   rank: species
#>   id: 93036
#>   authority: none

Instead of the name, rank, and ID classes, simple character vectors can be supplied.

(x <- taxon(
  name = "Poa annua",
  rank = "species",
  id = 93036,
  authority = "Linnaeus"
))
#> <Taxon>
#>   name: Poa annua
#>   rank: species
#>   id: 93036
#>   authority: none

The taxa class is just a list of taxon classes with some custom print methods. It is meant to store an arbitrary list of taxon.

(x <- taxon(
  name = taxon_name("Poa annua"),
  rank = taxon_rank("species"),
  id = taxon_id(93036)
))
#> <Taxon>
#>   name: Poa annua
#>   rank: species
#>   id: 93036
#>   authority: none
taxa(x, x, x)
#> <taxa> 
#>   no. taxa:  3 
#>   Poa annua / species / 93036 
#>   Poa annua / species / 93036 
#>   Poa annua / species / 93036

The "hierarchy" class

Taxonomic classifications are an ordered set of taxa, each at a different rank. The hierarchy class stores a list of taxon classes like taxa, but hierarchy is meant to store all of the taxa in a classification in the correct order.

x <- taxon(
  name = taxon_name("Poaceae"),
  rank = taxon_rank("family"),
  id = taxon_id(4479)
)

y <- taxon(
  name = taxon_name("Poa"),
  rank = taxon_rank("genus"),
  id = taxon_id(4544)
)

z <- taxon(
  name = taxon_name("Poa annua"),
  rank = taxon_rank("species"),
  id = taxon_id(93036)
)

(hier1 <- hierarchy(z, y, x))
#> <Hierarchy>
#>   no. taxon's:  3 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544 
#>   Poa annua / species / 93036

Multiple hierarchy classes are stored in the hierarchies class, similar to how multiple taxon are stored in taxa.

a <- taxon(
  name = taxon_name("Felidae"),
  rank = taxon_rank("family"),
  id = taxon_id(9681)
)
b <- taxon(
  name = taxon_name("Puma"),
  rank = taxon_rank("genus"),
  id = taxon_id(146712)
)
c <- taxon(
  name = taxon_name("Puma concolor"),
  rank = taxon_rank("species"),
  id = taxon_id(9696)
)
(hier2 <- hierarchy(c, b, a))
#> <Hierarchy>
#>   no. taxon's:  3 
#>   Felidae / family / 9681 
#>   Puma / genus / 146712 
#>   Puma concolor / species / 9696

hierarchies(hier1, hier2)
#> <Hierarchies> 
#>   no. hierarchies:  2 
#>   Poaceae / Poa / Poa annua 
#>   Felidae / Puma / Puma concolor

The "taxonomy" class

The taxonomy class stores unique taxon objects in a tree structure. Usually this kind of complex information would be the output of a file parsing function, but the code below shows how to construct a taxonomy object from scratch.

# define taxa
notoryctidae <- taxon(name = "Notoryctidae", rank = "family", id = 4479)
notoryctes <- taxon(name = "Notoryctes", rank = "genus", id = 4544)
typhlops <- taxon(name = "typhlops", rank = "species", id = 93036)
mammalia <- taxon(name = "Mammalia", rank = "class", id = 9681)
felidae <- taxon(name = "Felidae", rank = "family", id = 9681)
felis <- taxon(name = "Felis", rank = "genus", id = 9682)
catus <- taxon(name = "catus", rank = "species", id = 9685)
panthera <- taxon(name = "Panthera", rank = "genus", id = 146712)
tigris <- taxon(name = "tigris", rank = "species", id = 9696)
plantae <- taxon(name = "Plantae", rank = "kingdom", id = 33090)
solanaceae <- taxon(name = "Solanaceae", rank = "family", id = 4070)
solanum <- taxon(name = "Solanum", rank = "genus", id = 4107)
lycopersicum <- taxon(name = "lycopersicum", rank = "species", id = 49274)
tuberosum <- taxon(name = "tuberosum", rank = "species", id = 4113)
homo <- taxon(name = "homo", rank = "genus", id = 9605)
sapiens <- taxon(name = "sapiens", rank = "species", id = 9606)
hominidae <- taxon(name = "Hominidae", rank = "family", id = 9604)

# define hierarchies
tiger <- hierarchy(mammalia, felidae, panthera, tigris)
cat <- hierarchy(mammalia, felidae, felis, catus)
human <- hierarchy(mammalia, hominidae, homo, sapiens)
mole <- hierarchy(mammalia, notoryctidae, notoryctes, typhlops)
tomato <- hierarchy(plantae, solanaceae, solanum, lycopersicum)
potato <- hierarchy(plantae, solanaceae, solanum, tuberosum)

# make taxonomy
(tax <- taxonomy(tiger, cat, human, tomato, potato))
#> <Taxonomy>
#>   14 taxa: b. Mammalia ... n. lycopersicum, o. tuberosum
#>   14 edges: NA->b, NA->c, b->d ... h->l, i->m, j->n, j->o

Unlike the hierarchies class, each unique taxon object is only represented once in the taxonomy object. Each taxon has a corresponding entry in an edge list that encode how it is related to other taxa. This makes taxonomy more compact, but harder to manipulate using standard indexing. To make manipulation easier, there are methods for taxomomy that can provide indexes in a taxonomic context.

supertaxa

A "supertaxon" is a taxon of a coarser rank that encompasses the taxon of interest (e.g. "Homo" is a supertaxon of "sapiens"). The supertaxa function returns the supertaxa of all or a subset of the taxa in a taxonomy object.

supertaxa(tax)
#> $b
#> integer(0)
#> 
#> $c
#> integer(0)
#> 
#> $d
#> [1] 1
#> 
#> $e
#> [1] 1
#> 
#> $f
#> [1] 2
#> 
#> $g
#> [1] 3 1
#> 
#> $h
#> [1] 3 1
#> 
#> $i
#> [1] 4 1
#> 
#> $j
#> [1] 5 2
#> 
#> $k
#> [1] 6 3 1
#> 
#> $l
#> [1] 7 3 1
#> 
#> $m
#> [1] 8 4 1
#> 
#> $n
#> [1] 9 5 2
#> 
#> $o
#> [1] 9 5 2

By default, the taxon IDs for the supertaxa of all taxa are returned in the same order they appear in the edge list. Taxon IDs (character) or edge list indexes (integer) can be supplied to the subset option to only return information for some taxa.

supertaxa(tax, subset = "m")
#> $m
#> [1] 8 4 1

What is returned can be modified with the value option:

supertaxa(tax, subset = "m", value = "taxon_names")
#> $m
#>           i           e           b 
#>      "homo" "Hominidae"  "Mammalia"

supertaxa(tax, subset = "m", value = "taxon_ranks")
#> $m
#>        i        e        b 
#>  "genus" "family"  "class"

You can also subset based on a logical test:

supertaxa(tax, subset = taxon_ranks == "genus", value = "taxon_names")
#> $g
#>          d          b 
#>  "Felidae" "Mammalia" 
#> 
#> $h
#>          d          b 
#>  "Felidae" "Mammalia" 
#> 
#> $i
#>           e           b 
#> "Hominidae"  "Mammalia" 
#> 
#> $j
#>            f            c 
#> "Solanaceae"    "Plantae"

The subset and value work the same for most of the following functions as well. See tax$all_names() for what can be used with value.

subtaxa

The "subtaxa" of a taxon are all those of a finer rank encompassed by that taxon. For example, sapiens is a subtaxon of Homo. The subtaxa function returns all subtaxa for each taxon in a taxonomy object.

subtaxa(tax, value = "taxon_names")
#> $b
#>           d           g           k           h           l           e 
#>   "Felidae"  "Panthera"    "tigris"     "Felis"     "catus" "Hominidae" 
#>           i           m 
#>      "homo"   "sapiens" 
#> 
#> $c
#>              f              j              n              o 
#>   "Solanaceae"      "Solanum" "lycopersicum"    "tuberosum" 
#> 
#> $d
#>          g          k          h          l 
#> "Panthera"   "tigris"    "Felis"    "catus" 
#> 
#> $e
#>         i         m 
#>    "homo" "sapiens" 
#> 
#> $f
#>              j              n              o 
#>      "Solanum" "lycopersicum"    "tuberosum" 
#> 
#> $g
#>        k 
#> "tigris" 
#> 
#> $h
#>       l 
#> "catus" 
#> 
#> $i
#>         m 
#> "sapiens" 
#> 
#> $j
#>              n              o 
#> "lycopersicum"    "tuberosum" 
#> 
#> $k
#> named character(0)
#> 
#> $l
#> named character(0)
#> 
#> $m
#> named character(0)
#> 
#> $n
#> named character(0)
#> 
#> $o
#> named character(0)

roots

We call taxa that have no supertaxa "roots". The roots function returns these taxa.

roots(tax, value = "taxon_names")
#>          b          c 
#> "Mammalia"  "Plantae"

leaves

We call taxa without any subtaxa "leaves". The leaves function returns these taxa.

leaves(tax, value = "taxon_names")
#>              k              l              m              n              o 
#>       "tigris"        "catus"      "sapiens" "lycopersicum"    "tuberosum"

other functions

There are many other functions to interact with taxonomy object, such as stems and n_subtaxa, but these will not be described here for now.

The "taxmap" class

The taxmap class is used to store any number of tables, lists, or vectors associated with taxa. It is basically the same as the taxonomy class, but with the following additions:

  • A list called data that stores arbitrary user data associated with taxa

  • A list called funcs that stores user defined functions

    info <- data.frame(name = c("tiger", "cat", "mole", "human", "tomato", "potato"), n_legs = c(4, 4, 4, 2, 0, 0), dangerous = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE))

    phylopic_ids <- c("e148eabb-f138-43c6-b1e4-5cda2180485a", "12899ba0-9923-4feb-a7f9-758c3c7d5e13", "11b783d5-af1c-4f4e-8ab5-a51470652b47", "9fae30cd-fb59-4a81-a39c-e1826a35f612", "b6400f39-345a-4711-ab4f-92fd4e22cb1a", "63604565-0406-460b-8cb8-1abe954b3f3a")

    foods <- list(c("mammals", "birds"), c("cat food", "mice"), c("insects"), c("Most things, but especially anything rare or expensive"), c("light", "dirt"), c("light", "dirt"))

    reaction <- function(x) { ifelse(x$data$info$dangerous, paste0("Watch out! That ", x$data$info$name, " might attack!"), paste0("No worries; its just a ", x$data$info$name, ".")) }

    my_taxmap <- taxmap(tiger, cat, mole, human, tomato, potato, data = list(info = info, phylopic_ids = phylopic_ids, foods = foods), funcs = list(reaction = reaction))

In most functions that work with taxmap objects, the names of list/vector datasets, table columns, or functions can be used as if they were separate variables on their own. In the case of functions, instead of returning the function itself, the results of the functions are returned. To see what variables can be used this way, use all_names.

all_names(my_taxmap)
#>         taxon_names           taxon_ids       taxon_indexes 
#>       "taxon_names"         "taxon_ids"     "taxon_indexes" 
#>         n_supertaxa           n_subtaxa         n_subtaxa_1 
#>       "n_supertaxa"         "n_subtaxa"       "n_subtaxa_1" 
#>         taxon_ranks             is_root             is_stem 
#>       "taxon_ranks"           "is_root"           "is_stem" 
#>           is_branch             is_leaf      data$info$name 
#>         "is_branch"           "is_leaf"              "name" 
#>    data$info$n_legs data$info$dangerous   data$phylopic_ids 
#>            "n_legs"         "dangerous"      "phylopic_ids" 
#>          data$foods      funcs$reaction 
#>             "foods"          "reaction"

For example using my_taxmap$data$info$n_legs or n_legs will have the same effect inside manipulation functions like filter_taxa described below. To get the values of these variables, use get_data.

get_data(my_taxmap)
#> $taxon_names
#>              b              c              d              e              f 
#>     "Mammalia"      "Plantae"      "Felidae" "Notoryctidae"    "Hominidae" 
#>              g              h              i              j              k 
#>   "Solanaceae"     "Panthera"        "Felis"   "Notoryctes"         "homo" 
#>              l              m              n              o              p 
#>      "Solanum"       "tigris"        "catus"     "typhlops"      "sapiens" 
#>              q              r 
#> "lycopersicum"    "tuberosum" 
#> 
#> $taxon_ids
#>   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r 
#> "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" 
#> 
#> $taxon_indexes
#>  b  c  d  e  f  g  h  i  j  k  l  m  n  o  p  q  r 
#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 
#> 
#> $n_supertaxa
#> b c d e f g h i j k l m n o p q r 
#> 0 0 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 
#> 
#> $n_subtaxa
#>  b  c  d  e  f  g  h  i  j  k  l  m  n  o  p  q  r 
#> 11  4  4  2  2  3  1  1  1  1  2  0  0  0  0  0  0 
#> 
#> $n_subtaxa_1
#> b c d e f g h i j k l m n o p q r 
#> 3 1 2 1 1 1 1 1 1 1 2 0 0 0 0 0 0 
#> 
#> $taxon_ranks
#>         b         c         d         e         f         g         h 
#>   "class" "kingdom"  "family"  "family"  "family"  "family"   "genus" 
#>         i         j         k         l         m         n         o 
#>   "genus"   "genus"   "genus"   "genus" "species" "species" "species" 
#>         p         q         r 
#> "species" "species" "species" 
#> 
#> $is_root
#>     b     c     d     e     f     g     h     i     j     k     l     m 
#>  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
#>     n     o     p     q     r 
#> FALSE FALSE FALSE FALSE FALSE 
#> 
#> $is_stem
#>     b     c     d     e     f     g     h     i     j     k     l     m 
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
#>     n     o     p     q     r 
#> FALSE FALSE FALSE FALSE FALSE 
#> 
#> $is_branch
#>     b     c     d     e     f     g     h     i     j     k     l     m 
#> FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE 
#>     n     o     p     q     r 
#> FALSE FALSE FALSE FALSE FALSE 
#> 
#> $is_leaf
#>     b     c     d     e     f     g     h     i     j     k     l     m 
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE 
#>     n     o     p     q     r 
#>  TRUE  TRUE  TRUE  TRUE  TRUE 
#> 
#> $name
#>      m      n      o      p      q      r 
#>  tiger    cat   mole  human tomato potato 
#> Levels: cat human mole potato tiger tomato
#> 
#> $n_legs
#> m n o p q r 
#> 4 4 4 2 0 0 
#> 
#> $dangerous
#>     m     n     o     p     q     r 
#>  TRUE FALSE FALSE  TRUE FALSE FALSE 
#> 
#> $phylopic_ids
#>                                      m 
#> "e148eabb-f138-43c6-b1e4-5cda2180485a" 
#>                                      n 
#> "12899ba0-9923-4feb-a7f9-758c3c7d5e13" 
#>                                      o 
#> "11b783d5-af1c-4f4e-8ab5-a51470652b47" 
#>                                      p 
#> "9fae30cd-fb59-4a81-a39c-e1826a35f612" 
#>                                      q 
#> "b6400f39-345a-4711-ab4f-92fd4e22cb1a" 
#>                                      r 
#> "63604565-0406-460b-8cb8-1abe954b3f3a" 
#> 
#> $foods
#> $foods$m
#> [1] "mammals" "birds"  
#> 
#> $foods$n
#> [1] "cat food" "mice"    
#> 
#> $foods$o
#> [1] "insects"
#> 
#> $foods$p
#> [1] "Most things, but especially anything rare or expensive"
#> 
#> $foods$q
#> [1] "light" "dirt" 
#> 
#> $foods$r
#> [1] "light" "dirt" 
#> 
#> 
#> $reaction
#> [1] "Watch out! That tiger might attack!"
#> [2] "No worries; its just a cat."        
#> [3] "No worries; its just a mole."       
#> [4] "Watch out! That human might attack!"
#> [5] "No worries; its just a tomato."     
#> [6] "No worries; its just a potato."

Note how "taxon_names" and "dangerous" are used below.

Filtering

In addition to all of the functions like subtaxa that work with taxonomy, taxmap has a set of functions to manipulate data in a taxonomic context using functions based on dplyr. Like many operations on taxmap objects, there are a pair of functions that modify the taxa as well as the associated data, which we call "observations". The filter_taxa and filter_obs functions are an example of such a pair that can filter taxa and observations respectively. For example, we can use filter_taxa to subset all taxa with a name starting with "t":

filter_taxa(my_taxmap, startsWith(taxon_names, "t"))
#> <Taxmap>
#>   3 taxa: m. tigris, o. typhlops, r. tuberosum
#>   3 edges: NA->m, NA->o, NA->r
#>   3 data sets:
#>     info:
#>       # A tibble: 3 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1  tiger      4      TRUE        m
#>       2   mole      4     FALSE        o
#>       3 potato      0     FALSE        r
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 3 items
#>   1 functions:
#>  reaction

There can be any number of filters that resolve to TRUE/FALSE vectors, taxon ids, or edge list indexes.

filter_taxa(my_taxmap, startsWith(taxon_names, "t"), "r")

There are many options for filter_taxa that make it very flexible. For example, the supertaxa option can make all the supertaxa of selected taxa be preserved.

filter_taxa(my_taxmap, startsWith(taxon_names, "t"), supertaxa = TRUE)
#> <Taxmap>
#>   11 taxa: m. tigris ... g. Solanaceae, c. Plantae
#>   11 edges: h->m, j->o, l->r, d->h ... b->e, g->l, c->g, NA->c
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1  tiger      4      TRUE        m
#>       2    cat      4     FALSE        d
#>       3   mole      4     FALSE        o
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

The filter_obs function works in a similar way, but subsets observations in my_taxmap$data.

filter_obs(my_taxmap, "info", dangerous == TRUE)
#> <Taxmap>
#>   17 taxa: b. Mammalia ... q. lycopersicum, r. tuberosum
#>   17 edges: NA->b, NA->c, b->d ... j->o, k->p, l->q, l->r
#>   3 data sets:
#>     info:
#>       # A tibble: 2 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1  tiger      4      TRUE        m
#>       2  human      2      TRUE        p
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

You can choose to filter out taxa whose observations did not pass the filter as well:

filter_obs(my_taxmap, "info", dangerous == TRUE, drop_taxa = TRUE)
#> <Taxmap>
#>   7 taxa: b. Mammalia, d. Felidae ... m. tigris, p. sapiens
#>   7 edges: NA->b, b->d, b->f, d->h, f->k, h->m, k->p
#>   3 data sets:
#>     info:
#>       # A tibble: 2 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1  tiger      4      TRUE        m
#>       2  human      2      TRUE        p
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

Sampling

The functions sample_n_obs and sample_n_taxa are similar to filter_obs and filter_taxa, except taxa/observations are chosen randomly. All of the options of the "filter_" functions are available to the "sample_" functions

set.seed(1)
sample_n_taxa(my_taxmap, 3)
#> <Taxmap>
#>   3 taxa: g. Solanaceae, i. Felis, m. tigris
#>   3 edges: NA->g, NA->i, NA->m
#>   3 data sets:
#>     info:
#>       # A tibble: 4 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1  tiger      4      TRUE        m
#>       2    cat      4     FALSE        i
#>       3 tomato      0     FALSE        g
#>       # ... with 1 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 4 items
#>   1 functions:
#>  reaction
set.seed(1)
sample_n_taxa(my_taxmap, 3, supertaxa = TRUE)
#> <Taxmap>
#>   7 taxa: g. Solanaceae, i. Felis ... b. Mammalia, h. Panthera
#>   7 edges: c->g, d->i, h->m, NA->c, b->d, NA->b, d->h
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1  tiger      4      TRUE        m
#>       2    cat      4     FALSE        i
#>       3   mole      4     FALSE        b
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

Adding columns

Adding columns to tabular datasets is done using mutate_obs.

mutate_obs(my_taxmap, "info",
           new_col = "Im new",
           newer_col = paste0(new_col, "er!"))
#> <Taxmap>
#>   17 taxa: b. Mammalia ... q. lycopersicum, r. tuberosum
#>   17 edges: NA->b, NA->c, b->d ... j->o, k->p, l->q, l->r
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 6
#>           name n_legs dangerous taxon_id new_col newer_col
#>         <fctr>  <dbl>     <lgl>    <chr>   <chr>     <chr>
#>       1  tiger      4      TRUE        m  Im new Im newer!
#>       2    cat      4     FALSE        n  Im new Im newer!
#>       3   mole      4     FALSE        o  Im new Im newer!
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

Subsetting columns

Subsetting columns in tabular datasets is done using select_obs.

# Selecting a column by name
select_obs(my_taxmap, "info", dangerous)
#> <Taxmap>
#>   17 taxa: b. Mammalia ... q. lycopersicum, r. tuberosum
#>   17 edges: NA->b, NA->c, b->d ... j->o, k->p, l->q, l->r
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 2
#>         taxon_id dangerous
#>            <chr>     <lgl>
#>       1        m      TRUE
#>       2        n     FALSE
#>       3        o     FALSE
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

# Selecting a column by index
select_obs(my_taxmap, "info", 3)
#> <Taxmap>
#>   17 taxa: b. Mammalia ... q. lycopersicum, r. tuberosum
#>   17 edges: NA->b, NA->c, b->d ... j->o, k->p, l->q, l->r
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 2
#>         taxon_id dangerous
#>            <chr>     <lgl>
#>       1        m      TRUE
#>       2        n     FALSE
#>       3        o     FALSE
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

# Selecting a column by regular expressions
select_obs(my_taxmap, "info", matches("^dange"))
#> <Taxmap>
#>   17 taxa: b. Mammalia ... q. lycopersicum, r. tuberosum
#>   17 edges: NA->b, NA->c, b->d ... j->o, k->p, l->q, l->r
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 2
#>         taxon_id dangerous
#>            <chr>     <lgl>
#>       1        m      TRUE
#>       2        n     FALSE
#>       3        o     FALSE
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

Sorting

Sorting the edge list and observations is done using arrage_taxa and arrange_obs.

arrange_taxa(my_taxmap, taxon_names)
#> <Taxmap>
#>   17 taxa: n. catus, d. Felidae ... r. tuberosum, o. typhlops
#>   17 edges: i->n, b->d, d->i, b->f ... g->l, h->m, l->r, j->o
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1  tiger      4      TRUE        m
#>       2    cat      4     FALSE        n
#>       3   mole      4     FALSE        o
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction
arrange_obs(my_taxmap, "info", name)
#> <Taxmap>
#>   17 taxa: b. Mammalia ... q. lycopersicum, r. tuberosum
#>   17 edges: NA->b, NA->c, b->d ... j->o, k->p, l->q, l->r
#>   3 data sets:
#>     info:
#>       # A tibble: 6 x 4
#>           name n_legs dangerous taxon_id
#>         <fctr>  <dbl>     <lgl>    <chr>
#>       1    cat      4     FALSE        n
#>       2  human      2      TRUE        p
#>       3   mole      4     FALSE        o
#>       # ... with 3 more rows
#>     phylopic_ids:  e148eabb-f138-43c6-b1e4-5cda2180485a ... 63604565-0406-460b-8cb8-1abe954b3f3a
#>     foods: a list with 6 items
#>   1 functions:
#>  reaction

Parsing data

The taxmap class has the ability to contain and manipulate very complex data. However, this can make it difficult to parse the data into a taxmap object. For this reason there are three functions to help creating taxmap objects from nearly any kind of data that a taxonomy can be associated with and derived from. The figure below shows simplified versions of how to create taxmap objects from different types of data in different formats.

The parse_tax_data and lookup_tax_data have, in addition to the functionality above, the ability to include additional data sets that are somehow associated with the source datasets (e.g. share a common identifier). Elements in these datasets will be assigned the taxa defined in the source data, so functions like filter_taxa and filter_obs will work on all of the dataset at once.

Parsing Hierarchy and hierarchies objects

A set of functions are available for parsing objects of class Hierarchy and hierarchies. These functions are being ported from the CRAN package binomen.

The functions below are "taxonomically aware" so that you can use for example > and < operators to filter your taxonomic names data.

pick

pick() - Pick out specific taxa, while others are dropped

ex_hierarchy1
#> <Hierarchy>
#>   no. taxon's:  3 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544 
#>   Poa annua / species / 93036
# specific ranks by rank name
pick(ex_hierarchy1, ranks("family"))
#> <Hierarchy>
#>   no. taxon's:  1 
#>   Poaceae / family / 4479
# two elements by taxonomic name
pick(ex_hierarchy1, nms("Poaceae", "Poa"))
#> <Hierarchy>
#>   no. taxon's:  2 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544
# two elements by taxonomic identifier
pick(ex_hierarchy1, ids(4479, 4544))
#> <Hierarchy>
#>   no. taxon's:  2 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544
# combine types
pick(ex_hierarchy1, ranks("family"), ids(4544))
#> <Hierarchy>
#>   no. taxon's:  2 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544

pop

pop() - Pop out taxa, that is, drop them

ex_hierarchy1
#> <Hierarchy>
#>   no. taxon's:  3 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544 
#>   Poa annua / species / 93036
# specific ranks by rank name
pop(ex_hierarchy1, ranks("family"))
#> <Hierarchy>
#>   no. taxon's:  2 
#>   Poa / genus / 4544 
#>   Poa annua / species / 93036
# two elements by taxonomic name
pop(ex_hierarchy1, nms("Poaceae", "Poa"))
#> <Hierarchy>
#>   no. taxon's:  1 
#>   Poa annua / species / 93036
# two elements by taxonomic identifier
pop(ex_hierarchy1, ids(4479, 4544))
#> <Hierarchy>
#>   no. taxon's:  1 
#>   Poa annua / species / 93036
# combine types
pop(ex_hierarchy1, ranks("family"), ids(4544))
#> <Hierarchy>
#>   no. taxon's:  1 
#>   Poa annua / species / 93036

span

span() - Select a range of taxa, either by two names, or relational operators

ex_hierarchy1
#> <Hierarchy>
#>   no. taxon's:  3 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544 
#>   Poa annua / species / 93036
# keep all taxa between family and genus
# - by rank name, taxonomic name or ID
span(ex_hierarchy1, nms("Poaceae", "Poa"))
#> <Hierarchy>
#>   no. taxon's:  2 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544

# keep all taxa greater than genus
span(ex_hierarchy1, ranks("> genus"))
#> <Hierarchy>
#>   no. taxon's:  1 
#>   Poaceae / family / 4479

# keep all taxa greater than or equal to genus
span(ex_hierarchy1, ranks(">= genus"))
#> <Hierarchy>
#>   no. taxon's:  2 
#>   Poaceae / family / 4479 
#>   Poa / genus / 4544

# keep all taxa less than Felidae
span(ex_hierarchy2, nms("< Felidae"))
#> <Hierarchy>
#>   no. taxon's:  2 
#>   Puma / genus / 146712 
#>   Puma concolor / species / 9696

## Multiple operator statements - useful with larger classifications
ex_hierarchy3
#> <Hierarchy>
#>   no. taxon's:  6 
#>   Chordata / phylum / 158852 
#>   Vertebrata / subphylum / 331030 
#>   Teleostei / class / 161105 
#>   Salmonidae / family / 161931 
#>   Salmo / genus / 161994 
#>   Salmo salar / species / 161996
span(ex_hierarchy3, ranks("> genus"), ranks("< phylum"))
#> <Hierarchy>
#>   no. taxon's:  3 
#>   Vertebrata / subphylum / 331030 
#>   Teleostei / class / 161105 
#>   Salmonidae / family / 161931

For more information

This vignettte is meant to be just an outline of what taxa can do. In the future, we plan to release additional, in-depth vignettes for specific topics. More informaiton for specific functions and examples can be found on their man pages by typing the name of the function prefixed by a ? in an R session. For example, ?filter_taxa will pull up the help page for filter_taxa.

Use cases

  • use in binomen:
    • if this pkg does classes, binomen can focus on verbs, e.g., manipulating taxonomic classes, doing split-apply-combine type things
  • use in taxize:
    • as we don't want to break things, probably ideal to have coercion fxns, e.g., as.taxon(), which will convert e.g., the output of get_uid() to a taxa taxonomic class, which we can then go downstream and do things with (i.e., whatever we build on top of the classes)
    • Or we could even have output of get_*() functions do coercion to taxa classes on output since they are just simple S3 classes without print methods right now
  • use in metacoder: This will eventually replace the similar classes used in metacoder.

Contributors

Comments and contributions

We welcome comments, criticisms, and especially contributions! GitHub issues are the preferred way to report bugs, ask questions, or request new features. You can submit issues here:

https://github.com/ropensci/taxa/issues

Meta

  • Please report any issues or bugs.
  • License: MIT
  • Get citation information for taxa in R doing citation(package = 'taxa')
  • Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

News

taxa 0.2.0

Bug fixes

  • Fixed a few problems with using duplicated inputs to subset (issue #88, issue #89)
  • Fixed a bug that caused an error when using unnamed vectors (issue #86)
  • Fixed a bug that prevents using sequence accession numbers (issue #85)
  • Fixed bug in lookup_tax_data and extract_tax_data that caused an error when one of the queries failed too download.
  • Fixed bug that caused "data" argument of obs_apply to not work when passed as a varaible (issue #97)

Improvements

  • Added map_data_ for mapping without using NSE.
  • Make default dataset for n_obs and n_obs_1 and make them available for NSE (issue #91
  • parse_tax_data/extract_tax_data can now parse things like phylum;Nitrosopumilales;order;Nitrosopumilaceae;family; and split out the rank and taxon names by using multiple matches to the class_regex when class_sep is NULL.
  • extract_tax_data now gives warnings if a regex does not match.
  • Added n_supertaxa_1 function to get number of immediate supertaxa (always 1 or 0).
  • Added branches function to go with roots, leaves, and stems. (issue #56)
  • Added internodes and is_internode functions to go with roots, leaves, branches, and stems. USeful for removing uninformative taxonomic ranks/taxa.
  • Started to incorporate ability for taxon, taxon_name, taxon_id, taxon_rank, and taxa to handle NULL inputs as first class citizens to handle cases when you have essentially a blank taxon (use case comes from taxize package) #95 #107
  • data parsers: Put long, often unused columns last (issue #93)
  • When parsing classifications that have per-taxon info add input id column (issue #92)
  • New function classification as an abstraction to get either hierarchy of taxon indexes, names, or ids (issue #57)
  • New function get_data_frame for both Taxonomy and Taxmap objects that wraps around get_data to coerce into a data.frame. (issue #58) (PR #105)

Changes

  • In the output of the taxmap parsing functions like parse_tax_data, I moved "taxon_id" and "input_index" columns to front and "input" to rear. Also "tax_data" now comes before "class_data".

taxa 0.1.0

NEW FEATURES

  • Released to CRAN.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("taxa")

0.2.1 by Zachary Foster, 2 months ago


https://github.com/ropensci/taxa


Report a bug at https://github.com/ropensci/taxa/issues


Browse source code at https://github.com/cran/taxa


Authors: Scott Chamberlain [aut] (<https://orcid.org/0000-0003-1444-9135>), Zachary Foster [aut, cre] (<https://orcid.org/0000-0002-5075-0948>)


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports R6, jsonlite, dplyr, lazyeval, magrittr, tibble, knitr, rlang, stringr, crayon, tidyr, utils, taxize

Suggests roxygen2, testthat, rmarkdown


Depended on by metacoder.


See at CRAN